In the fast-paced world of technology, two fields are currently blowing up.
These two roles: MLOps and Data Engineering, are crucial in transforming how businesses leverage data.
While one carves a path toward the seamless integration (seemingly impossible) and management of Machine Learning models, the other lays the robust foundation of Big Data architecture that fuels innovation.
But which one is the right path for you?
Is it the new and exciting world of MLOps, where models are built from experimental repos to production pipelines, constantly adapting to ever-changing regulations and customer needs?
Or is it Data Engineering, where data’s raw potential is harnessed into organized, accessible, and valuable?
This blog post will explore MLOps and Data Engineering, breaking down what they are and why they matter.
We’ll look at how much you might earn in these fields, what the jobs are like, and what makes them different.
This information will help you determine the best fit for your interests and career goals.
So, if you’re already working in technology or just curious about these exciting areas, come along with us. We’ll help you learn about two important jobs in our world of data and technology. By the end, you might know which matches you best!
** Note: I currently work in MLOPS so I may be slightly biased. **
What is Data Engineering?
Data Engineering is collecting, cleaning, and organizing large datasets. It encompasses creating and maintaining architectures, such as databases and large-scale processing systems, and data transformation and analysis tools.
Data engineers build the infrastructure for data generation, transformation, and modeling.
Realize that scale is behind everything data engineers do, focusing primarily on data availability at scale.
Why is Data Engineering Important?
Data Engineering is vital for any organization that relies on data for decision-making. It enables:
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.
What is MLOps?
MLOps, short for Machine Learning Operations, is a set of practices that unifies machine learning (ML) system development and operations. It aims to automate and streamline the end-to-end ML lifecycle, covering everything from data preparation and model training to deployment and monitoring. MLOps helps maintain the ML models’ consistency, repeatability, and reliability.
What is commonly missed about MLOps is the CI/CD portion of the job. Correct builds, versioning, docker, runners, etc., make up a significant portion of the Machine learning engineers’ day-to-day work.
Why MLOps?
MLOps is critical in modern business environments for several reasons (besides feeding my family):
Streamlining The ML Workflow
MLOps helps different people in a company work together more smoothly on machine learning (ML) projects.
Think of it like a well-organized team sport where everyone knows their role:
- Data Scientists: The players who develop strategies (ML models) to win the game.
- Operations Teams: The coaches and support staff ensure everything runs smoothly.
- MLOps: The rules and game plan that help everyone work together efficiently so the team can quickly score (deploy models).
Maintaining Model Quality
ML models need to keep working well even when things change. MLOps does this by:
- Watching Constantly: Like a referee keeping an eye on the game, MLOps tools continuously check that the models are performing as they should.
- Retraining When Needed: If a model starts to slip, MLOps helps to “coach” it back into shape by using new data and techniques so it stays solid and valuable.
Regulatory Compliance
Just like there are rules in sports, there are laws and regulations in business. MLOps helps ensure that ML models follow these rules:
- Keeping Records: MLOps tools track what has been done, like a detailed scorecard. This ensures that the company can show they’ve followed all the necessary rules if anyone asks.
- Checking Everything: Like a referee inspecting the equipment before a game, MLOps ensures everything is done correctly and fairly.
Enhancing Agility
In sports, agility helps players respond quickly to changes in the game. MLOps does something similar for businesses:
- Quick Changes: If something in the market changes, MLOps helps the company to adjust its ML models quickly, like a team changing its game plan at halftime.
- Staying Ahead: This ability to adapt helps the business stay ahead of competitors, just like agility on the field helps win games.
So, in simple terms, MLOps is like the rules, coaching, refereeing, and agility training for the game of machine learning in a business. It helps everyone work together, keeps the “players” (models) at their best, makes sure all the rules are followed and helps the “team” (company) adapt quickly to win in the market.
Famous MLOps Tools
Docker (The KING of MLops):
About: Docker is a platform for developing, shipping, and running container applications.
Use in MLOps:
Containerization: Docker allows data scientists and engineers to package an application with all its dependencies and libraries into a “container.” This ensures that the application runs the same way, regardless of where the container is deployed, leading to consistency across development, testing, and production environments.
Scalability: In an MLOps context, Docker can be used to scale ML models easily. If a particular model becomes popular and needs to handle more requests, Docker containers can be replicated to handle the increased load.
Integration with Orchestration Tools: Docker can be used with orchestration tools like Kubernetes to manage the deployment and scaling of containerized ML models. This orchestration allows for automated deployment, scaling, and management of containerized applications.
Collaboration: Docker containers encapsulate all dependencies, ensuring that all team members, including data scientists, developers, and operations, work in the same environment. This promotes collaboration and reduces the “it works on my machine” problem.
Version Control: Containers can be versioned, enabling easy rollback to previous versions and ensuring that the correct version of a model is deployed in production.
