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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