Efficient Data Handling

Data Engineering plays a crucial role in ensuring efficient data handling within an organization. By implementing proper data structures, storage mechanisms, and organization strategies, data can be retrieved and manipulated with ease and speed. Here’s how it works:

• Organization: Sorting and categorizing data into meaningful groupings make it more navigable and searchable.
• Storage: Using optimal storage solutions that fit the specific data type ensures that it can be accessed quickly when needed.
• Integration: Combining data from various sources allows for a comprehensive view, which aids in more robust analysis and reporting.

Data Quality and Accuracy

Ensuring data quality and accuracy is paramount for making informed decisions:

• Cleaning: This involves identifying and correcting errors or inconsistencies in data to improve its quality. It can include removing duplicates, filling missing values, and correcting mislabeled data.
• Validation: Implementing rules to check the correctness and relevance of data ensures that only valid data is included in the analysis.
• Preprocessing: This may include normalization, transformation, and other methods that prepare the data for analysis, which ensures that the data is in the best possible form for deriving meaningful insights.

Scalability

Scalability in data engineering refers to the ability of a system to handle growth in data volume and complexity:

• Horizontal Scaling: Adding more machines to the existing pool allows handling more data without significantly changing the existing system architecture.
• Vertical Scaling: This involves adding more power (CPU, RAM) to an existing machine to handle more data.
• Flexible Architecture: Designing with scalability in mind ensures that the data handling capability can grow as the organization grows without a complete system overhaul.

Facilitating Data Analysis

Data Engineering sets the stage for insightful data analysis by:

• Data Transformation: This includes converting data into a suitable format or structure for analysis. It may involve aggregating data, calculating summaries, and applying mathematical transformations.
• Data Integration: Combining data from different sources provides a more holistic view, allowing analysts to make connections that might not be visible when looking at individual data sets.
• Providing Tools: By implementing and maintaining tools that simplify data access and manipulation, data engineers enable data scientists and analysts to focus more on analysis rather than data wrangling.
• Ensuring Timely Availability: Efficient pipelines ensure that fresh data is available for analysis as needed, enabling real-time or near-real-time insights.

Data Engineering forms the backbone and structure of most modern data-driven decision-making processes.

By focusing on efficient handling, quality, scalability, and facilitation of analysis, data engineers contribute to turning raw data into actionable intelligence that can guide an organization’s strategy and operations.

Famous Data Engineering Tools

Apache Hadoop

About: Apache Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers.

Use: It uses simple programming models and is designed to scale from single servers to thousands of machines.

Apache Spark

About: Apache Spark is an open-source distributed computing system for fast computation.

Use: It provides an interface for entire programming clusters and is particularly known for its in-memory processing speed.

Kafka

About: Apache Kafka is an open-source stream-processing software platform.

Use: It’s used to build real-time data pipelines and streaming apps, often used for its fault tolerance and scalability.

Apache Flink

About: Apache Flink is an open-source stream-processing framework.

Use: It’s used for real-time computation that can perform analytics and complex event processing (CEP).

Snowflake

About: Snowflake is a cloud data platform that provides data warehouse features.

Use: It is known for its elasticity, enabling seamless computational power and storage scaling.

Airflow

About: Apache Airflow is an open-source tool to author, schedule, and monitor workflows programmatically.

Use: It manages complex ETL (Extract, Transform, Load) pipelines and orchestrates jobs in a distributed environment.

Tableau

About: Tableau is a data visualization tool that converts raw data into understandable formats.

Use: It allows users to connect, visualize, and share data in a way that makes sense for their organization.

Talend

About: Talend is a tool for data integration and data management.

Use: It allows users to connect, access, and manage data from various sources, providing a unified view.

Amazon Redshift

About: Amazon Redshift is a fully managed, petabyte-scale data warehouse service by Amazon.

Use: It allows fast query performance using columnar storage technology and parallelizing queries across multiple nodes.

Microsoft Azure HDInsight

About: Azure HDInsight is a cloud service from Microsoft that makes it easy to process massive amounts of big data.

Use: It analyzes data using popular open-source frameworks such as Hadoop, Spark, Hive, LLAP, Kafka, Storm, R, etc.

These tools collectively provide robust capabilities for handling, processing, and visualization of large-scale data and are integral parts of the data engineering landscape.

In a nutshell, heuristic algorithms are like shortcuts to finding solutions.

In contrast, machine learning algorithms are a systematic way for computers to learn from data and create optimized, all-encompassing solutions. 

While the above is just a simple introduction to these two, throughout the rest of this article, we will give you our formula for deciding which of the two you should use whenever a problem arises.

Trust us, by the end of this article; you’ll be the go-to expert among your friends. 

An Easy Example To Understand How A Heurstic Is Different Than An Algorithm

Let’s break down the differences between a heuristic and an algorithm with a simple, everyday example.

A heuristic approach would be to think about the typical spots where you usually put your keys on the kitchen counter, by the front door, or in your coat pocket.

Although there’s no guarantee that you’ll find your keys using this method, it’s a quick and practical way to start your search.

Most of the time, this technique will lead you to your missing keys in no time!

On the other hand, an algorithmic approach would be more systematic and thorough.

You’d start at one corner of your house and search every inch, moving from room to room until you find your keys.

This method has a 100% success rate (assuming your keys are actually in the house), but it could take a long time to complete.

So, in a nutshell, a heuristic is like an intelligent guess or shortcut that saves time, while an algorithm is a step-by-step process that guarantees a solution but might take longer.

Are Machine Learning Algorithms Heuristic Algorithms?

From the example above, we hope you’ve now got a basic understanding of heuristics and algorithms – let’s talk about machine learning. 

You might be wondering: are machine learning algorithms heuristic algorithms? 

The answer is a little more complicated than it seems – remember their unique characteristics from above.

While both methods can be used to solve problems, machine learning algorithms focus on providing the best possible results under specific conditions. This is where they differ from heuristics.

Machine learning algorithms are designed to optimize performance and guarantee certain levels of accuracy, confined to their domain restrictions.

Each popular algorithm has its own set of guarantees for optimality, which is why we use them in different scenarios.

In other words, machine learning algorithms aim to deliver the best solution based on the available data.

Heuristics, on the other hand, don’t necessarily satisfy this premise.

They prioritize speed and simplicity, often leading to good-enough solutions rather than the best possible ones.

While heuristics can be effective in many situations, they may not always provide the optimal results that machine learning algorithms can achieve within the same restrictions.

