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( Duration: 5 Days )

This MLOps training course covers how to create ML pipelines, model deployment, monitor the performance in production, and adopt best practices from DevOps.

MLOps stands for machine learning operations. The term MLOps is derived from DevOps (Development Operations). It is used to streamline the machine learning process from development to deployment. The MLOps include training machine learning models, experiment tracking, model optimization, creating ML pipelines, saving and serving models, and monitoring and maintaining models in production. In short, you are automating all the processes from development to deployment, and you are constantly monitoring the logs, metrics, and performance.

Delegates attending this MLOps workshop, should have:

  • Experience with Python, Docker, command line
  • A deep understanding of machine learning models



Introduction to MLOps

  • What is MLOps?
  • Why we need MLOps and business impact?
  • Machine learning industrialization challenges
  • How does it relate to DevOps, AIOps, ModelOps, GitOps?

Introduction to ML and MLOps stages

  • What are the various stages in ML lifecycle
  • Detailed MLOps Principles and stages
    • Versioning
    • Testing
    • Automation (CI/CD)
    • Reproducibility
    • Deployment
    • Monitoring
  • MLOps Architectures
    • Architectures \w Open Source tools
    • Architectures \w cloud Native tools - AWS, GCP and Azure
    • Comparison among cloud native tools
    • Cost-benefit approach of each architecture and MLOps maturity
  • List of tools involved in each stages (MLOps tool ecosystem)
  • MLOps Maturity Model
  • Team ownership types in various stages of MLOps

Introduction to Git

  • Overview of Git
  • Understanding branching strategies and REPO
  • Standard GIT branching strategies(development,feature, bug, release, UAT)
  • Practicing important Git commands
  • Github Action overview and working

Introduction to CI/CD

  • Introduction to CI and CD
  • CI/CD challenges in Machine Learning
  • Steps involved in the CI/CD implementation in ML lifecycle and workflow
  • Glimpse of popular Tools used in the DevOps ecosystem on 1 cloud – e.g. AzureDevOps or CloudBuild or cloudformation

Cloud Native CI/CD Tools

  • AzureDevOps OR Cloud Build or cloud formation

Kubernetes Overview

  • Kubernetes overview
  • Kubernetes Architecture
    • Nodes
    • Control Plane
    • API Server
  • Kubernetes Resources
    • Pod
    • Deployment
    • Replica
    • Service
    • Volumes (PVC)
  • Kubernetes Deployment Strategy
    • Monitoring
    • Liveness and Readiness Probes
    • Labels and Selectors

Introduction to Model Management

  • What is a Model Management
  • What are the various activities in Model Management
  • Highlevel overview of below Model Management tools
    • MLFlow
    • DVC

Cloud ML Services

  • What is VertexAI / AzureML Services/Sagemaker
  • Various components of VertexAI/AzureML Services
  • Benefits of using VertexAI/AzureML Services

Introduction to Model Monitoring

  • Why monitoring is important?
  • What are the various types of monitoring related to model
  • Architecture of monitoring ecosystem in Azure/GCP
  • Various monitoring tools on cloud

Introduction to automl tools

  • H20
  • Dataiku
  • Domino
  • Datarobot

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 6,000 various courses on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

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Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.