Full Stack AI ML Development Courses

This document provides the curriculum outline of the Knowledge, Skills and Abilities that a Machine Learning Developer and DevOps/MLOps Administrator can be expected to demonstrate.



  • Python Programming Fundamentals.
  • Familiarity with Command Line Interface (CLI) and Git/GitHub, DVC
  • Fundamental Knowledge of Django, Database, HTML/CSS.
  • Familiarity with at least machine learning data preprocessing, YAML, JSON.


Out Come:

After attending this training, the trainees will be gaining the below skills on Full Stack AI/ML Model Design, Development & DevOps/MLOps Orchestration.


  • ML/MLOps Architecture Vs DevOps Framework.
  • Install/Configure Cookie Cutter Template and DVC (Data Tracking).
  • Build a Custom Environment for ML/MLOps model design and retraining.
  • Manage DevOps/MLOps Lifecycle, Storage, and CI/CD Pipeline.
  • Installing and Configuring Jenkins and Nagios.
  • Installing and Configuring MLFlow, PyTest, and Pep8.
  • Deploying and scaling the model Django & Haruko Platform.
  • Data Storage using PostgreSQL.
  • Auditing and Troubleshooting Machine Learning Model.
  • DevOps/MLOPs best Security Practices.
  • Complete the Life Cycle of AI/ML Software development using various technologies.
  • ML Model in Amazon SageMaker.
  • Auto Implement on DataRobot.


Local setup (Physical Mode)

Remote Lab Setup (Optional)



Laptop/Desktop with high-speed

OS: Windows 10 and Above

internet connection, Windows 10 and




Memory: 4 GB RAM

Memory: 32 GB RAM

CPU: 1 CPU Cores

CPU: 8 CPU Cores

Storage: 20 GB

Storage: 500 GB SSD



Part – 1


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, and GitOps?


Introduction to ML and MLOps stages:

  • What are the various stages in the ML lifecycle?                                                                                                             
  • Detailed MLOps Principles and stages                                                                                                                
  1. Testing
  2. Automation (CI/CD)
  3. Reproducibility
  4. Deployment
  5. Monitoring 
  6. Versioning                                                                                                                                                                     
  • MLOps Architectures:                                                                                                                                           
  1. Architectures \w Open Source tools
  2. Architectures \w cloud Native tools – Amazon Web Services 
  3. Comparison among cloud native tools                                                          
  4. 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/Github:

  • 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 Using Jenkins:

  • Introduction to CI/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 Cloud Build cloud formationion



Cloud Native CI/CD Tools 101:

  • Jenkins on AWS OR Cloud Build or cloud formation



  1. Data Set from Kaggle is considered to demonstrate the real time Machine Learning Model Design and Development. (Regression Model)
  2. ML Model will be retraining with an industry use case showing how CI/CD on an ML Model using DVC.
  3. Workshop on CloudBuild with an industry use case showing how CI and CD on an ML Model AWS
  4. Model Testing using Pytest and Tox Dependencies.


Kubernetes/Docker Overview

  • Kubernetes overview                                                                                                                                                     
  • Kubernetes Architecture o Nodes

o Control Plane o API Server


  • Kubernetes Resources o Pod

o Deployment o Replica

o  Service

o  Volumes (PVC)


  • Kubernetes Deployment Strategy o Monitoring

o  Liveness and Readiness Probes


  • Labels and Selectors
  • Docker Installation and Deployment.



  1. Kubernetes kubectl command practice
  2. Practice core concepts like Deploying Pod, deployment, ReplicaSet, Namespaces, imperative commands.
  3. Use YAML construct for declarative commands
  4. Create and Deploy ML pipeline on Kubernetes


Part – 2


Introduction to Model Management

  • What is a Model Management?
  • What are the various activities in Model Management?
  • High-level overview of below Model Management tools o MLFlow

o  DVC


Cloud ML Services

  • What is MLFlow
  • Various components of MLFlow Services
  • Benefits of using MLFlow Services



  • Building ML pipeline in Github Action.
  • Deploying model into Github (Action).
  • Monitoring Model Performance using Nagios/Heruko/AWS ElasticBeans.



Introduction to Django, PostgreSQL and Model Deployment using Heruko

  • Why Django and PostgreSQL is important?
  • What are the various types of front end design related to machine learning model?
  • Architecture of Django using Python Programming.
  • User Data Storage using PostgreSQL Database.
  • Model Deployment using Heruko Server



  • Building front end Graphical User Interface using Django, HTML/CSS
  • Data Storage using PostgreSQL Database for future retraining of ML Model.


Introduction to Model Monitoring

  • Why monitoring is important?
  • What are the various types of monitoring related to machine learning model?
  • Architecture of monitoring ecosystem in Nagios
  • Various monitoring tools on Local Machine/Cloud Platform.



  • Building a drift monitoring system on Nagios.


Introduction to AutoML tools

  • H20
  • Dataiku
  • Domino
  • Datarobot

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