Are Some Parts Of Machine Learning Heuristic In Nature?

When examining the inner workings of machine learning, it’s interesting to note that some aspects are indeed heuristic.

While the overall process relies on optimization and data-driven techniques, certain decisions made while developing a machine-learning model can be based on heuristics.

One example of a heuristic aspect in machine learning is the selection of input variables, also known as features.

These features are used to train the model, and choosing the right set is crucial for the model’s performance. 

The decision of which features to include or exclude is often based on domain knowledge and experience, making it a heuristic decision.

Another heuristic component in machine learning can be found in the design of neural networks.

A neural network’s topology or structure, including the number of layers and neurons in each layer, can significantly impact its performance.

While some guidelines exist for creating an effective neural network, the final design often comes down to trial and error, guided by heuristics (and intuition).

Maybe you notice that whenever someone buys graham crackers (my favorite), they also purchase marshmallows and Hershey chocolate bars. An obvious heuristic would be to suggest these products to customers together, 

However, using a machine learning algorithm to analyze customer behavior data and generate tailored shopping suggestions is a more advanced and accurate method, which would find much deeper relationships between item purchases.

Even so, certain heuristic decisions, like excluding irrelevant features such as the current outside temperature when building a model about financial decisions (as an example), will always play a role in developing a high-quality machine learning model.

Ultimately, the decision between heuristic algorithms and machine learning should be driven by a comprehensive understanding of the problem at hand, coupled with an awareness of the strengths and limitations inherent in each approach.

In many cases, a hybrid approach that combines the interpretability of heuristic algorithms with the predictive power of machine learning may offer the most effective solution.

Thus, rather than viewing heuristic algorithms and machine learning as competing paradigms, it is more fruitful to consider them as complementary tools in the data scientist’s toolkit, each serving a unique role in addressing complex real-world challenges.

Pytorch Lightning is a high-performance wrapper for Pytorch, providing a convenient way to train models on multiple GPUs. Tensorflow lite is designed to put pre-trained Tensorflow models onto mobile phones, reducing server and API calls since the model runs on the mobile device.

While this is just the general difference between the two, this comprehensive guide will highlight a few more critical differences between TensorFlow Lite and PyTorch Lightning to really drive home when and where you should be using each one.

We’ll also clarify whether PyTorch Lightning is the same as PyTorch and if it’s slower than its parent framework.

So, buckle up and get ready for a thrilling adventure into machine learning – and stay tuned till the end for an electrifying revelation that could change how you approach your next AI project!

Understanding The Difference Between PyTorch Lightning and TensorFlow Lite

Before we delve into the specifics of each framework, it’s crucial to understand the fundamental differences between PyTorch Lightning and TensorFlow Lite.

While both tools are designed to streamline and optimize machine learning tasks, they serve distinct purposes and cater to different platforms.

PyTorch Lightning: High-performance Wrapper for PyTorch

PyTorch Lightning is best described as a high-performance wrapper for the popular PyTorch framework.

It provides an organized, flexible, and efficient way to develop and scale deep learning models.

With Lightning, developers can leverage multiple GPUs and distributed training with minimal code changes, allowing faster model training and improved resource utilization.

This powerful tool simplifies the training process by automating repetitive tasks and eliminating boilerplate code, enabling you to focus on the core research and model development.

Moreover, PyTorch Lightning maintains compatibility with the PyTorch ecosystem, ensuring you can seamlessly integrate it into your existing projects.

TensorFlow Lite: ML on Mobile and Embedded Devices

On the other hand, TensorFlow Lite is a lightweight, performance-optimized framework designed specifically for deploying machine learning models on mobile and embedded devices.

It enables developers to bring the power of AI to low-power, resource-constrained platforms with limited internet connectivity.

TensorFlow Lite relies on high-performance C++ code to ensure efficient execution on various hardware, including CPUs, GPUs, and specialized accelerators like Google’s Edge TPU.

It’s important to note that TensorFlow Lite is not meant for training models but rather for running pre-trained models on mobile and embedded devices.

What Do You Need To Use TensorFlow Lite

To harness the power of TensorFlow Lite for deploying machine learning models on mobile and embedded devices, there are a few essential components you’ll need to prepare. 

Let’s discuss these prerequisites in detail:

A Trained Model

First and foremost, you’ll need a trained machine-learning model.

This model is usually developed and trained on a high-powered machine or cluster using TensorFlow or another popular framework like PyTorch or Keras.

The model’s architecture and hyperparameters are fine-tuned to achieve optimal performance on a specific task, such as image classification, natural language processing, or object detection.

Model Conversion

Once you have a trained model, you must convert it into a format compatible with TensorFlow Lite.

The conversion process typically involves quantization and optimization techniques to reduce the model size and improve its performance on resource-constrained devices.

TensorFlow Lite provides a converter tool to transform models from various formats, such as TensorFlow SavedModel, Keras HDF5, or even ONNX, into the TensorFlow Lite FlatBuffer format.

More information on it can be found here.

Checkpoints

During the training process, it’s common practice to save intermediate states of the model, known as checkpoints.

Checkpoints allow you to resume training from a specific point if interrupted, fine-tune the model further, or evaluate the model on different datasets. 

When using TensorFlow Lite, you can choose the best checkpoint to convert into a TensorFlow Lite model, ensuring you deploy your most accurate and efficient version.

When would you use Pytorch Lightning Over Regular Pytorch?

While PyTorch is a compelling and flexible deep learning framework, there are specific scenarios where using PyTorch Lightning can provide significant benefits.

Here are a few key reasons to consider PyTorch Lightning over regular PyTorch:

Minimize Boilerplate Code

Developing deep learning models often involves writing repetitive and boilerplate code for tasks such as setting up training and validation loops, managing checkpoints, and handling data loading.

PyTorch Lightning abstracts away these routine tasks, allowing you to focus on your model’s core logic and structure.

This streamlined approach leads to cleaner, more organized code that is easier to understand and maintain throughout a team of machine learning engineers.

Cater to Advanced PyTorch Developers

While PyTorch Lightning is built on top of PyTorch, it offers additional features and best practices that can benefit advanced developers.

With built-in support for sophisticated techniques such as mixed-precision training, gradient accumulation, and learning rate schedulers, PyTorch Lightning can further enhance the development experience and improve model performance.

Enable Multi-GPU Training

Scaling deep learning models across multiple GPUs or even multiple nodes can be a complex task with regular PyTorch.

PyTorch Lightning simplifies this process by providing built-in support for distributed training with minimal code changes.

This allows you to leverage the power of multiple GPUs or even a cluster of machines to speed up model training and reduce overall training time.

Reduce Error Chances in Your Code

By adopting PyTorch Lightning, you can minimize the risk of errors in your code due to its structured approach and automated processes.

Since the framework handles many underlying tasks, you’ll be less likely to introduce bugs related to training, validation, or checkpoint management – Think about it, with Pytorch Lightning, you’ll actually be writing less code – and when you’re writing less code – you’ll naturally make fewer errors.

Additionally, the standardized design of PyTorch Lightning promotes code reusability and modularity, making it easier to share, collaborate, and troubleshoot your models.

Think of a simulation as a virtual representation of a real-life scenario. 

Simulation is used in basically every field, such as engineering, science, and finance. 

Using data science techniques, we can better understand the data used in our simulations, leading us to better outputs. We can make simulations even more accurate and reliable by taking advantage of data science.

Whether you’re a student, a scientist, or just someone interested in making your simulations a bit more accurate, you’ll learn something new and valuable from this post. 

So let’s jump right in!

What Exactly Is A Simulation?

A simulation is a virtual representation of a real-life scenario.

It’s like a model of a real-world situation, but it exists in a computer or a virtual environment. Think about a video game highly representative of the real world since it was built with statistics of the world behind it.

The goal of a simulation is to make the simulation as close as possible to what might actually happen in real life. 

For example, if you wanted to know what would happen if you added an extra person to a line at the airport, you could create a simulation to study that specific situation. This would help you understand how the line would change anything and everything relevant to the line and what secondary effects it might have.

They allow us to study and analyze real-world scenarios without physically carrying out the experiment or situation.

This saves time, money, and resources and allows us to study situations that might be too dangerous, difficult, or expensive to study in real life.

How Can Data Science Improve The Accuracy Of A Simulation?

Data science can be a valuable tool for improving the accuracy of simulations.

Using everyday data science techniques, we can get more accurate simulation inputs and better understand the outputs.

Here’s how:

Accurate Inputs

Data science techniques can be used to extract highly accurate and relevant distributions and rates from data.

This is perfect because you’ll need data if you plan to do any simulations.

This extracted information can then be directly plugged into our simulations, creating more accurate – and thus more representative – simulations. 

For example, suppose we were trying to simulate traffic movement in a city. In that case, data science could help us gather data on traffic patterns, road conditions, and other factors that highly affect the simulation.

Think about it this way, if you were given a very messy dataset, how would you find the numbers needed to supply your simulation?

To get these, you’d pull directly from data science techniques, allowing you to quickly find the patterns and distributions in your data to build your simulation model.

Fake Data

Data science can also be used to create fake or synthetic data for simulations. This can be especially useful when actual data is unavailable, too difficult, or expensive to collect.

Using statistical methods, machine learning algorithms, predictive analytics, and correlation to compute and predict new values, data scientists can generate synthetic data that highly resembles high-quality data. 

This synthetic data can then be used in simulations to test and evaluate different scenarios for which data scientists can’t find relevant data. 

For example, suppose we were trying to simulate the spread of a disease in a population. In that case, data science could help us generate synthetic data on the population’s demographics, health status, and movement patterns – without needing the actual “real” population data.

This synthetic data could then be used in the simulation to study how the disease might spread under different conditions.

The benefit of using synthetic data is that it allows us to create simulations without relying on actual data.

This can also save time, money, and resources, allowing us to study situations that might be too dangerous, complex, highly unique, or expensive to study in real life.

Handle Large Data

Data science can be a valuable tool for improving the accuracy of simulations by allowing us to process high amounts of data.

With the help of data science techniques, we can analyze and make sense of large amounts of data, which can be used in simulations to create more accurate simulations of real-world scenarios.

For example, suppose we were trying to simulate adding a new bank in a city. In that case, data science could help us gather data on other banks, spending habits, and other factors that would have a noticeable effect on the simulation.

With this information, we could create a more accurate simulation of money flow in the city and even simulate other things like adding a couple of banks or a new restaurant. 

Understanding Outputs

Data science can also help us better understand the outputs of simulations.

While data is hard to understand, data science techniques show us what to look for in data, giving us an almost end goal to the data received from our simulations.

By analyzing the simulation results, we can identify patterns and trends and make more informed decisions about improving the simulation.

Another Avenue

Data science can help create more accurate simulations by literally allowing us to generate more simulations. 

While most receive their simulation values and have to be happy with them, Data science techniques, such as machine learning algorithms and statistical methods, can help us quantify new avenues and angles from our data, leading us to create more simulations.

Does Accuracy Matter In A Simulation?

Accuracy matters in simulations because it helps us make better decisions and predictions. 

By creating simulations that are as close as possible to what might happen in real life, we can better understand the situation and make more informed decisions about improving it.

In most fields, such as engineering, science, and finance, accuracy is critical since the difference of inches or percentages has a massive effect on the world around us.

Why Do We Need Simulations, If We Have Data Science?

Data science and simulations are tools that fall under the umbrella of statistics, and they have unique benefits and purposes.

Data science can be used to analyze and make sense of large amounts of data, and it provides us with answers/predictions for one point in time.

For example, if we were trying to understand how many people in a city use the public restroom, data science could quickly predict the whole year (if you had the data).

However, simulations allow us to see how things change over time. 

They visually represent a real-life scenario and help us understand how things might change or evolve. 

If we were trying to understand how traffic might build up in a city in the future, we could use a simulation to study the situation. This would help us understand how the traffic might change over time and what might happen when certain conditions change.

In short, we need simulations because they allow us to see how things change, while data science provides answers for one point in time. Both data science and simulations are valuable tools in the field of statistics, and they can be used together to understand real-world scenarios better.

Other Articles In Our Accuracy Series:

Accuracy is used EVERYWHERE, which is fine because we wrote these articles below to help you understand it

• Can Machine Learning Models Give An Accuracy Of 100
• What Is a Good Accuracy Score In Machine Learning?
• High Accuracy Low Precision In Machine Learning
• Data Science Accuracy vs. Precision
• Machine Learning Validation Accuracy
• Machine Learning: High Training Accuracy And Low Test Accuracy

Turning your machine-learning models from experimental projects into battle-tested production systems is no walk in the park. In fact, it can feel like taming a wild beast at times!

