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
Reproducibility
Requirements
Basic Understanding Of Machine Learning
Python Programming Language
You Will Learn The Rest In The Course
Description
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!
Overview
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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