MLOps

MLOps is the discipline of applying DevOps ideas to ML systems. It facilitates the creation and deployment of ML models in large data science projects. In larger initiatives, operational elements require just as much brainpower to put those models into prod as the dev elements.

• MLOps engineers?deploy,?manage, and?optimize?ML models in production environments, ensuring smooth integration and efficient operations.?
• MLOps Engineers take a data scientist’s model and?make it accessible?to the software that utilizes it.?

MLOps is the discipline of applying DevOps ideas to ML systems. It facilitates the creation and deployment of ML models in large data science projects. In larger initiatives, operational elements require just as much brainpower to put those models into prod as the dev elements.

• MLOps engineers?deploy,?manage, and?optimize?ML models in production environments, ensuring smooth integration and efficient operations.?
• MLOps Engineers take a data scientist’s model and?make it accessible?to the software that utilizes it.?

So, on a very broader scale, a BAU for an MLOps Engineer would be something like:

• Checking deployment pipelines for ML models.
• Review Code changes and pull requests from the data science team.
• Triggers CI/CD pipelines after code approvals.
• Monitors pipelines and ensures all tests pass and model artifacts are generated/stored correctly.
• Deploys updated models to prod after pipeline completion.
• Works closely with the software engineering and DevOps team to ensure smooth integration.
• Containerize models using Docker and deploy on cloud platforms (like AWS/GCP/Azure).
• Set up monitoring tools to track various metrics like response time, error rates, and resource utilization.
• Establish alerts and notifications to quickly detect anomalies or deviations from expected behavior.
• Analyze monitoring data, log, files, and system metrics.
• Collaborate with the data science team to develop updated pipelines to cover any faults.
• Documenting and troubleshoots, changes, and optimization.

This brings us to how MLOps engineers are different from Data Scientists, Software Engineers, Data Engineers, and ML Engineers.?

MLOps Engineers vs. Data Scientists?

Data Scientists specialize in finding and applying the optimum machine learning model to handle business challenges. They experiment with different algorithms, fine-tune their hyperparameters, and then assess and corroborate their results using a range of standards.?

While Data Scientists drive the development of ML models, MLOps Engineers enable their deployment, integration, and ongoing management, bridging the gap between data science and operations to ensure the efficient and effective use of ML models in real-world applications.

MLOps Engineers vs. Software Engineers

Software Engineers focus on access control, use data gathering, cross-platform integration, and hosting, encompassing various aspects such as architecture, coding, testing, and debugging.?

While Software Engineers handle the broader software development lifecycle, MLOps Engineers bring their expertise in machine learning and operations to effectively deploy and manage ML models within software systems.

MLOps vs. Data Engineers

MLOps Engineers primarily focus on the deployment, management, and monitoring of ML models in production, bridging the gap between data science and operations.

On the other hand, Data Engineers specialize in designing, building, and maintaining data pipelines and infrastructure for efficient and reliable data processing and storage.?

While there is overlap in some areas, MLOps Engineers concentrate on ML model deployment and management, while Data Engineers focus on data infrastructure and pipeline development.

MLOps Engineers vs. ML Engineers

ML Engineers enable model deployment automation to production systems. The amount of automation varies within the organization. They take a data scientist’s model and make it accessible to the software that utilizes it.?

Machine learning models are commonly built, tested, and validated using Jupyter notebooks or script files. However, software developers want machine learning models to be available through callable APIs like REST.

ML engineers may sit on platform teams as well as individual ML dev teams depending on the size of the company and the requirements of their ML models.?