This blog post will explore the five key challenges that can make or break your machine-learning project in a production environment.

We’ll cover the importance of data quality and consistency, the daunting task of model training and tuning, the hurdles of scalability and efficiency, the intricacies of model deployment, and the never-ending quest for monitoring and maintenance.

By the end of this post, you’ll have a solid understanding of these challenges and be better equipped to face them head-on.

As a bonus, we’ll also reveal some industry secrets and best practices that can help you tackle these challenges like a pro.

Buckle up, grab your favorite caffeinated beverage, and embark on a journey to transform your machine-learning projects from fragile prototypes into robust production powerhouses!

Model Training and Tuning in Machine Learning – The Hidden Costs and Challenges

The journey to operationalize a machine learning model starts with training and tuning, which can be complex and resource-intensive.

As you embark on this adventure, you’ll soon realize that finding quality data and managing the costs associated with model training are just the tip of the iceberg.

Let’s delve into some key challenges you’ll face when training and tuning your models for production systems.

Expensive training

State-of-the-art machine learning models, especially deep learning models, require significant computational resources for training. This can lead to high costs, mainly when using cloud-based GPU or TPU instances. You must optimize your training process and carefully manage your resources to minimize expenses.

Quality data scarcity

Acquiring high-quality, representative, and unbiased data is essential for training accurate and reliable models.

However, finding such data can be an arduous task. You may need to invest time and effort in data collection, cleaning, and preprocessing before your data is suitable for training.

Hyperparameter optimization

Machine learning models often have multiple hyperparameters that must be tuned to achieve optimal performance. Exhaustive search methods like grid search can be time-consuming and computationally expensive, whereas random search and Bayesian optimization methods can be more efficient but still require trial and error.

Model selection:

Choosing the right model architecture for your problem can be challenging, as numerous options are available, each with its strengths and weaknesses.

It is crucial to evaluate different models based on their performance on your specific dataset and use case and their interpretability and computational requirements.

Overfitting and underfitting:

Striking the right balance between model complexity and generalization is essential for good performance in production systems.

Overfitting occurs when a model learns the noise in the training data, leading to poor performance on unseen data. Conversely, underfitting happens when a model fails to capture the underlying patterns in the data, resulting in suboptimal performance.

Scalability and Efficiency – Navigating the Highs and Lows of Machine Learning Performance

Once you have a well-trained and optimized model, the next challenge is to scale it effectively to handle real-world scenarios. Scalability and efficiency are crucial factors that can determine the success or failure of your machine learning project in a production environment.

In this section, we’ll discuss some key aspects you’ll need to consider to ensure your model performs at its best, even as it grows and evolves.

Handling large datasets

Machine learning models often need to process massive amounts of data, posing challenges in memory usage and processing time.

Employing techniques like data partitioning, parallelization, and incremental learning can help you manage large datasets more effectively.

Distributed processing: 

As your models’ and datasets’ complexity and size grow, employing distributed processing across multiple machines or clusters may become necessary.

This can help you to scale your models and reduce training times, but it also introduces additional complexity in managing and orchestrating these distributed systems.

Hardware acceleration

Specialized hardware like GPUs, TPUs, and FPGAs can significantly improve the efficiency and speed of your machine-learning models.

However, leveraging these technologies often requires additional expertise and can lead to increased infrastructure costs.

Model Optimization 

Optimizing your models for efficiency and performance is essential, mainly when dealing with limited resources or strict latency requirements.

Techniques like quantization, pruning, and model compression can help reduce your model’s computational demands while maintaining acceptable accuracy levels.

Real-time processing: 

In some applications, machine learning models must process and respond to data in real-time, which can strain your infrastructure and require careful planning to ensure low-latency responses. Employing streaming data processing and efficient model architectures can help you achieve real-time performance.

Auto-scaling: 

As the demand for your machine learning system fluctuates, it’s essential to have a robust auto-scaling strategy in place. This will allow you to automatically adjust the number of resources allocated to your system, ensuring optimal performance and cost-efficiency.

Load balancing: 

Distributing the workload across multiple instances or nodes is crucial for maintaining high performance and availability in your machine learning system. Load balancing techniques can help you achieve this by efficiently distributing requests and preventing bottlenecks.

Model Deployment – Bridging the Gap Between Research and Production

After overcoming the challenges of model training, tuning, and scalability, the next step is to deploy your machine-learning models into production environments.

Model deployment is a critical phase where the rubber meets the road, and your models are integrated into real-world applications. This section will discuss some key considerations and challenges you’ll encounter when deploying your models for production use.

Deployment infrastructure: 

Choosing the proper infrastructure for your machine learning models is crucial, as it can impact performance, scalability, and cost. Options include on-premises servers, cloud platforms, and edge devices, each with pros and cons.

Containerization: 

Containerization technologies like Docker can simplify deployment by packaging your models, dependencies, and configurations into a portable, self-contained unit.

This enables you to deploy your models more easily across various environments and platforms.

Model serving: 

Serving your models effectively is crucial for seamless integration into production systems. This may involve setting up RESTful APIs, using model serving platforms like TensorFlow Serving or MLflow, or implementing custom solutions tailored to your specific use case.

Data pipelines: 

You’ll need to build and manage robust data pipelines to ensure your models receive the correct data at the right time. This may involve preprocessing, data transformation, and data validation, which must be orchestrated and monitored to guarantee smooth operation.

Integration with existing systems: 

Deploying machine learning models often require integration with existing software systems and workflows. This can be challenging, as it may necessitate adapting your models to work with legacy systems, APIs, or custom protocols.

Continuous integration and continuous deployment (CI/CD): 

Implementing CI/CD practices can help you streamline the deployment process and reduce the risk of errors. This involves automating tasks like building, testing, and deploying your models and monitoring their performance in production environments.

Model versioning: 

Managing different versions of your models, data, and code is essential for reproducibility, traceability, and smooth updates. Tools like Git, DVC, or MLflow can help you effectively maintain version control and manage your machine learning assets.

Model Monitoring and Maintenance – Ensuring Longevity and Reliability in Production

Once your machine learning models are deployed, the journey doesn’t end there. Model monitoring and maintenance are crucial to ensuring the continued success of your models in production environments.

This final section will discuss critical aspects of monitoring and maintaining your machine-learning models to guarantee their reliability, accuracy, and longevity.

Performance monitoring: 

Continuously tracking your model’s performance metrics, such as accuracy, precision, recall, or F1 score, is crucial for detecting issues early and maintaining high-quality predictions.

