Complete MLOps Bootcamp With 10+ End To End ML Projects

51 hours 1 Enrolled No ratings yet All Levels

Curriculum
Description

Introduction

Introduction
IDE’s And Code Editors You Can Use

Getting Started With Google Colab

Getting Started With Github Codespace

Anaconda And VS Code Installation
Python Prerequisites

Getting Started With VS Code And Environment

Python Basics-Syntax and Semantics

Variables In Python

Basics Data Types

Operators In Python

Conditional Statements In Python

Loops In Python

Practical Examples Of List

Sets In Python

Tuples In Python

Dictionaries In Python

Functions In Python

Python Function Examples

Lambda Functions In Python

Map functions In Python

Python Filter Function

Import Modules And Packages In Python

Standard Library Overview

File Operation In Python

Working With File Paths

Exception Handling In Python

OOPS In Python

Inheritance In Python

Polymorphism In Python

Encapsulation In Python

Abstraction In Python

Magic Methods In Python

Custom Exception In Python

Operator OverLoading In Python

Iterators In Python

Generators In Python

Decorators In Python

Working With Numpy In Python

Pandas DataFrame And Series

Data Manipulation And Analysis

Data Source Reading

Logging In Python

Logging With Multiple Loggers

Logging In a Real World Examples
Complete Flask Tutorial

Introduction To Flask Framework

Understanding A Sample Flask Application

Integrating HTML With Flask Framework

HTTP Verbs Get And Post

Building Dynamic Url With Jinja 2

Put Delete And API’s In Flask
Git and Github

Getting Started With Git And Github

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

Part 3- Resolving Git Branch Merge Conflict
Complete MLFLow Tutorials

Introduction To MLFLOW

Getting Started With MLFLOW

Creating MLFLOW Environment

Getting Started With MLFLow Tracking Server

Deep Diving Into MLFlow Experiments

Getting Started With MLFlow ML Project

First ML Project With MLFLOW

Inferencing Model Artifacts With MLFlow Inferencing

MLFLOW Model Registry Tracking
ML Project Integration With MLFLOW Tracking

Data Preparation House Price Prediction

Model Building And MLFLOW Tracking
Deep Learning ANN Model Building Integration With MLFLOW

ANN With MLFLOW- Part 1

ANN with MLFLOW-Part 2
Getting Started With DVC – Data Version Control

Introduction To DVC With Practical Implementation
Getting Started With Dagshub

Introduction To Dagshub Remote Repository

Creating First Remote Repo Using Dagshub

DVC With Dagshub Remote Repository
End To End Machine Learning Pipeline Using Git, DVC, MLFLOW And DAGSHUB

Getting Started With Project Structure

Implemeting Data Preprocessing Pipeline

Implementing Model Training Pipeline with MLFLOW Setup

MLFLOW Experiment Tracking In Dagshub

ML Evaluation Piepline With MLFLOW

Run The Complete Pipeline With DVC Stage And Repro
MLFLOW With AWS Cloud

Introduction To MLFLOW In AWS

MLFLOW Project Set Up With Installation

Implementing The End To End Project With MLFLOW

AWS Cloud EC2,IAM,S3 Bucket Set Up

AWS EC2 Instance- Setting MLFLOW Tracking Server
Complete Basic To Advance Dockers

Introduction To Docker Series

What are Dockers And Containers

Docker Images vs Containers

Dockers vs Virtual Machines

Dockers Installation

Docker Basic Commands

Creating A Docker Image

Push Docker Image To Docker Hub

Docker Compose
Getting Started With Airflow

Introduction To Apache Airflow

Key Components Of Apache Airflow

Why Airflow For MLOPS

Setting Up Airflow With Astro

Building Your First DAG With Airflow

Designing Mathematical Calculation DAG With Airflow

Getting Started With TaskFlow API Using Apache Airflow
Airflow ETL Pipeline with Postgre and API Integration In ASTRO Cloud And AWS

Introduction To ETL Pipeline

ETL Problem Statement And Project Structure Set Up

Defining ETL DAG With Implementing Steps

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

Step 2- NASA API Integration With Extract Pipeline

Step 3- Building Transformation And Load Pipeline

ETL Pipeline Final Implementation With AirFlow Connection Set Up

ETL Pipeline Deployment In Astro Cloud And AWS
Introduction To Github Actions

What is Github Action and CI CD Pipeline

What is Developers Workflow With Examples

Practicals-Automate Testing Workflow With Python
End To End Github Action Workflow Project With Dockerhub