Docker has become an essential part of the MLOps toolkit because it allows for a seamless transition from development to production, enhances collaboration, and supports scalable and consistent deployment of machine learning models.
MLflow
About: MLflow is an open-source platform designed to manage the ML lifecycle.
Use: It includes tools for tracking experiments, packaging code into reproducible runs, and sharing and deploying models.
Kubeflow
About: Kubeflow is an open-source Kubernetes-native platform for developing, orchestrating, deploying, and running scalable and portable ML workloads.
Use: It’s designed to make deploying scalable ML workflows on Kubernetes simple, portable, and scalable.
TensorFlow Extended (TFX)
About: TensorFlow Extended is a production ML platform based on TensorFlow.
Use: It provides a configuration framework and shared libraries to integrate common components needed to define, launch, and monitor system-managed ML workflows.
DVC (Data Version Control)
About: DVC is an open-source version control system for ML projects.
Use: It helps track and manage data, models, and experiments, making it easier to reproduce and collaborate on projects.
Seldon Core
About: Seldon Core is an open-source platform for deploying, scaling, and monitoring machine learning models in Kubernetes.
Use: It allows for the seamless deployment of ML models in a scalable and flexible manner.
Metaflow
About: Developed by Netflix, Metaflow is a human-centric framework for data science.
Use: It helps data scientists manage real-life data and integrates with existing ML libraries to provide a unified end-to-end workflow.
Pachyderm
About: Pachyderm is a data versioning, data lineage, and data pipeline system built on Go.
Use: It allows users to version their data and models, making the entire data lineage reproducible and explainable.
Neptune.ai
About: Neptune.ai is a metadata store for MLOps, centralizing all metadata and results.
Use: It’s used for experiment tracking and model registry, allowing teams to compare experiments and collaborate more effectively.
Allegro AI
About: Allegro AI offers tools to manage the entire ML lifecycle.
Use: It helps in dataset management, experiment tracking, and production monitoring, simplifying complex ML processes.
Hydra
About: Hydra is an open-source framework for elegantly configuring complex applications.
Use: It can be used in MLOps to create configurable and reproducible experiment pipelines and manage resources across multiple environments.
These tools collectively provide comprehensive capabilities to handle various aspects of MLOps, such as model development, deployment, monitoring, collaboration, and compliance.
By integrating these tools, organizations can streamline their ML workflows, maintain model quality, ensure regulatory compliance, and enhance overall agility in their ML operations.
Which Career Path Makes More?
According to Glassdoor, the average MLOps engineer will bring home about $125,000 yearly.
Comparing this to the average data engineer, who will bring home about $115,000 annually.
While the MLOps engineer will bring home, on average, about $10,000 more a year – In my honest opinion, it’s not enough money to justify choosing one over the other.
Sources:
https://www.glassdoor.com/Salaries/mlops-engineer-salary-SRCH_KO0,14.htm
https://www.glassdoor.com/Salaries/data-engineer-salary-SRCH_KO0,13.htm
Which Career Is Better?
Hear me out, the answer is MLOps.
Just kidding (kind of).
Both of these careers – MLOps and Data Engineering – are stimulating, growing Year-over-Year (YoY), and technologically fulfilling.
But let’s dive a little deeper:
Stimulating Work
MLOps: The dynamic field of MLOps keeps you on your toes. From managing complex machine learning models to ensuring they run smoothly in production, there’s never a dull moment. It combines technology, creativity, and problem-solving, providing endless intellectual stimulation.
Data Engineering: Data Engineering is equally engaging. Imagine being the architect behind vast data landscapes, designing structures that make sense of petabytes of information, and transforming raw data into insightful nuggets. It’s a puzzle waiting to be solved; only the most creative minds need to apply.
Growing YoY
MLOps: With machine learning at the core of modern business innovation, MLOps has seen significant growth. Organizations are realizing the value of operationalizing ML models, and the demand for skilled MLOps professionals is skyrocketing.
Data Engineering: Data is often dubbed “the new oil,” and it’s not hard to see why. As companies collect more and more data, they need experts to handle, process, and interpret it. Data Engineering has become a cornerstone of this data revolution, and the field continues to expand yearly.
Technologically Fulfilling
MLOps: Working in MLOps means being at the cutting edge of technology. Whether deploying a state-of-the-art deep learning model or optimizing a system for real-time predictions, MLOps offers a chance to work with the latest and greatest tech.
Data Engineering: Data Engineers also revel in technology. From building scalable data pipelines to employing advanced analytics tools, they use technology to drive insights and create value. It’s a role that marries technology with practical business needs in a deeply fulfilling way.
It’s hard to definitively say whether MLOps or Data Engineering is the “better” field. Both are thrilling, expanding and provide a chance to work with state-of-the-art technology. The choice between them might come down to personal interests and career goals.
(pick MLOps)
Some Other CI/CD Articles
Here at enjoymachinelearning.com we have a few other in-depth articles about CI/CD.
Here are a few of those:
