Practical MLOps for Data Scientists & DevOps Engineers - AWS

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Manifold AI Learning ®

23:57:04

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1. About AWS MLOps Course and Instructor

1. About the MLOps with AWS Course.mp4

02:41

2. How to make the most of this course.mp4

01:37

3. Source Code of this course.html

2. Introduction to MLOps

1. What & Why MLOps.mp4

16:06

2. Quick Hands On Demo on MLOps.mp4

05:30

3. MLOps Fundamentals.mp4

10:57

4. MLOps Fundamentals - Deep Dive.mp4

13:19

5. Why DevOps alone is not Suitable for Machine Learning .mp4

06:20

6. What is AWS & its Benefits.mp4

13:44

7. Technical Stack of AWS for MLOps & Machine Learning.mp4

05:57

3. DevOps for Data Scientists

1. What is SDLC & Why its Important.mp4

09:24

2. Types of SDLC.mp4

06:33

3. Waterfall Vs Agile Vs DevOps.mp4

08:58

4. DevOps Lifecycle & Tools in AWS.mp4

12:22

4. Getting Started with AWS

1. What do we cover in this section .mp4

01:52

2. Create AWS Account.mp4

04:18

3. Setting up MFA on Root Account.mp4

08:09

4. Create IAM Account and Account Alias.mp4

07:08

5. Setup CLI with Credentials.mp4

04:48

6. IAM Policy.mp4

02:42

7. IAM Policy generator & attachment.mp4

07:44

8. Delete the IAM User.mp4

01:11

9. S3 Bucket and Storage Classes.mp4

14:39

10. Creation of S3 Bucket from Console.mp4

07:50

11. Creation of S3 Bucket from CLI.mp4

04:52

12. Version Enablement in S3.mp4

06:17

13. Introduction EC2 instances.mp4

04:21

14. Launch EC2 instance & SSH into EC2 Instances.mp4

08:40

15. Clean Up Activity.mp4

00:49

5. Linux Operating System for DevOps and Data Scientists

1. What do we learn in this section .mp4

01:37

2. Linux Features & Bash.mp4

20:38

3. How to Launch EC2 Instances (Quick Refresh).mp4

06:21

4. Linux Basic Commands.mp4

01:37:19

6. Source code Management using GIT - CodeCommit

1. Introduction to CI CD Pipeline.mp4

11:14

2. Introduction to AWS Code Commit & DVCS.mp4

17:39

3. Git Initial config & Git Commands.mp4

06:16

4. Setting up the workspace for Git.mp4

15:35

5. Git Workflow.mp4

14:21

6. Adding files to Staging Area.mp4

01:58

7. Staged Differences.mp4

07:27

8. Git Unstage.mp4

11:50

9. Git Reset & Revert.mp4

28:37

10. AWS Code Commit Remote Git Commands.mp4

03:20

11. Cloning and Branching.mp4

03:10

12. Git Branching Hands On Part 1.mp4

24:42

13. Git Branching Hands On Part 2.mp4

16:37

14. Git Conflicts & Resolving them.mp4

16:10

15. Git Rebase Vs Git Merge.mp4

39:45

16. Git Stash Introduction.mp4

07:32

17. Git Stash Hands On.mp4

18:53

18. AWS Code Commit Security.mp4

04:38

19. AWS Code Commit Security - Hands On.mp4

08:09

20. AWS Code Commit Integration - Triggers - Notifications - CloudWatch - EventBridg.mp4

11:39

21. Summary.mp4

01:23

7. YAML Crash Course

1. YAML Crash Course.mp4

22:01

8. AWS CodeBuild

1. Introduction to AWS CodeBuild.mp4

01:59

2. Create First CodeBuild Project.mp4

15:04

3. buildspec.yml deep dive.mp4

21:30

4. Code Build Hands On.mp4

06:07

5. Environment Variables in CodeBuild & buildspec.yml deep dive Hands On.mp4

20:41

6. Working CodeBuild Artifacts Hands On.mp4

08:16

7. AWS CodeBuild Triggers.mp4

15:17

8. CleanUp Activity.mp4

