Complete Mlops Bootcamp With 10+ End To End Ml Projects

End-to-End MLOps Bootcamp: Build, Deploy, and Automate ML with Data Science Projects

What you’ll learn
Build scalable MLOps pipelines with Git, Docker, and CI/CD integration.
Implement MLFlow and DVC for model versioning and experiment tracking.
Deploy end-to-end ML models with AWS SageMaker and Huggingface.
Automate ETL pipelines and ML workflows using Apache Airflow and Astro.
Monitor ML systems using Grafana and PostgreSQL for real-time insights.

Requirements
Basic understanding of Python programming.
Familiarity with machine learning concepts and algorithms.
Basic knowledge of Git and GitHub for version control.
Understanding of Docker for containerization (optional but helpful).
Awareness of cloud computing concepts (AWS preferred, but not mandatory).

Description
Welcome to the Complete MLOps Bootcamp With End to End Data Science Project, your one-stop guide to mastering MLOps from scratch! This course is designed to equip you with the skills and knowledge necessary to implement and automate the deployment, monitoring, and scaling of machine learning models using the latest MLOps tools and frameworks.In today’s world, simply building machine learning models is not enough. To succeed as a data scientist, machine learning engineer, or DevOps professional, you need to understand how to take your models from development to production while ensuring scalability, reliability, and continuous monitoring. This is where MLOps (Machine Learning Operations) comes into play, combining the best practices of DevOps and ML model lifecycle management.This bootcamp will not only introduce you to the concepts of MLOps but will take you through real-world, hands-on data science projects. By the end of the course, you will be able to confidently build, deploy, and manage machine learning pipelines in production environments.What You’ll Learn:Python Prerequisites: Brush up on essential Python programming skills needed for building data science and MLOps pipelines.Version Control with Git & GitHub: Understand how to manage code and collaborate on machine learning projects using Git and GitHub.Docker & Containerization: Learn the fundamentals of Docker and how to containerize your ML models for easy and scalable deployment.MLflow for Experiment Tracking: Master the use of MLFlow to track experiments, manage models, and seamlessly integrate with AWS Cloud for model management and deployment.DVC for Data Versioning: Learn Data Version Control (DVC) to manage datasets, models, and versioning efficiently, ensuring reproducibility in your ML pipelines.DagsHub for Collaborative MLOps: Utilize DagsHub for integrated tracking of your code, data, and ML experiments using Git and DVC.Apache Airflow with Astro: Automate and orchestrate your ML workflows using Airflow with Astronomer, ensuring your pipelines run seamlessly.CI/CD Pipeline with GitHub Actions: Implement a continuous integration/continuous deployment (CI/CD) pipeline to automate testing, model deployment, and updates.ETL Pipeline Implementation: Build and deploy complete ETL (Extract, Transform, Load) pipelines using Apache Airflow, integrating data sources for machine learning models.End-to-End Machine Learning Project: Walk through a full ML project from data collection to deployment, ensuring you understand how to apply MLOps in practice.End-to-End NLP Project with Huggingface: Work on a real-world NLP project, learning how to deploy and monitor transformer models using Huggingface tools.AWS SageMaker for ML Deployment: Learn how to deploy, scale, and monitor your models on AWS SageMaker, integrating seamlessly with other AWS services.Gen AI with AWS Cloud: Explore Generative AI techniques and learn how to deploy these models using AWS cloud infrastructure.Monitoring with Grafana & PostgreSQL: Monitor the performance of your models and pipelines using Grafana dashboards connected to PostgreSQL for real-time insights.Who is this Course For?Data Scientists and Machine Learning Engineers aiming to scale their ML models and automate deployments.DevOps professionals looking to integrate machine learning pipelines into production environments.Software Engineers transitioning into the MLOps domain.IT professionals interested in end-to-end deployment of machine learning models with real-world data science projects.Why Enroll?By enrolling in this course, you will gain hands-on experience with cutting-edge tools and techniques used in the industry today. Whether you’re a data science professional or a beginner looking to expand your skill set, this course will guide you through real-world projects, ensuring you gain the practical knowledge needed to implement MLOps workflows successfully.Enroll now and take your data science skills to the next level with MLOps!

