MLOPS FULL COURSE | AWS AZURE GCP with The Best Real-World Trainings & Support

MLOPS FULL COURSE | AWS AZURE GCP

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UPI NO : +91 9542421540

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🚀 Bonus Perks

  • Real-world projects 🛠️
  • Certification support 🏅
  • Personalized career guidance 💡

💡 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

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