Machine Learning Operations (MLOps) Specialization Course

Instructor: Saratah Kumar Language: ENGLISH

Program features

About the Program

Looking for getting started with Hands-on Machine Learning Operations (MLOps) with a real-time project, then you've come to the right place. As per the market survey, 2023 is the year of MLOps and would become the mandate skill set for Enterprise ML projects. Corporates have been experimenting with machine learning models for a long time, 85% of Machine Learning projects do not reach production. In addition, the MLOps have exponentially grown in the last few years. MLOps was estimated at $23.2 billion for 2019 and is projected to reach $126 billion by 2025. Therefore, MLOps knowledge will give you numerous professional opportunities helps organizations to bring in real business value

Key Highlights

90 Hours of Live sessions from Industrial Experts
50+ Live Hands-on Labs
28 Real-time industrial projects
One-on-One Debugging with Industry Mentors

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Who Can Apply for the Course?

Data Scientists
Data engineers & Data Analysts
Research/Applied Scientists
ML engineers
DevOps engineers
Aspiring MLOps Professionals and Enthusiasts
Machine Learning professionals who want to deploy models to production
Anyone who wants to learn Docker & Kubernetes, AWS, Azure, GCP, DVC, Feast, MLFlow etc
Individuals interested in the data and the AI industry

Program Curriculum

Module 1: MLOps Introduction

What is MLOps?
State of machine learning
Machine learning industrialisation challenges
AI Industrialization Challenges
MLOps Motivation: High-level view
MLOps challenges
MLOps challenges similar to DevOps
MLOps Components
Machine Learning Life Cycle
How does it relate to DevOps, AIOps, ModelOps, LLMOps, FMOps, and GitOps?
Major Phases - what it takes to master MLOps
CI/CD in Production Case Study

Module 2: Overview of ML and MLOps Stages

MLOps Maturity Model

Detailed MLOps and stages

Versioning Data, Code, Model, Features & Containers

Testing

Automation (CI/CD)

Reproducibility

Deployment

Monitoring

Automated ML pipelines vs CI/CD ML pipelines

MLOps Architectures

Architectures - Open Source tools - Kubeflow, MLFlow, Metaflow, Kedro, ZenML, MLRun, CML

Architectures - Cloud Native tools - AWS, GCP and Azure

The cost-benefit approach of each architecture and MLOps maturity

List of tools involved in each stage (MLOps tool ecosystem)

Different Roles involved in MLOps ( ML Engineering + Operations )

Module 3: Git Essentials for MLOps Practitioners

Overview of Git

Understanding branching strategies and REPO

Standard GIT branching strategies (development, feature, bug, release, UAT)

Practising important Git commands

GitHub Action overview and working

GitHub Remote Repository

Project: Mastering Git: Commands, Branching, and Collaboration

Module 4: CI/CD Strategies for AWS, Azure, GCP, and GitHub Actions

Introduction to CI and CD

CI/CD challenges in Machine Learning

Steps involved in the CI/CD implementation in ML lifecycle and workflow

A glimpse of popular Tools used in the DevOps ecosystem on the Cloud.

AWS DevOp

AWS CodePipeline

AWS CodeBuild

AWS CodeDeploy

AWS CodeCommit

Project: AWS DevOps Pipeline

GCP DevOps

Cloud Run

Cloud Build

Cloud Deploy

Artifacts Registry

Cloud Source Repositories

Project: GCP DevOps Pipeline

Azure DevOps

Azure Boards

Azure Repos

Azure Pipeline

Azure Test Plans

Azure Artifacts

Infrastructure as code (IaC) with Azure DevOps

YAML Pipeline Structure

Project: Azure DevOps Pipeline

GitHub Actions

Introduction to GitHub Actions

GitHub Actions YAML pipeline structure

GitHub Action automation & Custom Workflows

GitHub Pages

Project: GitHub Actions DevOps Pipeline

Module 5: Docker & Kubernetes Overview

Docker Foundation

Installing Docker on Windows, macOS & Linux

Managing Container with Docker Commands

How does it work? Docker registry - Docker Hub

Building your own Docker images

Docker Network Types

Docker Volumes

Docker Compose

Docker Swarm

Project: Deploy a Node.js app in a Docker container

Project: Deploy an ML model in a Docker container

Project: Deploy a complete end-to-end ML model with Docker Compose

Kubernetes Overview

Kubernetes Architecture

Worker Nodes

Control Plane

Virtual Network

API Server

Command line tool - kubectl

Kubernetes Resources

Pod

ConfigMap

Service

Secret

Ingress

Deployment

StatefulSet

DaemonSet

Volumes (PVC)

