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MLOps (Machine Learning Operations) Training

Live Online & Classroom Enterprise Training

MLOps (Machine Learning Operations) focuses on streamlining and automating the deployment, monitoring, and management of machine learning models in production environments.

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What is MLOps (Machine Learning Operations) Course about?

MLOps (Machine Learning Operations) is a practical course that bridges the gap between Data Science and IT Operations, focusing on the deployment, monitoring, and lifecycle management of machine learning (ML) models in production environments. This course introduces industrystandard tools and best practices to automate ML workflows, enable continuous integration/continuous deployment (CI/CD), and ensure model reproducibility, scalability, and reliability.

What are the objectives of MLOps (Machine Learning Operations) Course ?

  • Design and implement end-to-end MLOps pipelines
  • Automate model training, testing, and deployment
  • Integrate version control, model tracking, and experiment management
  • Deploy ML models as microservices using Docker and Kubernetes
  • Monitor deployed models for drift, accuracy, and performance
  • Use tools like MLflow, Kubeflow, TensorFlow Serving, DVC, and Airflow
  • Apply CI/CD practices to ML systems

Who is MLOps (Machine Learning Operations) Course for?

  • Machine Learning Engineers
  • Data Scientists transitioning into production roles
  • DevOps Engineers working with ML teams
  • Software Engineers integrating AI into applications
  • AI/ML Product Managers and Tech Leads

What are the prerequisites for MLOps (Machine Learning Operations) Course?

  • Understanding of Machine Learning concepts (model training, evaluation, etc.)
  • Basic knowledge of Python
  • Familiarity with Git, Docker, and CI/CD pipelines is a plus
  • Experience with cloud platforms is helpful but not mandatory

Available Training Modes

Live Online Training

5 Days

Self-Paced Training

45 Hours

Course Outline Expand All

Expand All

  • What is MLOps?
  • The ML lifecycle: from experimentation to production
  • Why DevOps practices are essential for ML
  • MLflow and DVC for experiment tracking
  • Reproducibility and model versioning
  • Dataset management
  • Serving models via Flask and FastAPI
  • Dockerizing ML models
  • Kubernetes for orchestration
  • GitHub Actions, Jenkins, and GitLab CI/CD
  • Building automated ML pipelines
  • Triggering retraining and redeployment
  • Monitoring models in production
  • Tools for model drift and performance metrics
  • Logging with Prometheus, Grafana, and ELK Stack
  • Using Kubeflow Pipelines and Apache Airflow
  • Scheduling and managing retraining workflows
  • Distributed training and hyperparameter tuning
  • Overview of AWS SageMaker, Azure ML, and Google Vertex AI
  • Model registry, endpoints, and serverless deployment
  • Cost, security, and scaling considerations
  • Build a full MLOps pipeline
  • Deploy and monitor a real-world model
  • Presentation and peer review

Who is the instructor for this training?

The trainer for this MLOps (Machine Learning Operations) Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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