Full Stack AI/ML Development Courses

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.

Prerequisite:

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
above
Memory: 4 GB RAM	Memory: 32 GB RAM
CPU: 1 CPU Cores	CPU: 8 CPU Cores
Storage: 20 GB	Storage: 500 GB SSD

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

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
MLOps Architectures:
Architectures w Open Source tools
Architectures w cloud Native tools – Amazon Web Services
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/Github:

Introduction to CI/CD Using Jenkins:

Cloud Native CI/CD Tools 101:

Kubernetes/Docker Overview

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

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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Full Stack AI/ML Development Courses

About Course

What Will You Learn?

Course Content

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

MLOps Architectures:

Architectures w Open Source tools

Architectures w cloud Native tools – Amazon Web Services

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/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

Hands-on: Data Set from Kaggle is considered to demonstrate the real time Machine Learning Model Design and Development. (Regression Model) ML Model will be retraining with an industry use case showing how CI/CD on an ML Model using DVC.

Kubernetes/Docker Overview

Kubernetes overview

Kubernetes Architecture o Nodes

Control Plane o API Server

Kubernetes Resources o Pod

Deployment o Replica

Service

Volumes (PVC)

Kubernetes Deployment Strategy o Monitoring

Liveness and Readiness Probes

Labels and Selectors

Docker Installation and Deployment.

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

DVC

Cloud ML Services

What is MLFlow

Various components of MLFlow Services

Benefits of using MLFlow Services

Hands-on: 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

Introduction to Model Monitoring

Why monitoring is important? 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.

Hands-on: Building a drift monitoring system on Nagios.

Introduction to AutoML tools

H20

Dataiku

Domino

Datarobot

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