Complete MLOps Bootcamp With 10+ End To End ML Projects from Udemy

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.

What's inside

Learning objectives

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.

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.

Syllabus

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

Creating A Docker Image

Docker Basic Commands

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers essential tools like Git, Docker, MLflow, DVC, and Airflow, which are widely adopted in the MLOps industry for managing and automating ML pipelines

Includes hands-on experience with AWS SageMaker and Hugging Face, which are essential for deploying and scaling ML models in real-world scenarios

Features end-to-end projects, including an NLP project with Hugging Face, providing practical experience in deploying and monitoring transformer models

Requires familiarity with Python, as it covers Python basics, data types, functions, and modules, which may be time-consuming for individuals with limited programming experience

Requires learners to set up accounts with and learn to use services such as AWS, GitHub, DagsHub, and Astro, which may require a paid subscription after a trial period

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Complete MLOps Bootcamp With 10+ End To End ML Projects with these activities:

Review Python Fundamentals

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Solidify your understanding of Python basics to ensure a smooth start with the MLOps pipelines.

Browse courses on Python Syntax

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Review basic syntax and data types.
Practice writing functions and using modules.
Complete online Python tutorials.

Read 'Effective DevOps' by Jennifer Davis and Ryn Daniels

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Understand the core principles of DevOps to better grasp the MLOps concepts covered in the course.

View Effective DevOps on Amazon

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Read the book cover to cover.
Take notes on key concepts and practices.
Reflect on how these principles apply to machine learning.

Containerize a Simple ML Model with Docker

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Gain hands-on experience with Docker by containerizing a simple machine learning model.

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Choose a simple ML model (e.g., linear regression).
Write a Dockerfile to containerize the model.
Build and run the Docker image.
Test the containerized model.

Four other activities

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Read 'Designing Machine Learning Systems' by Chip Huyen

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Gain a deeper understanding of the architectural considerations for building scalable and reliable ML systems.

View AI Engineering: Building Applications with... on Amazon

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Read the book and take detailed notes.
Apply the concepts to your own ML projects.
Discuss the book with other MLOps practitioners.

Create a Blog Post on MLflow Experiment Tracking

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Solidify your understanding of MLflow by writing a blog post explaining its experiment tracking capabilities.

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Research MLflow experiment tracking features.
Write a clear and concise blog post.
Include code examples and screenshots.
Publish the blog post on a platform like Medium.

Build a CI/CD Pipeline with GitHub Actions

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Implement a CI/CD pipeline to automate testing and deployment of ML models using GitHub Actions.

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Set up a GitHub repository for your ML project.
Create a GitHub Actions workflow file.
Configure the pipeline to run tests and deploy the model.
Test the pipeline by making changes to the code.

Contribute to an Open Source MLOps Project

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Enhance your skills and contribute to the MLOps community by contributing to an open-source project.

Show steps

Find an open-source MLOps project on GitHub.
Identify an issue or feature to work on.
Submit a pull request with your changes.
Participate in code reviews and discussions.

Career center

Learners who complete Complete MLOps Bootcamp With 10+ End To End ML Projects will develop knowledge and skills that may be useful to these careers:

MLOps Engineer

An MLOps Engineer specializes in the automation and management of machine learning workflows. This course provides a comprehensive introduction to the field of MLOps, covering essential skills with tools such as Docker, MLflow, and Apache Airflow. The course's focus on practical, end-to-end projects helps build a strong foundation in MLOps, teaching how to deploy machine learning models to production. The use of GitHub Actions for CI/CD pipelines and monitoring using Grafana and PostgreSQL are vital components of the MLOps engineer's daily work. The course is specifically designed to equip learners with skills in this area and is an invaluable resource.