Overview
Section 1: Introduction

Lecture 1 Introduction

Section 2: IDE’s And Code Editors You Can Use

Lecture 2 Getting Started With Google Colab

Lecture 3 Getting Started With Github Codespace

Lecture 4 Anaconda And VS Code Installation

Section 3: Python Prerequisites

Lecture 5 Getting Started With VS Code And Environment

Lecture 6 Python Basics-Syntax and Semantics

Lecture 7 Variables In Python

Lecture 8 Basics Data Types

Lecture 9 Operators In Python

Lecture 10 Conditional Statements In Python

Lecture 11 Loops In Python

Lecture 12 Practical Examples Of List

Lecture 13 Sets In Python

Lecture 14 Tuples In Python

Lecture 15 Dictionaries In Python

Lecture 16 Functions In Python

Lecture 17 Python Function Examples

Lecture 18 Lambda Functions In Python

Lecture 19 Map functions In Python

Lecture 20 Python Filter Function

Lecture 21 Import Modules And Packages In Python

Lecture 22 Standard Library Overview

Lecture 23 File Operation In Python

Lecture 24 Working With File Paths

Lecture 25 Exception Handling In Python

Lecture 26 OOPS In Python

Lecture 27 Inheritance In Python

Lecture 28 Polymorphism In Python

Lecture 29 Encapsulation In Python

Lecture 30 Abstraction In Python

Lecture 31 Magic Methods In Python

Lecture 32 Custom Exception In Python

Lecture 33 Operator OverLoading In Python

Lecture 34 Iterators In Python

Lecture 35 Generators In Python

Lecture 36 Decorators In Python

Lecture 37 Working With Numpy In Python

Lecture 38 Pandas DataFrame And Series

Lecture 39 Data Manipulation And Analysis

Lecture 40 Data Source Reading

Lecture 41 Logging In Python

Lecture 42 Logging With Multiple Loggers

Lecture 43 Logging In a Real World Examples

Section 4: Complete Flask Tutorial

Lecture 44 Introduction To Flask Framework

Lecture 45 Understanding A Sample Flask Application

Lecture 46 Integrating HTML With Flask Framework

Lecture 47 HTTP Verbs Get And Post

Lecture 48 Building Dynamic Url With Jinja 2

Lecture 49 Put Delete And API’s In Flask

Section 5: Git and Github

Lecture 50 Getting Started With Git And Github

Lecture 51 Part 2- Git Merge,Push, Checkout And Log With Commands

Lecture 52 Part 3- Resolving Git Branch Merge Conflict

Section 6: Complete MLFLOW Tutorials

Lecture 53 Introduction To MLFLOW

Lecture 54 Getting Started With MLFLOW

Lecture 55 Creating MLFLOW Environment

Lecture 56 Getting Started With MLFLow Tracking Server

Lecture 57 Deep Diving Into MLFlow Experiments

Lecture 58 Getting Started With MLFlow ML Project

Lecture 59 First ML Project With MLFLOW

Lecture 60 Inferencing Model Artifacts With MLFlow Inferencing

Lecture 61 MLFLOW Model Registry Tracking

Section 7: ML Project Integration With MLFLOW Tracking

Lecture 62 Data Preparation House Price Prediction

Lecture 63 Model Building And MLFLOW Tracking

Section 8: Deep Learning ANN Model Building Integration With MLFLOW

Lecture 64 ANN With MLFLOW- Part 1

Lecture 65 ANN with MLFLOW-Part 2

Section 9: Getting Started With DVC- Data Version Control

Lecture 66 Introduction To DVC With Practical Implementation

Section 10: Getting Started With Dagshub

Lecture 67 Introduction To Dagshub Remote Repository

Lecture 68 Creating First Remote Repo Using Dagshub

Lecture 69 DVC With Dagshub Remote Repository

Section 11: End To End Machine Learning Pipeline Using GIT, DVC,MLFLOW And DAGSHUB

Lecture 70 Getting Started With Project Structure

Lecture 71 Implemeting Data Preprocessing Pipeline

Lecture 72 Implementing Model Training Pipeline with MLFLOW Setup

Lecture 73 MLFLOW Experiment Tracking In Dagshub

Lecture 74 ML Evaluation Piepline With MLFLOW

Lecture 75 Run The Complete Pipeline With DVC Stage And Repro

Section 12: MLFLOW With AWS Cloud

Lecture 76 Introduction To MLFLOW In AWS

Lecture 77 MLFLOW Project Set Up With Installation

Lecture 78 Implementing The End To End Project With MLFLOW

Lecture 79 AWS Cloud EC2,IAM,S3 Bucket Set Up

Lecture 80 AWS EC2 Instance- Setting MLFLOW Tracking Server

Section 13: Complete Basic To Advance Dockers

Lecture 81 Introduction To Docker Series

Lecture 82 What are Dockers And Containers

Lecture 83 Docker Images vs Containers

Lecture 84 Dockers vs Virtual Machines

Lecture 85 Dockers Installation

Lecture 86 Creating A Docker Image

Lecture 87 Docker Basic Commands

Lecture 88 Push Docker Image To Docker Hub

Lecture 89 Docker Compose

Section 14: Getting Started With Airflow

Lecture 90 Introduction To Apache Airflow

Lecture 91 Key Components Of Apache Airflow

Lecture 92 Why Airflow For MLOPS

Lecture 93 Setting Up Airflow With Astro

Lecture 94 Building Your First DAG With Airflow

Lecture 95 Designing Mathematical Calculation DAG With Airflow

Lecture 96 Getting Started With TaskFlow API Using Apache Airflow

Section 15: Airflow ETL Pipeline with Postgres and API Integration In ASTRO Cloud And AWS