01:51

9. AWS Code Deploy

1. AWS CodeDeploy Introduction.mp4

13:09

2. First AWS CodeDeploy - Intro to Hands On.mp4

09:00

3. First AWS CodeDeploy.mp4

25:00

4. appspec.yml - Deep Dive.mp4

16:59

5. CodeDeploy Summary.mp4

01:35

10. Code Pipeline

1. AWS CodePipeline Introduction.mp4

04:25

2. Create CodePepeline - Hands On.mp4

22:11

3. Automatic CI CD Process with Manual Approval.mp4

16:56

4. Summary & CleanUp.mp4

04:19

11. Docker Containers

1. Introduction to Docker.mp4

19:05

2. Installation of Docker Desktop.mp4

02:59

3. Docker Basics.mp4

15:17

4. Pull the image from Docker Registry.mp4

11:25

5. Dockerfile.mp4

13:33

6. Push the Docker Image to ECR.mp4

04:43

7. Hands On - Amazon ECR for AWS CodeBuild.mp4

17:33

8. Summary.mp4

01:31

12. Practical MLOps - Amazon Sagemaker

1. What is AWS Sagemaker .mp4

05:27

2. Why Sagemaker is the most preferred tool.mp4

03:48

3. Setting Up the Sagemaker Studio.mp4

06:24

4. CleanUp Activity.mp4

01:16

13. Feature Engineering - Feature Store in Sagemaker

1. What is Feature Engineering.mp4

07:00

2. Data Wrangler Setup.mp4

11:19

3. Data Quality and Insights Report.mp4

09:02

4. Univariate Analysis & Bias Report.mp4

08:52

5. Target Leakage.mp4

03:52

6. Data Transformation.mp4

15:37

7. Data Transformation - Custom Script.mp4

08:10

8. Export to S3.mp4

10:09

9. Export to Sagemaker Feature Store.mp4

18:39

10. Create DataFrame using Feature Store.mp4

14:58

11. Feature Engineering on Sagemaker Notebook Instance.mp4

06:18

12. Feature Engineering with Sagemaker Processing.mp4

15:59

13. Summary.mp4

02:34

14. Training, Tuning & Deploying the Model

1. Training the xgboost.mp4

06:36

2. Deploy the Model.mp4

07:44

3. Create End Point and End Point Configuration.mp4

09:11

4. Automatic Model Tuning.mp4

10:43

15. Create Custom Models

1. Introduction to Bring own Training Script.mp4

02:07

2. Use Custom Model created with Tensorflow.mp4

11:19

3. Use Custom Model created with Pytorch.mp4

09:06

4. Use Custom Model created with sklearn.mp4

30:40

16. MLOps Sagemaker Pipelines

1. Sagemaker Pipelines Introduction.mp4

01:49

2. Sagemaker Training Pipeline.mp4

28:32

3. Sagemaker Inference Pipeline.mp4

16:47

4. Advanced MLOps pipeline.mp4

03:59

5. Architecture Overview.mp4

11:18

6. System Setup for Cloud9.mp4

20:06

7. Create Data Repository for MLOps.mp4

11:31

8. Pipeline Assets Introduction.mp4

13:46

9. Push ETL Assets to CodeCommit.mp4

06:22

10. Training and Inference test Asset.mp4

14:27

11. Run Unit Test on Train & Predict.mp4

10:38

12. System Test Asets.mp4

15:51

13. Quick Summary on Assets.mp4

01:16

14. Working with Pipeline components.mp4

05:29

15. Create MLOps Pipeline.mp4

06:17

16. Execution of MLOps Pipeline.mp4

26:35

17. Invoke Load Simulation test.mp4

03:35

18. Generate Visualization with Cloud watch logs.mp4

05:06

19. Data Quality Drift, Baseline, Inference Data.mp4

07:23

20. CleanUp.mp4

03:43

17. References (V2)

1. AWS CodeDeploy Introduction.mp4

24:53

2. AWS CodeDeploy Hands On.mp4

38:14

3. Appspec.yml Deep Dive.mp4

14:33

Description

Practical MLOps for Data Scientists , Machine Learning & DevOps Engineers - Implement MLOps - Deploy Models and Operate

What You'll Learn?

