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💡 Join now and take the first step toward your dream career!

What You'll Learn:

• MLOps Fundamentals: Automating ML workflows and deployment.
• AWS, Azure, and GCP: Hands-on experience with major cloud platforms.
• DevOps Practices: CI/CD, containerization, and orchestration.
• Data Engineering Skills: Building scalable and reliable data pipelines.
• Real-World Applications: Practical use cases in multi-cloud environments.

This session is ideal for aspiring MLOps professionals, DevOps engineers, and data engineers aiming to upskill and advance their careers.

🔑 Key Features:

• Live interactive training.
• Industry-relevant projects and demos.
• Guidance from experienced trainers.

MLOps Course Syllabus 🚀

Who Can Apply for the Course? 🧑‍💻🎓

This course is designed for a wide range of professionals and enthusiasts who want to master MLOps and related technologies. Here’s who will benefit the most:

• 👩‍🔬 Data Scientists
• Bring your models to production with confidence and scale!
• 📊 Data Engineers & Data Analysts
• Elevate your data pipelines and workflows with MLOps tools.
• 🔬 Research/Applied Scientists
• Optimize experiments and streamline your AI research lifecycle.
• 🤖 ML Engineers
• Build robust CI/CD pipelines and take your ML expertise to the next level.
• 🛠️ DevOps Engineers
• Transition into MLOps by applying your DevOps skills to ML workflows.
• ✨ Aspiring MLOps Professionals & Enthusiasts
• Dive into the world of MLOps and become future-ready in AI and ML technologies.
• 🚀 Machine Learning Professionals
• Learn to seamlessly deploy models into production environments.
• 🐳 Docker & Kubernetes Enthusiasts
• Gain hands-on experience with Docker, Kubernetes, and cloud-native MLOps tools.
• ☁️ Cloud Practitioners
• Master AWS, Azure, GCP, and other cutting-edge tools like DVC, Feast, and MLFlow.
• 💡 Individuals Interested in Data and AI Industry
• Stay ahead in the rapidly evolving world of data and AI

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
• 📝 Case Study: CI/CD in Production

Module 2: Overview of ML and MLOps Stages 🧠

• 📈 MLOps Maturity Model
• 📋 Detailed MLOps and Stages:

1. 📂 Versioning Data, Code, Model, Features & Containers
2. 🧪 Testing
3. 🔧 Automation (CI/CD)
4. 🔁 Reproducibility
5. 🚀 Deployment
6. 🛡️ Monitoring

• 🤖 Automated ML Pipelines vs CI/CD ML Pipelines

1. 🏗️ MLOps Architectures:Open Source Tools: Kubeflow, Apache Airflow, MLFlow, Metaflow, Kedro, ZenML, MLRun, CML
2. Cloud-Native Tools: AWS, GCP, and Azure
3. ⚖️ 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 🖇️

[Hands-On]

• 🗂️ 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 ⚙️

[Hands-On]

• 🔄 Introduction to CI and CD
• 🤖 CI/CD in Machine Learning Operations
• 📋 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:
• 1. AWS DevOps
• 🛠️ AWS CodePipeline
• 🛠️ AWS CodeBuild
• 🛠️ AWS CodeDeploy
• 🛠️ AWS CodeCommit
• 🎯 Project: AWS DevOps Pipeline
• 2. GCP DevOps
• ☁️ Cloud Run
• ☁️ Cloud Build
• ☁️ Cloud Deploy
• ☁️ Artifacts Registry
• ☁️ Cloud Source Repositories
• 🎯 Project: GCP DevOps Pipeline
• 3. 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
• 4. GitHub Actions
• 🤖 Introduction to GitHub Actions
• 🔧 GitHub Actions YAML Pipeline Structure
• 🤝 GitHub Action Automation & Custom Workflows
• 🌐 GitHub Pages
• 🎯 Project: GitHub Actions Pipeline