Setting up automated monitoring and alerting systems can help you stay on top of your model’s performance and address any issues promptly.

Data drift detection: 

Real-world data can change over time, causing shifts in data distribution that can negatively impact your model’s performance.

Regularly monitoring for data drift and updating or retraining your models as needed can help you maintain their accuracy and relevance.

Model drift detection: 

As the underlying patterns in the data change, your models may become less effective at making accurate predictions.

Detecting and addressing model drift is essential for ensuring your models remain reliable and useful in the ever-changing production environment.

Logging and auditing: 

Maintaining comprehensive logs of your model’s predictions, inputs, and performance metrics can help you track its behavior, identify issues, and support audit requirements.

Implementing robust logging and auditing practices is essential for transparency and accountability.

Model updates and retraining: 

Regularly updating and retraining your models with new data is crucial in keeping them accurate and relevant.

This may involve fine-tuning your models with new data, re-evaluating model performance, or exploring alternative model architectures and techniques to improve performance.

Security and compliance: 

Ensuring that your machine learning models comply with data protection regulations and industry standards is critical for maintaining trust and avoiding legal or financial repercussions.

Regularly reviewing and updating your security and privacy practices can help you safeguard sensitive data and protect your machine-learning systems from potential threats.

This is a common problem that ALL data scientists face. 

But don’t worry; we know just the fix! 

In this post, we’ll talk about what it means to have high training accuracy and low test accuracy and how you can fix it. 

However, we want to emphasize that this is probably the wrong approach towards your modeling methods, and another technique could give you a much better insight into your modeling experience.

So, stay tuned and get ready to become an expert in machine learning!

Why Do We Need To Score Machine Learning Models?

Like in sports, where you keep score to track how you’re doing, in machine learning, we also need to score our models to see how well they perform.

This is important because you need to track your model’s performance to know if it’s making any decent predictions.

And to Score our Models, we use a thing called metrics. 

Metrics are tools that help machine learning engineers and data scientists measure the performance of our models. 

There are TONS of different metrics, so it’s essential to understand which metrics are best for your problem.

Hint, accuracy is not always the best fit!

For example, if you’re building a model to predict whether a patient has a particular disease, you might use metrics like accuracy, precision, and recall to measure its performance.

On the other hand, if you’re building a model to predict the price of a house, you might use metrics like mean absolute error or root mean squared error.

What Does High Training Accuracy and Low Test Accuracy Mean?

When you train a machine learning model, you split your data into training and test sets.

The model uses the training set to learn and make predictions, and then you use the test set to see how well the model is actually performing on new data.

If you find that your model has high accuracy on the training set but low accuracy on the test set, this means that you have overfit your model. 

Overfitting occurs when a model too closely fits the training data and cannot generalize to new data.

In other words, your model has memorized the training data but fails to predict on data accurately it has yet to see.

This can have a few different causes.

First, It could simply mean that accuracy isn’t the right metric for your problem. 

For example, suppose you’re building a model to predict whether a patient has a certain disease. In that case, accuracy might not be the best metric to use because you want to be sure that you catch all instances of the disease, even if that means having some false positive results. In scenarios like this, accuracy can be biased due to your dataset’s low amounts of actual true positives.

Another cause of high training and low test accuracy is simply needing a better model. This could be because your model is too complex or because it’s not capturing the underlying patterns in the data.

In this case, you should try a different model or change the model parameters you’re using.

Should Training Accuracy Be Higher Than Testing Accuracy?

In machine learning, it’s typical for the training accuracy to be a bit higher than the testing accuracy. This is because the model uses the training data to make predictions, so it’s expected to perform slightly better on the training data.

However, if the difference between the training and testing accuracy is too significant, this could indicate a problem. 

You generally want the difference between the training and testing accuracy to be as small as possible. If the difference is too significant, it could mean your model is not performing well on new data and needs improvement.

It’s important to remember that slight overfitting is impossible to avoid entirely. However, if you see a large difference between the training and testing accuracy, it’s a sign that you may need to make changes to your model or the data you’re using to train it.

However, in the next section, I argue that you should completely change how you do this WHOLE process.

Should I Even Be Testing My Model This Way?

When building a machine learning model, you’ve probably been told a thousand times that it’s essential to split your data into a training set and a test set to see how well your model is performing. (This is called a train test split)

However, a train test split only uses a single random subset of your data as the test set…

This means that you’re only getting a single score for your model, which might not represent how your model would perform over all of the data.

Think about it this way, what if you tested a different “test” set from your model and got a completely different score, which is the one you’d report to your manager?

Cross Validation is Superior To Train Test Split

Cross-validation is a method that solves this problem by giving all of your data a chance to be both the training set and the test set.

In cross-validation, you split your data into multiple subsets and then use each subset as the test set while using the remaining data as the training set. This means you’re getting a score for your model on all the data, not just one random subset.

The score from cross-validation is a much better representation of your model’s performance than a single-train test split score.

This is because the cross-validation score is the average test score from each subset of your entire dataset, not just one random part. 

This gives you a more accurate picture of how well your model is actually performing and helps you make better decisions about your model.

Can you always use Cross Validation?

Cross Validation can only be used in independent data. This means things like time-series data or other non-independent data are off-limits for cross-validation. While you can write a book on this topic (and we won’t cover it here), we wanted to emphasize this before Cross Validation becomes your only go-to modeling method. 

Other Articles In Our Accuracy Series:

Accuracy is used EVERYWHERE, which is fine because we wrote these articles below to help you understand it

• Can Machine Learning Models Give An Accuracy Of 100
• High Accuracy Low Precision In Machine Learning
• How Can Data Science Improve The Accuracy Of A Simulation?
• Machine Learning Validation Accuracy
• What Is a Good Accuracy Score In Machine Learning?
• Data Science Accuracy vs. Precision

You see, there are some particular deep-learning problems (neural networks) where we need an extra tool to ensure our model is “getting it.”

For this, we usually utilize a validation set.

However, this validation set is usually used to improve model performance in a different way instead of emphasizing the accuracy of the machine learning model.

While that may seem confusing, we will clear everything up below. We’ll look closer at validation accuracy and how it’s different ideologically from training and testing accuracy.

We’ll also share some cool insights that’ll make you a machine-learning whiz in no time. 

So, buckle up and get ready to learn something amazing!

What’s The Difference Between Validation Accuracy And Testing Accuracy?