Configuring the CI/CD Pipeline for Machine Learning Projects
Ability to track the source code & training images, configuration files with Git Based Repository – AWS CodeCommit
Ability to Perform the Build using AWS CodeBuild
Ability to Deploy the Application on Server using AWS CodeDeploy
Orchestrate the MLOps steps using AWS CodePipeline
Identify appropriate AWS services to implement ML solutions
Perform the Load testing
Monitoring the End Point Performance
Monitoring the Model Drift
The ability to follow model-training best practices
The ability to follow deployment best practices
The ability to follow operational best practices

Who is this for?

Anyone preparing for Data Science , Machine Learning & Deep Learning Interviews

Anyone interested in learning how Machine Learning is implemented on Large scale data

Anyone interested in AWS cloud-based machine learning and data science

Anyone looking to learn the best practices to deploy the Machine Learning Models on Cloud

Anyone looking to learn the best practices to Operationalize the Machine Learning Models

What You Need to Know?

Basic knowledge of AWS

Account with AWS for practical Hand-On

Basic knowledge of Machine Learning & Deep Learning

More details

Description
This course - Practical MLOps for Data Scientists & DevOps Engineers with AWS is intended for individuals who wants to perform an artificial intelligence/machine learning (AI/ML) development or data science role as close to Production Level working. This course helps you in improving your ability to design, build, deploy, optimize, train, tune, and maintain ML solutions for given business problems by using the AWS Cloud with Practices of DevOps for Machine Learning .

Right now, you may be aware of basics of Machine learning, but skills expected by employer is â€“ more than what you can run from local notebook.

From Employer perspective, its expected that Candidates to have :
Â· The ability to follow model-training best practices on Large Datasets on cloud
Â· The ability to follow deployment best practices so that it will work always
Â· The ability to follow operational best practices so that there will be Zero downtime

In short, you are expected to solve the Business problem by implementing on the dataset, not just work on the personal laptop.

In this learning journey of this course, we will follow the structured learning journey, which takes you in a logical way to understand the topics in a clear and detailed manner with relevant Practical Exercises/Demo.

The course structure is as follows:
Section 1 : About AWSMLOPS Course and Instructor
Section 2 : Introduction to MLOps
Section 3 : DevOps for Data Scientists
Section 4: Getting Started with AWS
Section 5: Linux Basics for MLOps
Section 6: Source code Management using GIT - AWS CodeCommit
Section 7: Crash Course on YAML
Section 8: AWS CodeBuild
Section 9: AWS CodeDeploy
Section 10: AWS CodePipeline
Section 11 : Docker Containers
Section 12 : Practical MLOps - Amazon Sagemaker
Section 13 : Feature Engineering - Feature Store in Sagemaker
Section 14: Training, Tuning & Deploying the Model
Section 15 : Create Custom Models
Section 16 : MLOps Sagemaker Pipelines

All the source code is shared on github, which ensures that- you get to access from anywhere and always have the latest version.

Below are the Tools, Technologies and Concepts covered as part of this Course:

Â· Ingestion/Collection
Â· Processing/ETL
Â· Data analysis/visualization
Â· Model training
Â· Model deployment/inference
Â· Operational Aspectes
Â· AWS ML application services
Â· Notebooks and integrated development environments (IDEs)
Â· AWS CodeCommit
Â· Amazon Athena
Â· AWS Batch
Â· Amazon EC2
Â· Amazon Elastic Container Registry (Amazon ECR)
Â· AWS Glue
Â· Amazon SageMaker
Â· Amazon CloudWatch
Â· AWS Lambda
Â· Amazon S3
Who this course is for:
Anyone preparing for Data Science , Machine Learning & Deep Learning Interviews
Anyone interested in learning how Machine Learning is implemented on Large scale data
Anyone interested in AWS cloud-based machine learning and data science
Anyone looking to learn the best practices to deploy the Machine Learning Models on Cloud
Anyone looking to learn the best practices to Operationalize the Machine Learning Models

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Manifold AI Learning ® is an online Academy with the goal to empower the students with the knowledge and skills that can be directly applied to solving the Real world problems in Data Science, Machine Learning and Artificial intelligence.Checkout our instructor profile for the complete list of courses.All the best for your Learning.- Team ManifoldAILearning ®"Learn the Future"

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