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MLOps Training

This MLOps (Machine Learning Operations) course is designed to equip learners with the skills to effectively deploy, monitor, and manage machine learning models in production environments. The course bridges the gap between data science and software engineering by introducing best practices and tools that automate the end-to-end ML lifecycle.

Participants will learn about

  • The fundamentals of MLOps and its importance in scalable ML deployments
  • Key components of the MLOps lifecycle including data versioning, model training, and deployment
  • Tools and technologies such as Docker, Kubernetes, MLflow, and CI/CD pipelines
  • Techniques for monitoring model performance, detecting drift, and managing model retraining
  • Cloud-based MLOps solutions on platforms like AWS, Azure, and Google Cloud

Through hands-on projects and labs, learners will gain practical experience in building automated ML pipelines, deploying models at scale, and maintaining them with continuous integration and monitoring strategies.

This course is ideal for data scientists, ML engineers, DevOps professionals, and anyone looking to specialize in the operational aspects of machine learning systems.

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MLOps Course Learning Outcomes

By the end of this course, learners will be able to:

Understand the MLOps lifecycle and its role in bridging machine learning development and operations.

Implement version control for datasets, code, and machine learning models to ensure reproducibility.

Design and build scalable, containerized ML applications using Docker and Kubernetes.

Ensure security, compliance, and governance in ML model deployment workflows.

Create automated CI/CD pipelines to streamline model training, testing, deployment, and monitoring.

Deploy machine learning models in production environments across cloud platforms like AWS, Azure, or GCP.

Monitor model performance effectively using metrics and alerting systems to detect data and model drift.

Manage model retraining and updates automatically to maintain model accuracy over time.

Apply best practices for collaboration between data scientists, DevOps engineers, and stakeholders.

Utilize popular MLOps tools such as MLflow, Kubeflow, TensorFlow Serving, and others to build end-to-end solutions.

MLOps Course Tools

1. Version Control & Experiment Tracking
  • Git — Code versioning and collaboration
  • MLflow — Experiment tracking, model registry, and lifecycle management
  • DVC (Data Version Control) — Data and model versioning
  • Docker — Containerizing ML applications for consistent environments
  • Kubernetes — Container orchestration and scalable deployment
  • Kubeflow — Kubernetes-native platform for deploying, monitoring, and managing ML workflows
  • Jenkins — Automating builds, tests, and deployments
  • GitHub Actions — CI/CD workflows integrated with GitHub repositories
  • Apache Airflow — Workflow orchestration and scheduling for ML pipelines
  • TensorFlow Serving — Serving TensorFlow models in production
  • Seldon Core — Deploying, scaling, and managing ML models on Kubernetes
  • AWS SageMaker — End-to-end managed service for building, training, and deploying ML models
  • Prometheus — Monitoring system and time series database
  • Grafana — Visualization dashboard for monitoring metrics
  • ELK Stack (Elasticsearch, Logstash, Kibana) — Logging and analysis
  • AWS (Amazon Web Services)
  • Microsoft Azure
  • Google Cloud Platform (GCP)

MLOps Course Content

Module 1: Introduction to MLOps
  • What is MLOps?
  • The ML lifecycle vs. Software Development lifecycle
  • Importance and benefits of MLOps
  • Key challenges in ML deployment
  • Importance of version control in MLOps
  • Introduction to Git for code versioning
  • Data versioning with DVC and LakeFS
  • Experiment tracking with MLflow
  • Introduction to Docker
  • Building Docker images for ML models
  • Managing dependencies and environments
  • Introduction to Kubernetes basics
  • Automating model training pipelines
  • Hyperparameter tuning and experiment management
  • Tools for experimentation tracking
  • Concepts of CI/CD in MLOps
  • Building CI/CD pipelines using Jenkins/GitHub Actions
  • Automating testing and validation of ML models
  • Deploying models using CI/CD pipelines
  • Serving ML models with TensorFlow Serving, FastAPI, or Flask
  • Scalable deployment using Kubernetes and Kubeflow
  • Cloud-based model deployment (AWS SageMaker, Azure ML, GCP AI Platform)
  • Monitoring model performance and data drift
  • Setting up alerts and logging (Prometheus, Grafana)
  • Model retraining and rollback strategies
  • Securing ML pipelines and data
  • Ensuring compliance with industry regulations
  • Governance best practices in MLOps
  • Explainability and fairness in ML models
  • A/B testing and canary deployments
  • MLOps for deep learning and large-scale systems
  • Build and deploy an end-to-end MLOps pipeline
  • Automate model training, deployment, and monitoring
  • Showcase best practices learned throughout the course

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