As we dive deeper into machine learning, it’s essential to understand the distinction between validation and testing accuracy. 

At first glance, the difference may seem simple: validation accuracy pertains to the validation set, while testing accuracy refers to the test set.

However, this superficial comparison doesn’t capture the true essence of what sets them apart.

In reality, the validation set plays a unique role in the machine learning process.

It’s primarily used for tasks like assessing the performance of a model’s loss function and monitoring its improvement. The validation set also helps us determine when to halt the training process, a technique known as early stopping. 

By contrast, the test set is used to evaluate a model’s performance in a more comprehensive manner, providing a final accuracy score that indicates how well the model generalizes to unseen data.

In other words, while the validation set helps us fine-tune our model during training, the test set is our ultimate measuring stick.

We can obtain a true accuracy score only when we utilize the test set, which tells us how well our model will likely perform when faced with real-world challenges.

You can safely report this accuracy score to your boss, not the one from the training or validation set.

Understanding the nuances between validation and testing datasets is crucial for anyone delving into machine learning. 

By recognizing their distinct roles in developing and evaluating models, we can better optimize our approach to training and testing, ultimately leading to more accurate and robust machine learning solutions.

Do I Even Need A Validation Set?

When building our models, we must ask ourselves whether a validation set is always necessary. 

To answer this question, let’s first consider the scenarios where validation sets play a crucial role.

Validation datasets are predominantly used in deep learning, mainly when working with complex neural networks.

These networks often require fine-tuning and monitoring during the training process, and that’s where the validation set steps in.

However, it’s worth noting that deep learning is just a slice of the machine learning spectrum.

In fact, about 90%+ of machine learning problems (This number is from personal experience) are tackled through supervised learning.

In these cases, validation sets don’t typically play any role.

This might lead you to believe only training and test sets are needed for supervised learning.

While that’s true to some extent, there’s an even better technique to ensure you thoroughly understand your model’s performance is cross-validation.

Cross-validation is a robust method that involves dividing your dataset into multiple smaller sets, or “folds.”

You then train your model on a combination of these folds and test it on the remaining one.

This process is repeated several times, with each fold serving as the test set once.

By using cross-validation, you can obtain a more accurate and reliable estimation of your model’s performance.

Does Cross Validation Use A Validation Set?

While we now know that cross-validation is perfect for supervised learning, It’s natural to wonder how cross-validation fits into the bigger picture, especially when using validation sets. 

Simply put, if you’re using cross-validation, there’s no need for a separate validation set.

To understand why, let’s first recap what cross-validation entails. During this process, your dataset is divided into several smaller sets, or “folds.” The model is then trained on a combination of these folds and tested on the remaining one. This procedure is repeated multiple times, with each fold taking its turn as the test set.

Essentially, cross-validation ensures that each piece of data is used for both training and testing at different times.

Introducing a separate validation dataset doesn’t make sense in this context. In cross-validation, the data already serves the purpose of training and testing, eliminating the need for an additional validation set.

By leveraging the power of cross-validation, you can obtain a more accurate and reliable estimation of your model’s performance without the added complexity of a validation dataset.

Can Validation Accuracy Be 100%?

So, let’s say you’ve encountered scenarios or an epoch where your model’s validation accuracy reaches a seemingly perfect 100%. 

Is this too good to be true? 

Let’s explore some factors to consider when encountering such “extraordinary results.”

First and foremost, it’s important to determine whether this 100% validation accuracy is a one-time occurrence during the training process or a consistent trend.

If it’s a one-off event, it may not hold much significance.

However, if you’re consistently achieving high scores on your predictions, it’s time to take a look at your validation set more closely.

It’s crucial to ensure that your validation set isn’t silently biased.

For example, in a deep learning classification problem, you’ll want to verify that your validation data doesn’t exclusively represent one category. 

This could lead to an illusion of perfection, while in reality, your model may not be generalizing well to other categories.

Finally, remember that accuracy isn’t always the best metric to evaluate your model.

Other metrics such as precision, recall, or F1-score might be more suitable depending on the problem at hand – especially in the context of problems trying to solve for “rare events.”

Solely relying on accuracy could falsely assess your model’s actual performance.

And thus make the machine learning engineer behind it look a bit silly.

What Percentage Should Of Our Data Should The Validation Set Be?

Determining the ideal percentage of data to allocate for the validation set can be a perplexing task.

If you don’t live under a rock, You may have encountered standard rules of thumb like “use 10%!”

However, these one-size-fits-all guidelines can be shortsighted and may only sometimes apply to some situations.

The truth is, the best percentage for your validation set depends on your specific dataset.

Although there is no universally applicable answer, the underlying goal remains the same: you want your training dataset to be as large as possible.

This principle is based on the idea that the quality of your training data directly impacts the performance of your algorithm. And as you might already know, one of the most straightforward ways to enhance your training data is to increase its size.

More data allows your model to learn better patterns, which leads to improved generalization (less overfitting) when faced with new, unseen data.

Other Articles In Our Accuracy Series:

Accuracy is used EVERYWHERE, which is fine because we wrote these articles below to help you understand it

• Can Machine Learning Models Give An Accuracy Of 100
• High Accuracy Low Precision In Machine Learning
• Data Science Accuracy vs. Precision
• Machine Learning: High Training Accuracy And Low Test Accuracy
• How can Data Science Improve The Accuracy Of A Simulation?
• What Is a Good Accuracy Score In Machine Learning?

Two popular models often competing for the spotlight are Vector Autoregression (VAR) and Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX).

Both models have their unique strengths, but the choice ultimately depends on the structure of your data and the type of problem you’re trying to solve.

The main difference between the two is their ability to handle multiple time series: VAR is built for multivariate time series analysis, while ARIMAX focuses on univariate time series with exogenous variables.

Below, we’ll go more in-depth on the VAR and ARIMAX models, discuss some differences between moving averages and autoregressive formulation and explain some of the tough-to-understand terms used above.

You’re not going to want to miss this one. 

Differences Between Autoregression and Moving Average

Understanding the difference between Autoregression (AR) and Moving Average (MA) is essential when diving into the world of time series analysis.

Let’s break down these concepts in a way that everyone can understand.

Autoregression (AR) is about using the past values, or “lags,” of a time series to predict future values.

Imagine you’re trying to forecast the temperature for tomorrow. If you know that today’s temperature was 75 degrees and yesterday’s was 72 degrees, you could use this information to make a prediction.