Minikube

Project: Deploy an ML model in a Kubernetes cluster

Module 6: Kubernetes Deployment Strategy

Kubernetes Deployment Strategy Types

Monitoring

Liveness and Readiness Probes

Labels and Selectors

Amazon Elastic Kubernetes Service (EKS)

Project: Deploy a Kubernetes infrastructure on Amazon EKS and deploy an ML model on EKS

Module 7: High-level Overview of Model Management Tools

What is a Model Management

What are the various activities in Model Management

Data Versioning

Code Versioning

Experiment Tracker

Model Registry

Model Monitoring

A high-level overview of the below Model Management tools

MLFlow

Project: Deploy MLFlow stack on the cloud

Project: Build, train, and deploy an ML model using MLFlow Experiments and MLFlow model registry.

Data Version Control (DVC)

Versioning of data and models

DVC with Git workflows

Data source for DVC

Project: Version data stored in cloud storage services

Git Large File Storage (LFS)

Module 8: Feature Store

Introduction to Feature Stores, SageMaker Feature Store, Vertex AI Feature Store, Databricks, Tecton, Feast, Hopsworks etc.

Feast open source feature store

Feature Store: Onlne Vs Offline

Project: Deploy Feast Online/Offline feature store

Online & offline feature store options

Feast Feature Store on Cloud

Monitor features programmatically

Visualizing feature drift over time

Module 9: Deep Dive into MLOps Cloud Services (AWS, Azure & GCP)

AWS SageMaker

Introduction to Amazon Sagemaker

Using Amazon S3 along with Sagemaker

Amazon SageMaker Notebooks

Notebook instance type, IAM Role & VPC

Build, Train & deploy ML Model using Sagemaker

Endpoint & Endpoint configurations

Generate inference from deployed model

AWS SageMaker Pipelines

SageMaker Studio & SageMaker domain

SageMaker Projects

Repositories

Pipelines & Graphs

Experiments

Model groups

Endpoints

Project: Deploy an end-to-end MLOps pipeline using Sagemaker Studio.

GCP VertexAI

Introduction of Vertex AI

Gather, Import & label datasets

Build, Train & deploy ML Solutions

Manage your models with confidence

Using Pipelines throughout your ML workflow

Adapting to changes in data

Creating models with Vertex AI and deploying ML models using aiplatform pipelines

Project: Deploy an end-to-end MLOps pipeline using Vertex AI

Azure MLOps

Azure Machine learning studio

Azure MLOps

Azure ML components

Azure MLOps + DevOps

Fully automated end-to-end CI/CD ML pipelines

Project:Deploy an end-to-end MLOps V2 pipeline using Azure Machine Learning

Module 10: Kubeflow Stack and ML Pipeline Development

Kubeflow Introduction

Kubeflow - Who uses it

Kubeflow Components

Kubeflow features

Kubeflow Fairing

Kubeflow Pipelines

Kubeflow use cases

Project: Deploy a Kubeflow stack and create end-to-end ML pipelines on it

Module 11: MLOps for LLM’s (LLMOps)

What is LLM?

