MLOps Bootcamp: Mastering AI Operations for Success - AIOps

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Manifold AI Learning ®

36:55:39

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1. Introduction to Complete MLOps Bootcamp

1. What and Why MLOps.mp4

04:20

2. The Stages of MLOps.mp4

07:04

2. Python for MLOps

1. About the Section.mp4

01:47

2. Python Quiz.html

3. Introduction to Python Programming.mp4

05:46

4. Install Anaconda.mp4

02:11

5. Hello World - Python.mp4

04:12

6. Jupyter Lab Quick Tour.mp4

06:04

7. Variables in Python.mp4

03:53

8. Variables - Comments - Markdown Cells - Hands On.mp4

10:27

9. Python Literals - Hands On.mp4

10:16

10. Operators in Python Programming Language.mp4

10:06

11. Collection - Strings.mp4

13:00

12. Python String - Builtin Functions - Hands On.mp4

04:13

13. Data Structures - List.mp4

08:06

14. Data Structures - Tuples.mp4

04:14

15. Data Structures - Dictionary.mp4

05:01

16. Data Structures - Sets.mp4

03:47

17. Explicit and Implicit Casting in Python Programming.mp4

04:41

18. Reading the Input from Keyboard.mp4

03:32

19. String Formatting.mp4

04:58

20. Control Statements - Conditional Statements in Python.mp4

05:44

21. Control Statements - Looping Statements.mp4

12:11

22. List comprehension.mp4

04:08

23. Functions.mp4

09:55

24. Modules in Python.mp4

04:59

25. Classes in Python.mp4

07:04

26. File Handling in Python.mp4

07:28

27. Working with Python Scripts.mp4

02:36

28. Libraries in Python.mp4

02:35

3. Git and Github Fundamentals for MLOps

1. Introduction to Version Control Systems.mp4

10:19

2. Getting Started with git.mp4

05:42

3. Local Repo vs Remote Repo.mp4

10:25

4. Git Configurations.mp4

05:42

5. Getting Started with Local Repo.mp4

08:02

6. Concept of Working Directory - Staging Area - Commit.mp4

06:50

7. Git Workflow - Local Repo.mp4

12:01

8. Git Branch.mp4

13:18

9. Switching the Branches.mp4

08:32

10. Merging.mp4

08:43

11. Checking Out Commits.mp4

08:31

12. Git Hosting Services.mp4

05:24

13. Working with Remote Repositories.mp4

12:35

14. Cloning and Delete Branches.mp4

07:20

15. 3 way merge.mp4

10:20

16. Summary.mp4

02:41

4. Crash Course on YAML

1. YAML Crash Course.mp4

21:44

5. Packaging the ML Models

1. Introduction to Packaging the ML Models.mp4

05:29

2. Typical Experimentation with Dataset.mp4

28:18

3. Model fit and generate Predictions.mp4

04:26

4. Challenges in Working inside the Jupyter Notebook.mp4

23:33

5. Understanding the Modular Programming.mp4

18:45

6. Creating Folder Hierarchy for ML Project.mp4

17:13

7. Create Config Module.mp4

20:33

8. Data Handling Module.mp4

08:05

9. Data Preprocessing part 1.mp4

24:20

10. Data Preprocessing part 2.mp4

03:06

11. sklearn pipeline.mp4

12:13

12. Training Pipeline.mp4

10:13

13. Prediction Pipeline.mp4

08:15

14. Fixes on Python Scripts.mp4

03:29

15. Add Python Path to MacOS.mp4

03:26

16. Perform Training and Predictions.mp4

03:22

17. Requirements txt file.mp4

06:02

18. Testing the New Virtual Environments.mp4

05:04

19. Create Python tests.mp4

14:38

20. Running Pytest.mp4

06:52

21. Create Manifest file.mp4

05:20

22. Create Version File.mp4

03:20

23. Create setup.py.mp4

08:32

24. Packagiing the ML Model & testing.mp4

15:49

25. Summary.mp4

05:09

6. Mlflow - Manage ML experiments

1. Introduction to Mlflow.mp4

11:09

2. Getting System Ready with mlflow.mp4

06:06

3. Logging Functions of Mlflow Tracking.mp4

11:40

4. Basic Mlflow tutorial.mp4

18:40

5. Exploration of mlflow.mp4

07:45

6. Machine Learning Experiement on MLFlow.mp4

19:56

7. Create ML Model for Loan Prediction.mp4

11:05

8. MLFlow Project.mp4

20:06

9. MLFlow Models.mp4

15:51

10. Setting Up MySql Database Locally.mp4

07:42

11. Log Model Metrics in MySql.mp4

16:28

12. Register the Model & Serve the Model.mp4

17:17

13. Summary.mp4

02:06

7. Docker for Machine Learning

1. Docker for Machine Learning.mp4

04:04

2. Introduction to Docker.mp4

27:14

3. Installation of Docker Desktop.mp4

05:24

4. Working with Docker.mp4

18:38

5. Running the Docker Container.mp4

09:42

6. Working with Dockerfile.mp4

11:41

7. Push the Docker Image to DockerHub.mp4

03:02

8. Dockerize the ML Model.mp4

10:35

9. Packaging the training code in Docker Environment & Summary.mp4