In other words, AR models rely on the idea that the past can help predict the future.

Moving Average (MA), however, is focused on the errors, or “error lags,” in the time series. Let’s say you tried to predict the temperature for yesterday and made an error in your forecast.

An MA model would look at your past errors to help better predict today and tomorrow. This way, the model learns from its mistakes and improves its forecasting ability over time – based on the assumption that errors have some trend.

Understanding the difference between these two forecasting ideologies is HUGE when trying to understand the difference between ARIMAX and VAR.

One Vs. Many

Before we continue diving into the differences between VAR and ARIMAX, we must understand the terms “multivariate” and “univariate.”

In time series analysis, “multivariate” means working with multiple time series simultaneously, while “univariate” means focusing on just one time series. 

Now, let’s explore how VAR and ARIMAX are designed for these different situations.

Vector Autoregression (VAR) is designed explicitly for multivariate time series analysis.

This means it can handle multiple time series that might be related to each other.

For example, if you wanted to forecast the prices of several stocks in the market, a VAR model could consider how the prices of these stocks influence each other over time.

This makes VAR a powerful tool for understanding complex relationships between multiple time series.

On the other hand, Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) is built for univariate time series analysis, which means it focuses on just one time series.

However, it has an added twist: it can incorporate exogenous variables. 

Exogenous variables are simply just external factors that might affect the time series but aren’t part of it. 

For instance, if you were forecasting the sales of a particular product, you might want to consider factors like the price, advertising campaigns, or even the weather. These external factors can help improve the accuracy of the ARIMAX model’s forecasts.

Is VAR better than Arimax?

Asking if Vector Autoregression is better than ARIMAX is the wrong way to think about things.

Deciding between (VAR) and ARIMAX mostly depends on the specific problem you’re working on and the nature of your data. 

Each model has advantages; the best choice depends on your unique situation.

Let’s review some factors to consider when choosing between VAR and ARIMAX:

The number of time series

If you are dealing with interconnected time series, VAR is the better choice because it is designed for multivariate analysis. On the other hand, if you are working with a single time series, ARIMAX would be more appropriate.

Exogenous variables

If external factors influence your time series, ARIMAX is useful because it allows you to incorporate these exogenous variables. VAR does not have this feature, so if exogenous variables are critical to your analysis, ARIMAX may be the better choice.

Model complexity

VAR models can become quite complex when dealing with multiple time series, which may require more computational power and time to estimate. If you need a simpler model and have only one time series to analyze, ARIMAX might be more suitable.

Interpretability

ARIMAX models can be easier to interpret when dealing with exogenous variables, as you can directly see the impact of these external factors on your time series. In contrast, VAR models focus on the relationships between multiple time series, which can be more challenging to understand and explain.

Are Arimax and VAR the only two Time Series Models?

While ARIMAX and VAR are popular time series models, they are not the only options for time series analysis. There is a wide variety of models to choose from, each with its strengths and weaknesses. Here are a few other common time series models to consider:

Autoregressive (AR) model

This univariate model uses the time series’s past values, or lags, to make predictions. It is a simpler version of ARIMAX without the integrated moving average or exogenous variables components.

Moving Average (MA) model

Another univariate model, the MA model, focuses on past errors, or error lags, to improve its forecasting ability.

Autoregressive Integrated Moving Average (ARIMA) model

Combining the AR and MA models, the ARIMA model also accounts for differencing to make the time series stationary. It is essentially an ARIMAX model without exogenous variables.

Seasonal Decomposition of Time Series (STL)

This technique breaks down a time series into its trend, seasonal, and residual components. It can help analyze time series with strong seasonality.

Exponential Smoothing State Space Model (ETS)

This family of models includes simple, double, and triple exponential smoothing, which can be used for forecasting univariate time series with different levels of trend and seasonality.

Long Short-Term Memory (LSTM) networks

These are a type of recurrent neural network designed explicitly for sequence data, such as time series. They can be helpful for complex problems and large datasets where traditional time series models may struggle. (Ever Heard of ChatGPT?)

Believe it or not, SVG is a machine-learning algorithm, and we’re here to both prove it and clarify the confusion surrounding this notion.

Some might wonder how SVG, a widely known design-based algorithm, could be related to machine learning. 

Well, hold on to your hats because we’re about to dive deep into the fascinating world of SVG, fonts, design, and machine learning.

In this post, we’ll explore the connections between these two seemingly unrelated fields, and we promise that by the end, you’ll have a whole new appreciation for SVG and its unique role in machine learning. 

Stay tuned for an exciting journey that will challenge your preconceptions and shed light on the hidden depths of SVG!

What Is SVG, and where did it come from?

The origins of Scalable Vector Graphics (SVG) can be traced back to a groundbreaking research paper that aimed to model fonts’ drawing process using sequential generative vector graphics models.

This ambitious project sought to revolutionize our understanding of vision and imagery by focusing on identifying higher-level attributes that best summarized various aspects of an object rather than exhaustively modeling every detail.

In plain English, SVG works as a machine learning algorithm using mathematical equations to create vector-based images.

Unlike raster graphics that rely on a grid of pixels to represent images, vector graphics are formed using paths defined by points, lines, and curves.

These paths can be scaled, rotated, or transformed without any loss of quality, making them highly versatile and ideal for graphic design applications.

SVG’s machine learning aspect comes into play through its ability to learn a dataset’s statistical dependencies and richness, such as an extensive collection of fonts.

By analyzing these patterns, the SVG algorithm can create new font designs or manipulate existing ones to achieve desired styles or effects.

This is made possible by exploiting the latent representation of the vector graphics, which allows for systematic manipulation and style propagation.

It also brilliantly plays off of traditional epoch training, where each new “design” can be an entire training session of the data. While formal machine learning has low expectations for some of the first outputs of a trained model, these seemingly un-trained representations can have unique designs.

SVG is a powerful tool for creating and manipulating vector graphics and a sophisticated machine-learning algorithm. 

Its applications in the design world are vast.

It continues to revolutionize the way we approach graphic design by enabling designers to create, modify, and experiment with fonts and other visual elements more efficiently and effectively than ever before.

Why The Internet Is Wrong, and SVG is a machine learning algorithm.

Despite the clear evidence provided by the research paper authored by Raphael Gontijo Lopes, David Ha, Douglas Eck, and Jonathon Shlens, a quick Google search may lead you to believe that SVG is not a machine-learning algorithm.

However, this widely circulated misconception couldn’t be further from the truth.

