MLOps Training and Certification in Yangon, Myanmar

This training provides an in-depth understanding of MLOps (Machine Learning Operations), an essential discipline that integrates machine learning model development and operational workflows. Participants will explore the lifecycle of machine learning models, including deployment, monitoring, scaling, and governance. Hands-on sessions focus on leveraging tools such as Docker, Kubernetes, Git, CI/CD pipelines, and cloud platforms for automating and managing ML workflows. By the end of the course, participants will gain practical skills to implement robust and scalable MLOps practices.

Introduction to MLOps and ML Lifecycle

• Session 1: Understanding MLOps

  • What is MLOps?
  • Benefits of MLOps in ML workflows.
  • Overview of the ML lifecycle and challenges in production.

• Session 2: Tools and Technologies for MLOps

  • Key tools: Git, Docker, Kubernetes, Jenkins, and MLflow.
  • Overview of cloud platforms for MLOps (e.g., AWS, Azure, GCP).

• Session 3: Data Preparation and Feature Engineering

  • Automating data preprocessing workflows.
  • Feature versioning and management strategies.

Model Deployment and CI/CD Pipelines

• Session 4: Containerization for Machine Learning Models

  • Introduction to Docker for ML workflows.
  • Building and managing Docker images for ML models.

• Session 5: Continuous Integration and Deployment

  • Understanding CI/CD principles for MLOps.
  • Designing and implementing CI/CD pipelines.

• Session 6: Orchestration with Kubernetes

  • Introduction to Kubernetes for scaling ML models.
  • Deploying and managing ML models in Kubernetes clusters.

Monitoring, Retraining, and Governance

• Session 7: Monitoring and Managing Deployed Models

  • Setting up model monitoring frameworks (e.g., Prometheus, Grafana).
  • Detecting model drift and performance degradation.

• Session 8: Retraining and Model Governance

  • Automating model retraining workflows.
  • Versioning models with MLflow or DVC.
  • Governance practices for ML pipelines.

• Session 9: Course Wrap-Up

  • Q&A session and best practices for implementing MLOps.

This MLOps training equips participants with the skills and tools needed to operationalize machine learning workflows effectively. With a blend of theoretical insights and hands-on exercises, this training ensures that participants are ready to manage and scale ML models in real-world production environments.