08:21

8. Build MLApps using FastAPI

1. What is API, REST and REST API.mp4

06:37

2. How REST API Works .mp4

12:23

3. What is FastAPI.mp4

04:50

4. Crash course on FastAPI.mp4

23:15

5. Data Validation with Pydantic.mp4

06:27

6. Deploying the Machine Learning Model with FastAPI.mp4

08:38

9. Build MLApps using Streamlit

1. Introduction to Streamit.mp4

03:45

2. Hands On Working with Streamlit.mp4

18:35

3. Building the ML Model with Streamlit.mp4

34:21

10. Lnux Operating System for DevOps and Data Scientists

1. Agenda of this section.mp4

01:48

2. Linux Features & Bash.mp4

20:38

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

06:21

4. Basic Linux Commands of Linux.mp4

01:37:19

11. Working with CI CD Tool Jenkins

1. Introduction to Jenkins.mp4

15:35

2. How do we Use Jenkins in MLOps.mp4

04:39

3. Prepare and Package ML Model.mp4

08:20

4. Deploy as API with FASTAPI.mp4

18:42

5. Test FastAPI App.mp4

09:53

6. Create Dockerfile.mp4

06:15

7. Exposing the Application Port as per Dockerfile.mp4

01:37

8. Test Locally using Docker Containers.mp4

11:17

9. Installation of Jenkins on AWS EC2 Instances.mp4

11:16

10. Installation of Docker in EC2 Instance.mp4

05:33

11. Configure Github Repo - Webhook - Jenkins Credentials.mp4

18:05

12. Introduction to Jenkins FreeStyle Projects and Pipeline Jobs.mp4

03:43

13. Exploration of Jenkins UI.mp4

03:18

14. Create your first First Jenkins Project.mp4

05:19

15. Test Github Webhook with Jenkins.mp4

15:23

16. Installation of Docker Plugin & System Readiness.mp4

10:37

17. Setup Email Notification with Gmail.mp4

12:59

18. Introduction to CI CT CD Pipeline.mp4

02:09

19. Create CI CT CD Pipeline - Github Dockerhub.mp4

15:08

20. Create CI CT CD Pipeline - Training.mp4

07:48

21. Create CI CT CD Pipeline - Testing.mp4

05:33

22. Create CI CT CD Pipeline - Deployment.mp4

05:48

23. Perform Test of Pipeline.mp4

02:47

24. Summary.mp4

05:46

12. Monitoring and Debugging of ML System

1. Why Monitoring Machine Learning Models is Important.mp4

04:00

2. What is Monitoring of ML models & When to Update Model in Production.mp4

03:07

3. Why Monitoring Machine Learning Models is Difficult.mp4

11:37

4. Challenge - Who Owns what .mp4

05:06

5. Functional Level Monitoring.mp4

13:01

6. Model Drift.mp4

09:56

7. Operational Level Monitoring.mp4

03:10

8. Tools and Best Practices of Machine Learning Model Monitoring.mp4

03:02

13. Continuous Monitoring with Prometheus

1. Introduction to Continuous Monitoring.mp4

08:21

2. Use case on Continuous Monitoring.mp4

03:40

3. Introduction to Prometheus.mp4

04:28

4. Architecture of Prometheus.mp4

11:38

5. Metric Types of Prometheus.mp4

03:49

6. Installation of Prometheus.mp4

14:06

7. Introduction Grafana.mp4

02:02

8. Installation of Grafana.mp4

04:54

9. Prometheus Configuration file.mp4

07:12

10. Exploring the Basic Querying Prometheus.mp4

08:04

11. Monitor the Infrastructure with Prometheus.mp4

02:45

12. Monitor the Linux Server with Node Exporter.mp4

10:07

13. Monitor the Client Application using Prometheus.mp4

04:20

14. Monitor the FastAPI Application using Prometheus.mp4

10:09

15. Monitor All EndPoints using Prometheus.mp4

07:29

16. Create Visualization with Grafana.mp4

18:00

17. Trigger Alerts with Grafana.mp4

13:46

14. Deploy Applications with Docker Compose

1. Introduction to Docker Compose.mp4

03:55

2. Hands On - Docker Compose with Flask Application.mp4

22:02

3. Hands On - Docker Compose Prometheus Grafana.mp4

15:56

15. Continuous Monitoring of Machine Learning Application

1. Architecture of ML Application Monitoring.mp4

05:19

2. Hands On Monitoring of ML Application using Prometheus.mp4

15:07

16. Monitor the ML System with WhyLogs

1. Introduction to ML Monitoring.mp4

12:13

2. Setting Up WhyLabs.mp4

01:37

3. Whylogs - Drift Detection, Input, Output, Bias Monitoring.mp4

47:29

4. WhyLogs - Constraints and Drift Reports.mp4

10:25

5. Summary.mp4

02:30

17. Post Productionizing ML Models

1. Post-Productionalizing ML Models - What Next .mp4

04:56

2. Model Security.mp4

02:17

3. Adversarial Attack.mp4

03:12

4. Data Poisoning Attack.mp4

01:02

5. Distributed Denial of Service Attack (DDOS).mp4

00:57

6. Data Privacy Attack.mp4

01:38

