Sustainable & Scalable Machine Learning Project Development

Learn, hands-on, how to build and manage Machine Learning Systems

What you will learn

How To Efficiently Build Sustainable And Scalable Machine Learning Projects Using The Best Practices

Data Versioning

Distributed Data Processing

Feature Extraction

Distributed Model Training

Model Evaluation

Experiment Tracking

Error analysis

Model Inference

Creating An Application Using The Model We Train

Metadata management



Are you ready to take your Machine Learning skills to the next level and develop projects that have real-world impact and are sustainable for the future? Look no further! This course is designed to give you the comprehensive knowledge and hands-on experience you need to design, build and maintain successful Machine Learning projects at scale.

In this course, you will learn how to tackle the most pressing challenges faced by ML professionals today, such as handling increasing amounts of data and ensuring that model and project development are both scalable and sustainable in the long run. Throughout the course, you will gain hands-on experience with the latest ideas and techniques used by top ML practitioners, and learn how to apply these techniques to real-world projects. From data versioning and data pre-processing, to model training, evaluation and versioning, you will acquire a deep understanding of each stage of the ML project development process.

You will also delve into the practical aspects of building scalable and sustainable ML projects, including designing robust pipelines and workflows. Throughout the course, you will work on a real-world project that will put your knowledge to test, and you will receive feedback and guidance from an experienced instructor who has worked on large-scale ML projects in the industry. You will also learn how to work with cloud-based ML infrastructure to ensure your projects are easily scalable. By the end of the course, you will have a powerful completed project in your portfolio that showcase your skills and demonstrate your ability to build and maintain scalable and sustainable ML solutions.


In this course, a strong emphasis is placed on sustainability, helping you avoid common pitfalls and ensuring that your projects can handle growing complexity, while remaining scalable and efficient in the long run. You will learn how to design projects that are robust and adaptable, and how to ensure that they will continue to provide value even as the industry evolves.

Join us today and become part of a vibrant community of ML professionals, through our chat platform (Slack), who are driving innovation and change in the industry. By the end of the course, you will have the confidence and skills needed to turn your ideas into successful and scalable ML solutions. Start your journey towards becoming a top ML professional!


Course Introduction
Why This Course?
Why Too Many Companies Fail?
Why Too Many Companies Fail – Resources
What This Course is NOT About?
Important Information
Discord Server
Where to start?

Git and Github Quickstart

Git and Github Quickstart section introduction
Git and Github – What are they?
Git Installation – Linux
Git Installation – Windows
Git Installation – MacOS
Github – Account creation
Adding an SSH key pair to GitHub account – Linux
Adding an SSH key pair to GitHub Account – MacOS
Git and GitHub – Basic workflow
Reverting Your Changes Back
Commit History
Reverting Back to a Previous Commit
Git Diff
Branching and Merging
Pull Request and Code Review
Cherry Pick
Git and GitHub – Final Words

Docker Quickstart

Docker Quickstart section introduction
What Is Docker and Why Do We Use It?
Installation – Linux
Installation – Windows
Installation – MacOS
Docker Containers
Docker Containers – Hands On
Why Docker Is So Good?
Docker Images
More about Dockerfile
Persistent Data In Docker
Persistent Data In Docker – Volumes – Hands On
Persistent Data in Docker – Bind Mounting – Hands On
Docker Compose
Dockerfile Best Practices


DVC – Section Introduciton
Data Versioning
Accessing Your Data
Pipelines – Part 1
Pipelines – Part 2
Pipelines – Part 3
Metrics And Experiments


Hydra – Section Introduction
How to Use Hydra From Command-Line?
Specifying A Config File
More About OmegaConf
Grouping Config Files
Selecting Default Configs
Output And Working Directory
Tab Completion
Structured Configs
Structured Configs Basic Usage
Hierarchical Static Configuration
Config Groups in Structured Configs
Defaults List in Structured Configs
Structured Config Schema

Google Cloud Platform Quickstart

Google Cloud Platform – Section Introduction
How to Create An Account?
How to Create a Project?
“gsutils” and “gcloud” commands
Google Cloud Storage (GCS) – Bucket Creation
Google Cloud Storage (GCS) – Bucket Usage
Google Compute Engine (GCE)
Google Compute Engine (GCE) – Quotas


Dask – Section Introduction
Dask DataFrame
Getting Started with Dask
Creating and Storing Dask DataFrames
Dask DataFrame – Best Practices
Shuffling for GroupBy and Join
Deploying Clusters – Command Line
Deploying Clusters – Python API

Data Versioning With DVC

GitHub Repository Creation
Specifying Python Dependencies
Dockerfile Creation
docker-compose File Creation
Makefile Creation
Initializing DVC
Initializing DVC Storage
Setting Up Hydra Configuration
How To Update Python Dependencies?
Data Versioning
Data Versioning – Creating A New Version
Data Versioning – Creating A New Version – Assignment
Data Versioning – Creating A New Version – Assignment Solution
Sorting – Formatting – Type Checking

Ads Blocker Image Powered by Code Help Pro

Ads Blocker Detected!!!

We have detected that you are using extensions to block ads. Please support us by disabling these ads blocker.

Powered By
100% Free SEO Tools - Tool Kits PRO

Check Today's 30+ Free Courses on Telegram!