Github Action Workflow Project with Docker hub

Setting Project Structure With Github Repo

Setting Up Github Repository

Implementing Project With Flask And Dockers

Building the Yaml file for Dockers
Getting Started With Your First End To End Data Science Project With Deployment

Project Structure, Github Repo And Environment Set Up

Custom Logging Implementation

Common Utilities Functions Implementation

Step By Step Building Data Ingestion Pipeline- Part 1

Data Ingestion Pipeline-Part 2

Complete Data Validation Pipeline Implementation

Complete Data Transformation Pipeline Implementation

Model Trainer Pipeline Implementation

Model Evaluation Pipeline Implementation

Training And Prediction Pipeline With Flask App
End To End MLOPS Projects With ETL Pipelines – Building Network Security System

Project Structure Set up With Environment

Github Repository Set Up With VS Code

Packaging the Project With Setup.py

Logging And Exception Handling Implementation

Introduction To ETL Pipelines

Setting Up MongoDb Atlas

ETL Pipeline Setup With Python

Data Ingestion Architecture

Implementing Data Ingestion Configuration

Implementing Data Ingestions Component

Implementing Data Validation-Part 1

Implementing Data Validation- Part 2

Data Transformation Architecture

Data Transformation Implementation

Model Trainer-Part 1

Model Trainer And Evaluation With Hyperparameter Tuning

Model Experiment Tracker With MLFlow

MLFLOW Experiment Tracking With Remote Respository Dagshub

Model Pusher Implementation

Model Training Pipeline Implementation

Batch Prediction Pipeline Implementation

Final Model And Artifacts Pusher To AWS S3 buckets

Building Docker Image And Github Actions

Github Action-Docker Image Push to AWS ECR Repo Implementation

Final Deployment To EC2 instance
End To End DS Projects Implementation With Multiple AWS, Azure Deployment

Github And Code Setup

Project structure Logging And Exception

Project Problem Statement EDA And Model Training

Data Ingestion Implementation

Data Transformation Implementation

Model Trainer Implementation

Hyperparameter Tuning Implementation

Building Prediction Pipeline

Deployment AWS Beanstalk

Deployment In EC2 Instance

Deployment In Azure Web App
End To End NLP Project With HuggingFace And Transformers

Introduction To Huggingface And Problem Statement

Github Repo And Project Structure Set up

Logging And Utils Common Functionalities

Finetuning HuggingFace Models In Google Colab

Data Ingestion Implementation- Part 1

Data Ingestion Implementation- Part 2

Data Transformation Implementation

Model Trainer Implementation

Model Evaluation Implementation

Prediction Pipeline And API Integration
Build, Train, Deploy And Create Endpoints For ML Project Using AWS Sagemaker

Introduction To AWS Sagemaker Amd Project Set up

EDA,AWS IAM, S3 Set up With Data Ingestion

Implementing Training Script For AWS Sagemaker

Training With An On Spot Instance In AWS Sagemaker

Training With An On Spot Instance In AWS Sagemaker

Deployment Of Endpoint With AWS Sagemaker And Inferencing
Grafana-Open Source Tool For Data Visualization And Monitoring

Introduction To Grafana Open Source Tool

Grafana Cloud Set Up And Problem Statement

Visualization Implementation With Grafana Cloud And Postgresql In AWS
Generative AI Series With AWS LLMOPS

LifeCycle Of Gen AI Projects In Cloud

Blog Generation Generative AI App Using AWS Lambda And Bedrock

Deployment Of HuggingFace LLM Model In AWS Sagemaker

End To End GENAI App Using NVIDIA NIM

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!

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What's included

51 hours on-demand video
39 downloadable resources
Access on mobile and TV
Certificate of completion

Xlbake Academy

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Complete MLOps Bootcamp With 10+ End To End ML Projects

What's included

Requirements

Audience

What Will I Learn?

Xlbake Academy

554

198

137

Complete MLOps Bootcamp With 10+ End To End ML Projects

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What's included

Requirements

Audience

What Will I Learn?

Xlbake Academy

554

198

137