See salaries and explore the career path for MLOps Engineer

Machine Learning Engineer

A Machine Learning Engineer builds and maintains the infrastructure that makes machine learning models useful. This course is directly relevant to the role, as it provides experience with the tools and methods needed to deploy, monitor, and scale machine learning models. Specifically, the hands-on experience with Docker, MLflow, and AWS SageMaker is precisely the skill set needed for a Machine Learning Engineer to succeed at the task of implementing and automating the machine learning pipeline. The course's focus on real-world projects and end-to-end model deployment makes it a valuable experience for anyone looking to enter or advance in this field.

See salaries and explore the career path for Machine Learning Engineer

AI Platform Engineer

An AI Platform Engineer develops and maintains the platforms used to build, deploy, and manage AI models. This course teaches critical concepts and tools, such as Docker, MLflow, and AWS SageMaker, which are essential for creating robust and scalable machine learning platforms. The course's focus on hands-on experience with end-to-end projects makes it an ideal resource for aspiring AI Platform Engineers. They will learn how to automate workflows using tools like Apache Airflow and monitor systems using Grafana and PostgreSQL. The course provides the foundational knowledge needed for a successful role as an AI Platform Engineer.

See salaries and explore the career path for AI Platform Engineer

DevOps Engineer

A DevOps Engineer focuses on the automation and improvement of software delivery and infrastructure management. This course is highly useful, because it blends DevOps principles with a machine learning context. The curriculum's emphasis on CI/CD with GitHub Actions, containerization with Docker, and workflow automation with Apache Airflow mirrors the day to day work of a DevOps Engineer. This course provides hands-on examples of how to integrate machine learning pipelines into production environments, making it highly beneficial for those looking to expand their skills and take on roles that intersect with machine learning.

See salaries and explore the career path for DevOps Engineer

Data Engineering Manager

A Data Engineering Manager oversees teams that build and maintain data pipelines and infrastructure. While this role is managerial, the understanding of technologies and processes taught in the course is directly useful. The Data Engineering Manager will benefit from a firm understanding of tools like Docker, MLflow, and Airflow, which are common in modern data engineering pipelines and are covered in this course. The course’s emphasis on MLOps will inform the managerial decision making of a Data Engineering Manager, allowing for a streamlined approach to overseeing the implementation of machine learning systems. It will also help in communicating effectively with the engineers on the team.

See salaries and explore the career path for Data Engineering Manager

Cloud Engineer

A Cloud Engineer is responsible for designing, implementing, and managing cloud-based infrastructure and services. This course provides exposure to cloud technologies like AWS SageMaker and integrating cloud services with machine learning pipelines. This course would be helpful for a cloud engineer to better understand how to build scalable and cloud-based machine learning infrastructure. The hands-on experience with deploying and monitoring models in the cloud is a valuable skill set for a Cloud Engineer working with modern data-driven applications, particularly those in the field of AI.

See salaries and explore the career path for Cloud Engineer

AI Solutions Architect

An AI Solutions Architect designs and directs the implementation of AI and machine learning solutions for businesses. This course provides a strong practical foundation in MLOps, covering topics like model deployment, version control, and workflow automation, which are all critical for an AI Solutions Architect. The hands-on experience with tools like Docker, MLflow, and AWS SageMaker is essential for designing robust and scalable AI solutions. This course may be helpful in understanding the challenges faced during implementation and for designing realistic solutions.

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Solutions Engineer

A Solutions Engineer works with clients to design and implement technical solutions to their business problems. This course will be useful to a Solutions Engineer who works with clients looking to implement machine learning or AI related solutions. The course provides insight into the practical aspects of implementing MLOps, covering tools like Docker, MLflow, and SageMaker. The knowledge gained from this course would help a Solutions Engineer to better explain the benefits, challenges, and possible workflows of implementing machine learning, and thus provide a more complete client solution.

