Sustainable Scalable Machine Learning Project Development

Learn, hands-on, how to build and manage Machine Learning Systems
Sustainable Scalable Machine Learning Project Development
File Size :
3.73 GB
Total length :
9h 45m



Kıvanç Yüksel


Last update




Sustainable Scalable Machine Learning Project Development

What you’ll 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

Sustainable Scalable Machine Learning Project Development


Basic Understanding Of Machine Learning
Python Programming Language
You Will Learn The Rest In The Course


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!


Section 1: Introduction

Lecture 1 Course Introduction

Lecture 2 Why This Course?

Lecture 3 Why Too Many Companies Fail?

Lecture 4 Why Too Many Companies Fail – Resources

Lecture 5 What This Course is NOT About?

Lecture 6 Important Information

Lecture 7 Where to start?

Section 2: Git and Github Quickstart

Lecture 8 Git and Github Quickstart section introduction

Lecture 9 Git and Github – What are they?

Lecture 10 Git Installation – Linux

Lecture 11 Git Installation – Windows

Lecture 12 Git Installation – MacOS

Lecture 13 Github – Account creation

Lecture 14 Adding an SSH key pair to GitHub account – Linux

Lecture 15 Adding an SSH key pair to GitHub Account – MacOS

Lecture 16 Git and GitHub – Basic workflow

Lecture 17 Reverting Your Changes Back

Lecture 18 Commit History

Lecture 19 Aliases

Lecture 20 Reverting Back to a Previous Commit

Lecture 21 Git Diff

Lecture 22 Branching and Merging

Lecture 23 Pull Request and Code Review

Lecture 24 Rebase

Lecture 25 Stashing

Lecture 26 Tagging

Lecture 27 Cherry Pick

Lecture 28 Git and GitHub – Final Words

Section 3: Docker Quickstart

Lecture 29 Docker Quickstart section introduction

Lecture 30 What Is Docker and Why Do We Use It?

Lecture 31 Installation – Linux

Lecture 32 Installation – Windows

Lecture 33 Installation – MacOS

Lecture 34 Docker Containers

Lecture 35 Docker Containers – Hands On

Lecture 36 Why Docker Is So Good?

Lecture 37 Docker Images

Lecture 38 Dockerfile

Lecture 39 More about Dockerfile

Lecture 40 Persistent Data In Docker

Lecture 41 Persistent Data In Docker – Volumes – Hands On

Lecture 42 Persistent Data in Docker – Bind Mounting – Hands On

Lecture 43 Docker Compose

Lecture 44 Dockerfile Best Practices

Section 4: DVC

Lecture 45 DVC – Section Introduciton

Lecture 46 Data Versioning

Lecture 47 Accessing Your Data

Lecture 48 Pipelines – Part 1

Lecture 49 Pipelines – Part 2

Lecture 50 Pipelines – Part 3

Lecture 51 Metrics And Experiments

Section 5: Hydra

Lecture 52 Hydra – Section Introduction

Lecture 53 How to Use Hydra From Command-Line?

Lecture 54 Specifying A Config File

Lecture 55 More About OmegaConf

Lecture 56 Grouping Config Files

Lecture 57 Selecting Default Configs

Lecture 58 Multirun

Lecture 59 Output And Working Directory

Lecture 60 Logging

Lecture 61 Debugging

Lecture 62 Tab Completion

Lecture 63 Structured Configs

Lecture 64 Structured Configs Basic Usage

Lecture 65 Hierarchical Static Configuration

Lecture 66 Config Groups in Structured Configs

Lecture 67 Defaults List in Structured Configs

Lecture 68 Structured Config Schema

Section 6: Google Cloud Platform Quickstart

Lecture 69 Google Cloud Platform – Section Introduction

Lecture 70 How to Create An Account?

Lecture 71 How to Create a Project?

Lecture 72 “gsutils” and “gcloud” commands

Lecture 73 Google Cloud Storage (GCS) – Bucket Creation

Lecture 74 Google Cloud Storage (GCS) – Bucket Usage

Lecture 75 Google Compute Engine (GCE)

Lecture 76 Google Compute Engine (GCE) – Quotas

Section 7: Dask

Lecture 77 Dask – Section Introduction

Lecture 78 Dask DataFrame

Lecture 79 Getting Started with Dask

Lecture 80 Creating and Storing Dask DataFrames

Lecture 81 Dask DataFrame – Best Practices

Lecture 82 Shuffling for GroupBy and Join

Lecture 83 Delayed

Lecture 84 Futures

Lecture 85 Scheduling

Lecture 86 Deploying Clusters – Command Line

Lecture 87 Deploying Clusters – Python API

Section 8: Data Versioning With DVC

Lecture 88 Prerequisites

Lecture 89 GitHub Repository Creation

Lecture 90 Specifying Python Dependencies

Lecture 91 Dockerfile Creation

Lecture 92 docker-compose File Creation

Lecture 93 Makefile Creation

Lecture 94 Datasets

Lecture 95 Initializing DVC

Lecture 96 Initializing DVC Storage

Lecture 97 Setting Up Hydra Configuration

Lecture 98 How To Update Python Dependencies?

Lecture 99 Data Versioning

Lecture 100 Data Versioning – Creating A New Version

Lecture 101 Data Versioning – Creating A New Version – Assignment

Lecture 102 Data Versioning – Creating A New Version – Assignment Solution

Lecture 103 Sorting – Formatting – Type Checking

Students who are interested in pursuing a career in machine learning project development and want to gain expertise in sustainable and scalable development practices,Machine learning engineers who are interested in developing machine learning solutions that are scalable and sustainable in the long run,Data scientists who are looking to expand their skill set to include machine learning project development that is scalable and sustainable,Researchers who are interested in developing machine learning models more efficiently,Software developers who want to gain expertise in developing sustainable and scalable machine learning projects,Start-up founders who want to develop machine learning projects that can be scaled up to meet future demands while also being sustainable,Technical project managers who want to learn how to effectively manage and oversee sustainable and scalable machine learning projects,Professionals in the technology industry who want to stay up-to-date with the latest trends and advancements in machine learning project development,Companies and organizations that want to implement sustainable and scalable machine learning projects to improve their operations, efficiency, and profitability

Course Information:

Udemy | English | 9h 45m | 3.73 GB
Created by: Kıvanç Yüksel

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