Machine Learning Operations (MLOps)

Master the art of deploying, monitoring, and managing machine learning models in production.

Program Overview

Our MLOps program is designed for aspiring and experienced professionals looking to bridge the gap between machine learning development and production deployment. In today's rapidly evolving AI landscape, the ability to reliably and efficiently deliver ML models into real-world applications is paramount. This program covers the essential tools, techniques, and best practices for building robust MLOps pipelines.

Key Learning Objectives:

  • Understand the MLOps lifecycle and its importance.
  • Learn about CI/CD principles applied to ML.
  • Master containerization with Docker and orchestration with Kubernetes.
  • Explore model versioning, experiment tracking, and data drift detection.
  • Implement strategies for model monitoring, retraining, and governance.
  • Gain hands-on experience with popular MLOps platforms and tools.
  • Develop skills in cloud-native ML deployment (AWS SageMaker, Azure ML, GCP AI Platform).

Core Curriculum Modules

Foundations of MLOps

Introduction to MLOps, its principles, and the challenges in deploying ML models.

CI/CD for Machine Learning

Automating the build, test, and deployment phases for ML workflows.

Containerization & Orchestration

Using Docker for packaging models and Kubernetes for scaling and management.

Model Management & Versioning

Tracking experiments, managing model artifacts, and ensuring reproducibility.

Monitoring & Observability

Setting up systems to monitor model performance, data drift, and system health.

Cloud ML Platforms

Leveraging managed services from major cloud providers for MLOps.

Who Should Enroll?

This program is ideal for:

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