Module 5: Docker and Kubernetes Overview 🐳 + ☸️

[Hands-On]

Docker Foundation

• 📥 Installing Docker on Windows, macOS & Linux
• 🔧 Managing Containers 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

1. 🎯 Projects:Deploy a Node.js App in a Docker Container
2. Deploy an ML Model in a Docker Container
3. Deploy a Complete End-to-End ML Model with Docker Compose

Kubernetes Overview

• ☸️ Kubernetes Architecture:

1. Worker Nodes
2. Control Plane
3. Virtual Network
4. API Server
5. CLI Tool - kubectl

• ☸️ Kubernetes Resources:

1. Pod
2. ConfigMap
3. Service
4. Secret
5. Ingress
6. Deployment
7. StatefulSet
8. DaemonSet
9. Volumes (PVC)

• ☸️ Minikube
• 🎯 Project: Deploy an ML Model in a Kubernetes Cluster

Module 6: Kubernetes Deployment Strategy ☸️

[Hands-On]

• 📦 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 Model Management?
• 📋 Activities in Model Management:

1. Data Versioning
2. Code Versioning
3. Experiment Tracker
4. Model Registry
5. Model Monitoring
6. 🛠️ Overview of Model Management Tools:MLFlow

• 🎯 Project: Deploy MLFlow Stack on the Cloud
• 🎯 Project: Build, Train, and Deploy an ML Model Using MLFlow

1. DVC (Data Version Control)

• Versioning Data and Models
• DVC with Git Workflows
• Data Source for DVC
• 🎯 Project: Version Data Stored in Cloud Storage Services

1. Git LFS (Large File Storage)

Module 8: Feature Store 📊

[Hands-On]

• 📘 Introduction to Feature Stores: SageMaker Feature Store, Vertex AI Feature Store, Databricks, Tecton, Feast, Hopsworks
• 🔍 Feast Open Source Feature Store
• 🔄 Online vs Offline Feature Store
• 🎯 Project: Deploy Feast Online/Offline Feature Store
• ☁️ Feast Feature Store on Cloud
• 📈 Monitor Features Programmatically
• 📊 Visualizing Feature Drift Over Time

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

[Hands-On]

AWS SageMaker

• 🤖 Introduction to Amazon SageMaker
• 🛠️ Using Amazon S3 Along with SageMaker
• 📝 SageMaker Notebooks: Instance Type, IAM Role & VPC
• 🚀 Build, Train & Deploy ML Models Using SageMaker
• 📡 Endpoint & Endpoint Configurations
• 🧠 Generate Inference from Deployed Models

SageMaker Pipelines

• SageMaker Studio & Domain
• SageMaker Projects
• Pipelines & Graphs
• Experiments
• Model Groups
• 🎯 Project: Deploy an End-to-End MLOps Pipeline Using SageMaker Studio

GCP Vertex AI

• 🌟 Introduction to Vertex AI
• 📥 Gather, Import & Label Datasets
• 🚀 Build, Train & Deploy ML Solutions
• 🛡️ Manage Your Models with Confidence
• 📊 Pipelines Throughout the ML Workflow
• 🔄 Adapting to Changes in Data
• 🎯 Project: Deploy an End-to-End MLOps Pipeline Using Vertex AI

Azure MLOps

• 🛠️ Azure Machine Learning Studio
• 🧩 Azure MLOps Components
• 🔧 Azure MLOps + DevOps Integration
• 🔄 Fully Automated End-to-End CI/CD ML Pipelines
• 🎯 Project: Deploy an End-to-End MLOps Pipeline with Azure Machine Learning

Module 10: Kubeflow Stack and ML Pipeline Development ☸️

• 📘 Kubeflow Introduction
• 🌍 Who Uses Kubeflow?
• 🧩 Kubeflow Components
• 🛠️ Kubeflow Features
• 🎯 Kubeflow Fairing
• 🔄 Kubeflow Pipelines
• 🏗️ Kubeflow Use Cases
• 🎯 Project: Deploy a Kubeflow Stack and Create End-to-End ML Pipelines