See salaries and explore the career path for Solutions Engineer

Software Engineer

A Software Engineer develops and maintains software applications, and this course can help those who want to expand their expertise into machine learning. The course introduces the software engineer to the end-to-end lifecycle of machine learning models. Practical experience with tools like Git, Docker, and CI/CD pipelines is directly relevant to a software engineer looking to integrate machine learning into their applications. The course gives the software engineer the knowledge and hands-on skills to build and deploy machine learning solutions as part of the software development process, and it may be useful to broaden their skill set.

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Data Scientist

A Data Scientist uses data to derive insights and build predictive models. While this role often focuses on model development, this course is useful for providing a complete view of the machine learning lifecycle. Through hands-on projects, Data Scientists learn how to deploy and scale their models using tools like Docker, MLflow, and AWS SageMaker. By incorporating MLOps principles taught in this course, a Data Scientist can improve the speed of getting their models deployed while ensuring that they are continuously monitored for optimal performance. This course may be helpful in rounding out the skill set of a data scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Consultant

A Machine Learning Consultant advises organizations on how to leverage machine learning to solve business problems. This course may be helpful to a Machine Learning Consultant by providing hands-on experience with end-to-end ML projects. A consultant will learn about the challenges and solutions involved in deploying and maintaining machine learning models. The course's focus on MLOps best practices, along with tools like Docker, MLflow and AWS SageMaker, are directly beneficial in providing informed recommendations. A consultant must be comfortable with practical application, and this course may help build that foundation.

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Product Manager

A Product Manager defines the vision and strategy for a product. This course may be helpful for a Product Manager who is building or maintaining a product that involves machine learning or AI. The Product Manager gets an end-to-end view of the processes and tools involved in the development, deployment and monitoring of machine learning models. This course will be useful to manage a product involving machine learning or to better communicate with the engineering teams involved in building this product. The course’s emphasis on real-world projects provides context to the Product Manager.

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Research Scientist

A Research Scientist conducts research to advance scientific knowledge, often in the field of machine learning. This course may be helpful for a Research Scientist who wants to understand the steps of developing, deploying and monitoring machine learning models in a production setting. The course's coverage of MLOps tools and techniques, such as experiment tracking with MLflow and data versioning with DVC, could be useful for the reproducibility of research results. However, since research is often focused on more novel model development, this course may be more supplemental.

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Data Analyst

A Data Analyst interprets data and communicates findings to inform business decisions. While this role is focused on analysis and reporting, a Data Analyst may also find this course helpful when they plan to build and deploy machine learning models. Skills acquired from this course provide a more holistic understanding of the data science pipeline. The course introduces MLOps concepts, and tools like Git, Docker, and CI/CD pipelines. This course may be useful for a Data Analyst looking to expand their skill set into the machine learning domain.

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Technical Project Manager

A Technical Project Manager oversees the planning, execution, and completion of technical projects, and an understanding of machine learning is a valuable skill. This course may be helpful so the technical project manager can understand the processes and tools involved in the development and deployment of machine learning models. The course covers the end-to-end lifecycle of machine learning projects. This gives the Technical Project Manager an informed view of the typical tasks, tools (like Docker, MLflow, and AWS services) and workflows involved in an MLOps setting and allows them to better manage projects.

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Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Complete MLOps Bootcamp With 10+ End To End ML Projects.

AI Engineering

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Provides a comprehensive guide to designing, building, and deploying production-ready machine learning systems. It covers various aspects of MLOps, including data engineering, model training, deployment, monitoring, and scaling. This book is commonly used as a reference by industry professionals. It adds more depth to the course by providing real-world examples and best practices.

AI Engineering: Building Applications with...

Kindle Edition

$$$

Effective DevOps

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Provides a comprehensive overview of DevOps principles and practices. It is helpful for understanding the cultural and organizational aspects of MLOps. While not directly focused on machine learning, it provides a solid foundation for understanding the DevOps mindset, which is crucial for successful MLOps implementation. It is more valuable as additional reading to provide context.

Complete MLOps Bootcamp With 10+ End To End ML Projects

What's inside

Learning objectives

Syllabus

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Activities

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