Lecture 97 Introduction To ETL Pipeline

Lecture 98 ETL Problem Statement And Project Structure Set Up

Lecture 99 Defining ETL DAG With Implementing Steps

Lecture 100 Step 1- Setting Up Postgres And Creating Table Task In Postgres

Lecture 101 Step 2- NASA API Integration With Extract Pipeline

Lecture 102 Step 3- Building Transformation And Load Pipeline

Lecture 103 ETL Pipeline Final Implementation With AirFlow Connection Set Up

Lecture 104 ETL Pipeline Deployment In Astro Cloud And AWS

Section 16: Introduction To Github Actions

Lecture 105 What is Github Action and CI CD Pipeline

Lecture 106 What is Developers Workflow With Examples

Lecture 107 Practicals-Automate Testing Workflow With Python

Section 17: End To End Github Action Workflow Project With Dockerhub

Lecture 108 Github Action Workflow Project with Docker hub

Lecture 109 Setting Project Structure With Github Repo

Lecture 110 Setting Up Github Repository

Lecture 111 Implementing Project With Flask And Dockers

Lecture 112 Building the Yaml file for Dockers

Section 18: Getting Started With Your First End To End Data Science Project With Deployment

Lecture 113 Project Structure, Github Repo And Environment Set Up

Lecture 114 Custom Logging Implementation

Lecture 115 Common Utilities Functions Implementation

Lecture 116 Step By Step Building Data Ingestion Pipeline- Part 1

Lecture 117 Data Ingestion Pipeline-Part 2

Lecture 118 Complete Data Validation Pipeline Implementation

Lecture 119 Complete Data Transformation Pipeline Implementation

Lecture 120 Model Trainer Pipeline Implementation

Lecture 121 Model Evaluation Pipeline Implementation

Lecture 122 Training And Prediction Pipeline With Flask App

Section 19: End To End MLOPS Projects With ETL Pipelines- Building Network Security System

Lecture 123 Project Structure Set up With Environment

Lecture 124 Github Repository Set Up With VS Code

Lecture 125 Packaging the Project With Setup.py

Lecture 126 Logging And Exception Handling Implementation

Lecture 127 Introduction To ETL Pipelines

Lecture 128 Setting Up MongoDb Atlas

Lecture 129 ETL Pipeline Setup With Python

Lecture 130 Data Ingestion Architecture

Lecture 131 Implementing Data Ingestion Configuration

Lecture 132 Implementing Data Ingestions Component

Lecture 133 Implementing Data Validation-Part 1

Lecture 134 Implementing Data Validation- Part 2

Lecture 135 Data Transformation Architecture

Lecture 136 Data Transformation Implementation

Lecture 137 Model Trainer-Part 1

Lecture 138 Model Trainer And Evaluation With Hyperparameter Tuning

Lecture 139 Model Experiment Tracker With MLFlow

Lecture 140 MLFLOW Experiment Tracking With Remote Respository Dagshub

Lecture 141 Model Pusher Implementation

Lecture 142 Model Training Pipeline Implementation

Lecture 143 Batch Prediction Pipeline Implementation

Lecture 144 Final Model And Artifacts Pusher To AWS S3 buckets

Lecture 145 Building Docker Image And Github Actions

Lecture 146 Github Action-Docker Image Push to AWS ECR Repo Implementation

Lecture 147 Final Deployment To EC2 instance

Section 20: End To End DS Project Implementation With Mulitple AWS,Azure Deployment

Lecture 148 Github And Code Setup

Lecture 149 Project structure Logging And Exception

Lecture 150 Project Problem Statement EDA And Model Training

Lecture 151 Data Ingestion Implementation

Lecture 152 Data Transformation Implementation

Lecture 153 Model Trainer Implementation

Lecture 154 Hyperparameter Tuning Implementation

Lecture 155 Building Prediction Pipeline

Lecture 156 Deployment AWS Beanstalk

Lecture 157 Deployment In EC2 Instance

Lecture 158 Deployment In Azure Web App

Section 21: Build, Train ,Deploy And Create Endpoints For ML Project Using AWS Sagemaker

Lecture 159 Introduction To AWS Sagemaker Amd Project Set up

Lecture 160 EDA,AWS IAM, S3 Set up With Data Ingestion

Lecture 161 Implementing Training Script For AWS Sagemaker

Lecture 162 Training With An On Spot Instance In AWS Sagemaker

Lecture 163 Deployment Of Endpoint With AWS Sagemaker And Inferencing

Section 22: Grafana-Open Source Tool For Data Visualization And Monitoring

Lecture 164 Introduction To Grafana Open Source Tool

Lecture 165 Grafana Cloud Set Up And Problem Statement

Lecture 166 Visualization Implementation With Grafana Cloud And Postgresql In AWS

Section 23: Generative AI Series With AWS LLMOPS

Lecture 167 LifeCycle Of Gen AI Projects In Cloud

Lecture 168 Blog Generation Generative AI App Using AWS Lambda And Bedrock

Lecture 169 Deployment Of HuggingFace LLM Model In AWS Sagemaker

Lecture 170 End To End GENAI App Using NVIDIA NIM

Data Scientists and Machine Learning Engineers looking to scale and deploy ML models.,DevOps professionals wanting to integrate ML pipelines.,Software Engineers interested in transitioning to MLOps.,Beginners with basic ML knowledge aiming to learn end-to-end deployment.,IT professionals eager to understand MLOps tools and practices for real-world projects.

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