As stated in the paper, SVG employs a class-conditioned, convolutional variational autoencoder, which is undeniably a machine learning algorithm. Variational autoencoders (VAEs) are a type of generative model that learn to encode data into a lower-dimensional latent space and then decode it back to its original form.

In the case of SVG, this algorithm captures the essence of fonts and other vector graphics, enabling the creation and manipulation of these designs more efficiently.

The SVG algorithm is not just any ordinary machine learning algorithm; it can be considered state-of-the-art.

By harnessing the power of convolutional neural networks (CNNs) and VAEs, SVG has demonstrated remarkable capabilities in capturing intricate patterns and dependencies within large datasets of fonts and other graphics.

This makes it an invaluable tool for graphic designers and researchers, as it facilitates generating new designs and exploring creative possibilities.

So, the next time you come across information suggesting that SVG is not a machine learning algorithm, remember the groundbreaking research by Lopes, Ha, Eck, and Shlens that proves otherwise.

In fact, SVG is not only a machine learning algorithm but a state-of-the-art one with the potential to revolutionize how we approach graphic design and push the boundaries of our creative capabilities.

Link To The Paper:

https://arxiv.org/abs/1904.02632 

Why You Should Be Careful Trusting Anything You See

The misconception surrounding SVG being unrelated to machine learning is a prime example of why it’s essential to approach information on the internet with a critical eye.

While the internet is an invaluable resource for knowledge and learning, it’s also rife with misinformation and half-truths.

Before accepting anything you read or see online as fact, make sure to verify its accuracy by cross-referencing multiple sources or consulting reputable research papers and experts in the field.

Being vigilant in your quest for accurate information will help you avoid falling prey to misconceptions, form well-informed opinions, and make better decisions in other aspects of life.

While they may seem similar, they are actually fundamentally different fields. 

Choosing the right path to study can significantly impact your future career, and making the right choice can cut down the time it takes to get one of these jobs A TON.

But don’t worry; we’ve got you covered! 

In this blog post, we’ll break down the key differences between data science and machine learning and help you decide which is right FOR YOU. 

Keep reading to find out which field is best for you, why we separate these two, and some extra information so you can feel confident about your decision. 

Trust us; you won’t want to miss this!

Understanding The Career Path of a Data Scientist and a Machine Learning Engineer

Data science stems from the field of analytics and focuses on making sense of large amounts of data.

A data scientist analyzes data and finds patterns and insights to help a company make better decisions.

Data scientists typically use statistical methods, data visualization tools, and programming languages like Python and R to complete the job. 

While coding is a part of the job, it’s usually less prominent than data analytics work.

On the other hand, machine learning stems from the field of software engineering.

While machine learning engineers and data scientists both build these algorithms, machine learning engineers will be coding much more than data scientists. 

Machine learning engineers focus on implementing these algorithms and building systems that allow these algorithms to flourish.

While analytics is still a part of the job, due to the software engineering branch, machine learning engineers spend much less time analyzing data.

Are Data Science and Machine Learning The Same Thing?

While data science and machine learning might seem similar, they are actually two distinct fields.

Both fields revolve around building models and making sense of data, but the focus and approach differ.

Data science is closer to the optimization branch of mathematics, where the goal is to make slight improvements to already-built systems.

Data scientists use statistical methods and visualization tools to analyze data and find insights to help companies make better decisions.

They might also build predictive models, but the focus is on finding the best solution within the constraints of the existing system.

On the other hand, machine learning is a software engineering job focused on building the systems themselves.

Machine learning engineers use programming languages like Python and R to write code to build algorithms and systems that can foster these algorithms. 

The goal is to build models that can be used for various tasks, such as image recognition and natural language processing.

While data science and machine learning might seem similar, they are very different regarding day-to-day work.

Building a system and monitoring a system are two very different things.

As a data scientist, you will spend more time analyzing data and finding insights into pre-built systems. 

As a machine learning engineer, you’ll spend more time writing code and building these systems.

How To Pick Between Learning Data Science or Machine Learning First

When it comes to choosing between learning data science and machine learning first, the answer is pretty simple.

The most critical factor in choosing is figuring out what you enjoy doing. 

If you enjoy analyzing data and finding patterns, then data science might be your “perfect” choice. 

Also, those with strong statistical and mathematical backgrounds quickly learn data science.

While working as a senior data scientist, most of my team came from academics with Ph. Ds in physics, astronomy, and computer science.

This makes sense as you’ll use statistical methods to analyze data and find insights to help companies make better decisions – things learned in masters and Ph.D. programs.

The transition into a career as a data scientist will be much more fluid, as you already know you enjoy this type of thing, making the end goal much easier to achieve. 

If you have a passion for building things and have a system-oriented mindset, then pursuing machine learning first might be the right choice.

This is an excellent path for those from a software engineering-type role who has been writing code and feel confident in their coding abilities.

Machine learning engineers build algorithms that allow computers to learn from data and the skills you’ve previously learned while coding will directly apply to your work.

You’ll use programming languages like Python and R to write code and build models that can be used for various tasks, such as image recognition and natural language processing.

What Would I Do If I Have No Experience In Either?

If you have no experience in either data science or machine learning, it might be a good idea to start by targeting a career in data science.

This approach has been successful for many people who have transitioned into the field.

By teaching yourself to code and securing a data science role, you’ll gain valuable experience and build a foundation that you can use to transition into a machine learning role later on.

We suggest starting with data science first because you can get a job in about half the time it takes to get a machine learning engineer role. 

While it might take 18 months or more to gain the necessary experience and skills to get a machine learning engineer role, you can get to work as a data scientist in as little as nine months. 

This allows you to start your career and earn money sooner while you continue to build your skills and gain experience.

Once you have gained confidence in your coding abilities and built a strong data science foundation, you can leverage that experience to transition into a machine learning engineer role.

By starting with data science, you’ll gain a deeper understanding of the field and be better equipped to make the transition later on.

Should I Just Learn Both?

While it may seem like a good idea to learn data science and machine learning, it’s better to focus on one area and become an expert in it.

Careers are better with expertise, and by focusing on one area, you can develop a deep understanding of the field and become an expert in it.

You may have to learn about both of them initially to figure out which one you enjoy more, but once you’ve decided, diving deep and focusing on one area is essential. 

By doing so, you’ll develop a deeper understanding of the field and be better equipped to make a real impact.

And honestly, people pay more $$$ for expertise and experience.