Sustainable & Scalable Machine Learning Project Development

Focused View

Kıvanç Yüksel

9:44:03

80 View

1. Introduction

1. Course Introduction.mp4

03:19

2. Why This Course.mp4

03:26

3.1 2020 state of enterprise machine learning.html

3.2 Machine Learning Engineering.html

3.3 MLOps - Motivation.html

3. Why Too Many Companies Fail.mp4

05:50

4. Why Too Many Companies Fail - Resources.html

5. What This Course is NOT About.mp4

01:18

6. Important Information.mp4

00:36

7. Where to start.html

2. Git and Github Quickstart

1. Git and Github Quickstart section introduction.mp4

00:44

2. Git and Github - What are they.mp4

00:23

3. Git Installation - Linux.mp4

02:50

4. Git Installation - Windows.mp4

02:40

5. Git Installation - MacOS.mp4

01:36

6. Github - Account creation.mp4

03:14

7. Adding an SSH key pair to GitHub account - Linux.mp4

04:02

8. Adding an SSH key pair to GitHub Account - MacOS.mp4

06:38

9. Git and GitHub - Basic workflow.mp4

12:41

10. Reverting Your Changes Back.mp4

03:15

11. Commit History.mp4

04:44

12. Aliases.mp4

02:57

13. Reverting Back to a Previous Commit.mp4

16:42

14. Git Diff.mp4

02:29

15. Branching and Merging.mp4

09:29

16. Pull Request and Code Review.mp4

07:07

17. Rebase.mp4

18:11

18. Stashing.mp4

07:09

19. Tagging.mp4

04:58

20. Cherry Pick.mp4

03:17

21. Git and GitHub - Final Words.mp4

00:20

3. Docker Quickstart

1. Docker Quickstart section introduction.mp4

02:55

2. What Is Docker and Why Do We Use It.mp4

01:37

3. Installation - Linux.mp4

04:50

4. Installation - Windows.mp4

03:13

5. Installation - MacOS.mp4

01:10

6. Docker Containers.mp4

02:19

7. Docker Containers - Hands On.mp4

13:57

8. Why Docker Is So Good.mp4

01:48

9. Docker Images.mp4

03:18

10. Dockerfile.mp4

10:14

11. More about Dockerfile.mp4

07:40

12. Persistent Data In Docker.mp4

01:16

13. Persistent Data In Docker - Volumes - Hands On.mp4

09:12

14. Persistent Data in Docker - Bind Mounting - Hands On.mp4

02:52

15. Docker Compose.mp4

02:47

16. Dockerfile Best Practices.mp4

03:12

4. DVC

1. DVC - Section Introduciton.mp4

00:19

2. Data Versioning.mp4

15:18

3. Accessing Your Data.mp4

08:17

4. Pipelines - Part 1.mp4

13:28

5. Pipelines - Part 2.mp4

10:20

6. Pipelines - Part 3.mp4

13:02

7. Metrics And Experiments.mp4

00:39

5. Hydra

1. Hydra - Section Introduction.mp4

01:16

2. How to Use Hydra From Command-Line.mp4

03:38

3. Specifying A Config File.mp4

03:17

4. More About OmegaConf.mp4

16:47

5. Grouping Config Files.mp4

07:18

6. Selecting Default Configs.mp4

07:48

7. Multirun.mp4

05:18

8. Output And Working Directory.mp4

05:01

9. Logging.mp4

03:37

10. Debugging.mp4

02:31

11. Tab Completion.mp4

01:40

12. Structured Configs.mp4

01:58

13. Structured Configs Basic Usage.mp4

04:02

14. Hierarchical Static Configuration.mp4

02:59

15. Config Groups in Structured Configs.mp4

04:06

16. Defaults List in Structured Configs.mp4

02:24

17. Structured Config Schema.mp4

08:05

6. Google Cloud Platform Quickstart

1. Google Cloud Platform - Section Introduction.mp4

01:10

2. How to Create An Account.mp4

02:03

3. How to Create a Project.mp4

02:01

4. gsutils and gcloud commands.mp4

02:50

5. Google Cloud Storage (GCS) - Bucket Creation.mp4

04:08

6. Google Cloud Storage (GCS) - Bucket Usage.mp4

05:32

7. Google Compute Engine (GCE).mp4

08:39

8. Google Compute Engine (GCE) - Quotas.mp4

03:52

7. Dask

1. Dask - Section Introduction.mp4

02:59

2. Dask DataFrame.mp4

02:52

3. Getting Started with Dask.mp4

12:09

4. Creating and Storing Dask DataFrames.mp4

06:16

5. Dask DataFrame - Best Practices.mp4

04:52

6. Shuffling for GroupBy and Join.mp4

02:09

7. Delayed.mp4

21:59

8. Futures.mp4

12:45

9. Scheduling.mp4

02:40

10. Deploying Clusters - Command Line.mp4

05:45

11. Deploying Clusters - Python API.mp4

03:12

8. Data Versioning With DVC

1. Prerequisites.html

2. GitHub Repository Creation.mp4

05:38

3. Specifying Python Dependencies.mp4

09:26

4. Dockerfile Creation.mp4

15:40

5. docker-compose File Creation.mp4

05:47

6. Makefile Creation.mp4

10:28

7. Datasets.mp4

07:50

8. Initializing DVC.mp4

11:44

9. Initializing DVC Storage.mp4

02:57

10. Setting Up Hydra Configuration.mp4

17:48

11. How To Update Python Dependencies.mp4

08:43

12. Data Versioning.mp4

13:51

13. Data Versioning - Creating A New Version.mp4

07:36

14. Data Versioning - Creating A New Version - Assignment.mp4

00:41

15. Data Versioning - Creating A New Version - Assignment Solution.mp4

01:16

16. Sorting - Formatting - Type Checking.mp4

11:22

Description

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

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

Who is this for?

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

More details

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!
Who this course is for:
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

User Reviews

Rating

average 0

Total votes0

Focused display

I have always been interested in machines and software. One of the earliest memories I can remember is me building imaginary machines with big building blocks in the kindergarten I was attending. My interest grew with me. This is the reason why I choose to study Electrical and Electronics Engineering in my Bachalor's degree. Later on, I focused mainly on Computer Vision applications during my Master's degree. During my studies I published 4 papers and graduated with a very good GPA. Right after my studies, I started to work as a Machine Learning Engineer. Since then, I have worked on many different Computer Vision, NLP, and audio processing applications which have been used by many people. I have experience both in academia as a Machine Learning Researcher, and in industry as a Machine Learning Engineer. Therefore I know how to combine theory and practice in a well suited way. One of the biggest issues I had in my studies was that most of the instructors just didn't care enough about the lectures, and they didn't care about whether or not the students were getting what they were talking about. I suffered from that a lot, and my intention is to make my courses as clear and detailed as possible, and my goal is to make everyone truly understand the content of my lectures.

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.