MLOps

Machine learning operations (MLOps) is a set of practices that help manage the machine learning (ML) life cycle. It involves automating and standardizing processes for developing, testing, deploying, and maintaining ML models. The goal of MLOps is to ensure that ML models are reliable, scalable, and maintainable.?

Principles of MLOps

Version control

This process involves tracking changes in the machine learning assets so you can reproduce results and roll back to previous versions if necessary. Every ML training code or model specification goes through a code review phase. Each is versioned to make the training of ML models reproducible and auditable.

Reproducibility in an ML workflow is important at every phase, from data processing to ML model deployment. It means that each phase should produce identical results given the same input.

Automation

Automate various stages in the machine learning pipeline to ensure repeatability, consistency, and scalability. This includes stages from data ingestion, preprocessing, model training, and validation to deployment.

These are some factors that can trigger automated model training and deployment:

• Messaging
• Monitoring or calendar events
• Data changes
• Model training code changes
• Application code changes.

Automated testing helps you discover problems early for fast error fixes and learnings. Automation is more efficient with infrastructure as code (IaC). You can use tools to define and manage infrastructure. This helps ensure its reproducible and can be consistently deployed across various environments.

Continuous X

Through automation, you can continuously run tests and deploy code across your ML pipeline.

In MLOps, continuous refers to four activities that happen continuously if any change is made anywhere in the system:

• Continuous integration extends the validation and testing of code to data and models in the pipeline
• Continuous delivery automatically deploys the newly trained model or model prediction service
• Continuous training automatically retrains ML models for redeployment
• Continuous monitoring concerns data monitoring and model monitoring using metrics related to business

Model governance

Governance involves managing all aspects of ML systems for efficiency. You should do many activities for governance:

• Foster close collaboration between data scientists, engineers, and business stakeholders
• Use clear documentation and effective communication channels to ensure everyone is aligned
• Establish mechanisms to collect feedback about model predictions and retrain models further
• Ensure that sensitive data is protected, access to models and infrastructure is secure, and compliance requirements are met

It’s also essential to have a structured process to review, validate, and approve models before they go live. This can involve checking for fairness, bias, and ethical considerations.

Benefits of MLOps

Machine learning helps organizations analyze data and derive insights for decision-making. However, it's an innovative and experimental field that comes with its own set of challenges. Sensitive data protection, small budgets, skills shortages, and continuously evolving technology limit a project's success. Without control and guidance, costs may spiral, and data science teams may not achieve their desired outcomes.

MLOps provides a map to guide ML projects toward success, no matter the constraints. Here are some key benefits of MLOps.

Faster time to market

MLOps provides your organization with a framework to achieve your data science goals more quickly and efficiently. Your developers and managers can become more strategic and agile in model management. ML engineers can provision infrastructure through declarative configuration files to get projects started more smoothly.

Automating model creation and deployment results in faster go-to-market times with lower operational costs. Data scientists can rapidly explore an organization's data to deliver more business value to all.

Improved productivity

MLOps practices boost productivity and accelerate the development of ML models. For instance, you can standardize the development or experiment environment. Then, your ML engineers can launch new projects, rotate between projects, and reuse ML models across applications. They can create repeatable processes for rapid experimentation and model training. Software engineering teams can collaborate and coordinate through the ML software development lifecycle for greater efficiency.

Efficient model deployment

MLOps improves troubleshooting and model management in production. For instance, software engineers can monitor model performance and reproduce behavior for troubleshooting. They can track and centrally manage model versions and pick and choose the right one for different business use cases.

When you integrate model workflows with continuous integration and continuous delivery (CI/CD) pipelines, you limit performance degradation and maintain quality for your model. This is true even after upgrades and model tuning.