Full Stack AI/ML Development Courses

Last Updated : May 27, 2024
59 Lessons
0 Enrolled

About Course

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.


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

What Will You Learn?

  • 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.

Course Content

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:

Introduction to Git/Github:

Introduction to CI/CD Using Jenkins:

Cloud Native CI/CD Tools 101:

Kubernetes/Docker Overview

Part 2- Introduction to Model Management

Cloud ML Services

Introduction to Django, PostgreSQL and Model Deployment using Heruko

Introduction to Model Monitoring

Introduction to AutoML tools

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