MLOps for LLM’s

FMOps/LLMOps: Operationalize generative AI

LLM System Design

High-level view LLM-driven application

LLMOps Pipeline

Module 12: Understanding Model Monitoring (AWS, Azure & GCP)

Importance Of Model Monitoring

What are the various types of monitoring related to the model

The architecture of monitoring ecosystem in AWS/Azure/GCP

AWS Model Monitoring

Azure Model Monitoring

GCP Model Monitoring

Optimize and Manage Models at the Edge

Common Issues in ML Model Deployment

Feedback Loop Role

Project: Model & infrastructure monitoring using cloud tools

Module 13: Introduction to automl tools

H20 MLOps

Valohai

Domino Data Lab

neptune.ai

iguazio

W&B

Module 14: Post-Deployment Challenges

Post Deployment Challenges intro

Post Deployment Challenges - ML Related

Challenges when deploying machine learning to edge devices

Post Deployment - Monitoring the Drift - Evidently

Monitoring the Drift - Using Sagemaker

Post Deployment Challenges - Software Engineering Related

Common Issues in ML Model Deployment

Project: Evidently AI for Monitoring the Drift

Module 15: (Self-paced) Other Open-Source/Cloud tools for MLOps

Jenkins

Understanding Jenkins CI/CD

Jenkins Plugins

Pipeline as Code

Distributed Builds

Understanding Jenkins User Interface

Integration with SCM Tools

Monitoring and Reporting

Jenkins Common Use Cases

Project: Building a Python Application with Jenkins Pipelines

Project: Build end-to-end ML pipelines using Jenkins, Docker containers, and MLflow

Apache Airflow

What is Apache Airflow?

Airflow workflow

Airflow use cases

Airflow benefits

Apache Airflow Fundamentals

Directed Acyclic Graph (DAG)

DAG run

Airflow Tasks

Airflow Operators

Airflow Hooks

Project: Building Scheduled ETL data Pipelines with Apache Airflow

Project: Building Scheduled End-to-End ML Pipelines with Apache Airflow

Google Kubernetes Engine (GKE)

Introduction to GKE

Benefits of using GKE

How does GKE work?

Limitations of using GKE

Project: Deploy a Kubernetes infrastructure on GKE and deploy an ML model

Azure Kubernetes Service (AKS)

What is AKS?

Overview of AKS

When to use AKS?

Features of AKS

Project: Deploy a Kubernetes infrastructure on AKS and deploy an ML model

Terraform

What is Terraform?

How does Terraform work?

Infrastructure as code (IaC) using Terraform

Terraform stages

Why Terraform?

Terraform Registry

Terraform configuration

Project: Build, Modify, and Destroy Docker Infrastructure for ML Model Deployment with Terraform

Project: Build, change, and destroy AWS cloud infrastructure using Terraform

Argo Workflows

Intro Argo Workflows

CI/CD with Argo Workflows on Kubernetes

Argo Architecture

Argo Workflows CRDs (Custom Resource Definitions)

Argo Templates

Argo Workflows UI

Project: Building End-to-End Scheduled ML Pipelines with Argo Workflows and Kubernetes

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Basic Plan

Lifetime

$624
$674

Advanced Plan

Lifetime

$674
$774

Advanced Plus

Lifetime

$774
$873

View all plans

$624

$674

Testimonials

Nisheeth Jaiswal - Participants - MLOps

Dipali Matkar - MLOps Engineer Must Watch 👇

Rahul Patil - Participants - MLOps

Sitaram - Participants - MLOps Specialization course

Dhirendra Kumar Singh - Participants - MLOps

Fathima Hafeez - Participants - MLOps

What you’ll learn

Instructor-led Training

Get trained by top industry experts

Projects and Exercises

Get real-world experience through Projects

Peer Networking and Group Learning

Improve your professional network and learn from peers through our innovative Peer WhatsApp & community groups.

24*7 Technical Support

Speak to Subject Matter Experts anytime and clarify your queries instantly.

Live Hands-on

Hands-on exercises, project work, quizzes, and capstone projects

Testimonials in WhatsApp groups

This is how our participants feel after completing our Course & Workshop. Participants' feedback from WhatsApp Group.

ParticipantsTestimonial

Participants' feedback from various different IT backgrounds

😍MLOps Salary Trends

This is the best time to learn machine learning as the trends in the market suggest. The global machine learning market is estimated at US dollar 8.43 billion in 2019 and is expected to reach 117 billion by 2027, at a CAGR of 39.2%. Thus, job opportunities in this sector are going to grow with a boom in the coming years.

Machine Learning Operations (MLOps) Specialization Course

Program features

About the Program

Watch Demo

Who Can Apply for the Course?