Module 11: MLOps for LLMs (LLMOps) & Generative AI 🤖

[Demo]

• 🌟 What is LLM?
• 🛠️ MLOps for LLMs
• 🔄 FMOps/LLMOps: Operationalizing Generative AI
• 📐 LLM System Design
• 📊 High-Level View of LLM-Driven Applications
• 🔧 LLMOps Pipeline

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

[Hands-On]

• 🚨 Importance of Model Monitoring
• 📋 Various Types of Monitoring Related to ML Models
• 🏗️ Architecture of Monitoring Ecosystem in AWS, Azure, and GCP:

1. AWS Model Monitoring
2. Azure Model Monitoring
3. GCP Model Monitoring

• 🌍 Optimize and Manage Models at the Edge
• 🚀 Common Issues in ML Model Deployment
• 🔄 Role of Feedback Loop
• 🎯 Project: Model & Infrastructure Monitoring Using Cloud Tools

Module 13: Introduction to AutoML Tools 🛠️

[Demo]

• 🔄 H2O MLOps
• 🌟 Valohai
• 🛠️ Domino Data Lab
• 🧠 Neptune.ai
• 🛡️ Iguazio
• 📊 Weights & Biases (W&B)

Module 14: Post-Deployment Challenges ⚙️

[Hands-On]

• 🚧 Introduction to Post-Deployment Challenges
• ❗ ML-Related Post-Deployment Challenges
• 🌍 Challenges When Deploying ML to Edge Devices
• 📊 Monitoring Drift with Evidently AI
• 🔄 Common Software Engineering Challenges in ML Deployment
• 🎯 Project: Use Evidently AI for Monitoring Data Quality and Drift

Module 15: Other Open-Source/Cloud Tools for MLOps 🌐

[Self-Paced + Hands-On]

Jenkins

• 🔄 Understanding Jenkins CI/CD
• 🔧 Jenkins Plugins
• 📋 Pipeline as Code
• 🌍 Distributed Builds
• 🖥️ Jenkins User Interface
• 🤝 Integration with SCM Tools
• 📊 Monitoring and Reporting

1. 🎯 Projects:Building a Python Application with Jenkins Pipelines
2. Build End-to-End ML Pipelines Using Jenkins, Docker Containers, and MLflow

Apache Airflow

• 📘 What is Apache Airflow?
• 🔄 Airflow Workflows and Use Cases
• 🛠️ Airflow Benefits and Components:

1. DAG (Directed Acyclic Graph)
2. Airflow Tasks, Operators, and Hooks
3. 🎯 Projects:Build Scheduled ETL Pipelines with Apache Airflow
4. Build Scheduled End-to-End ML Pipelines

Google Kubernetes Engine (GKE)

• ☸️ Introduction to GKE
• 📊 Benefits and Limitations of GKE
• 🎯 Project: Deploy a Kubernetes Infrastructure on GKE and Deploy an ML Model

Azure Kubernetes Service (AKS)

• 🔷 Overview of AKS and When to Use It
• 🌟 Features of AKS
• 🎯 Project: Deploy a Kubernetes Infrastructure on AKS and Deploy an ML Model

Terraform

• 🔧 What is Terraform?
• 🛠️ Infrastructure as Code (IaC) Using Terraform
• 📋 Terraform Configuration and Stages

1. 🎯 Projects:Build, Modify, and Destroy Docker Infrastructure for ML Model Deployment
2. Build, Change, and Destroy AWS Cloud Infrastructure

Argo Workflows

• 🌟 Introduction to Argo Workflows
• 🔄 CI/CD with Argo Workflows on Kubernetes
• 🛠️ Argo Templates and UI
• 🎯 Project: Build End-to-End Scheduled ML Pipelines Using Argo Workflows

Please contact +91 9100665231 For more information and details about the course and fees