7. How to Mitigate Risk of Model Attacks.mp4

03:07

8. AB Testing.mp4

03:58

9. Future of MLOps.mp4

03:22

18. Reference 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

19. Python for Data Science - Numpy - Pandas - Matplotlib - (Optional Section)

1. Introduction to Numpy Library.mp4

06:39

2. Basics of numpy array object.mp4

03:41

3. Import Numpy & Access help.mp4

04:49

4. Creation of Array Object - np.array().mp4

04:47

5. Attributes of Numpy Array.mp4

03:38

6. Array Indexing and Slicing.mp4

09:26

7. Array Creation Functions.mp4

10:46

8. Copy Arrays.mp4

04:45

9. Mathematical Operation on Numpy Arrays.mp4

04:11

10. Linear Algebra Functions in Numpy.mp4

03:20

11. Shape Modification of Arrays.mp4

09:37

12. np.arange().mp4

03:54

13. Relational Operators & Aggregation Functions on Numpy Arrays.mp4

06:35

14. Boolean Masking.mp4

02:06

15. Broadcasting on Numpy Arrays.mp4

18:13

16. Summary of Numpy Library Journey.mp4

03:29

17. Introduction to Pandas.mp4

05:05

18. Working with Pandas Series.mp4

08:41

19. Mathematical Operation on Pandas Series.mp4

02:38

20. Dataframes in Pandas.mp4

13:02

21. Working with Data in Pandas DataFrame.mp4

09:10

22. Combining the DataFrames.mp4

09:22

23. Other Functions on Pandas DataFrame.mp4

10:42

24. Advanced Functions in Pandas DataFrame.mp4

20:57

25. Introduction to EDA.mp4

03:16

26. Accessing Google Colab.mp4

05:17

27. Loading the Large Dataset for Working.mp4

07:17

28. Preliminary Analysis on DataFrame.mp4

14:14

29. Null values in the Dataframe.mp4

06:50

30. Data Cleaning.mp4

09:44

31. Introduction to Data Visualization.mp4

06:17

32. Matplotlib Basics.mp4

09:28

33. Types of Plot - Line plot.mp4

03:12

34. Line Plots Hands On.mp4

09:29

35. Adjusting the Plots.mp4

09:14

36. Plot Adjustment Hands On.mp4

08:00

37. Scatter Plot.mp4

03:30

38. Scatter Plot hands on.mp4

09:53

39. Historgram Plot.mp4

05:33

40. Introduction to Seaborn.mp4

03:12

41. Exploring the data.mp4

09:38

42. Univariate & Bivariate Plots - Continuous Data.mp4

11:19

43. Plot - Categorical Data.mp4

09:15

44. Advanced Plots in Seaborn.mp4

06:46

45. Which Plot to use .mp4

04:59

20. Appendix

1. MLOps with MLFlow in 1 Hour.mp4

50:55

2. Kubernetes 101 Part 1.mp4

45:47

3. Kubernetes 101 Part 2.mp4

33:52

Description

Unlock success in AI Operations with our MLOps Bootcamp – mastering tools,techniques, AIOps for cutting-edge expertise

What You'll Learn?

Develop a solid foundation in Python, tailored for MLOps applications.
Streamline Machine Learning processes using Python's powerful capabilities.
Leverage Python for effective data manipulation and analysis in Data Science.
Understand how Python enhances the entire data science lifecycle.
Master version control using Git for collaborative development.
Learn to manage and track changes efficiently within MLOps projects.
Dive into the art of packaging Machine Learning models for easy deployment.
Ensure models are reproducible and deployable in diverse environments.
Effectively manage and track Machine Learning experiments using MLflow.
Utilize MLflow for enhanced experiment tracking and management.
Acquire essential skills in YAML for MLOps configuration and deployment.
Gain practical experience in writing and interpreting YAML files.
Explore Docker and its role in containerizing Machine Learning applications.
Understand the advantages of containerization for efficient MLOps.
Develop Machine Learning applications with FastAPI for efficient and scalable deployments.
Explore Streamlit and Flask for creating interactive web applications.
Implement Continuous Integration and Continuous Deployment pipelines for MLOps.
Automate development, testing, and deployment of ML models.
Gain a solid understanding of the Linux operating system.
Explore how Linux is essential for both DevOps and Data Scientists in MLOps.
Dive into Jenkins, an open-source automation server.
Learn to set up and configure Jenkins for automating MLOps workflows.
Develop insights into effective monitoring and debugging strategies for MLOps.
Utilize tools and techniques to identify and address issues in ML systems.
Set up continuous monitoring for MLOps using Prometheus and Grafana
Enhance observability in Machine Learning applications.
Extend Docker skills by mastering Docker Compose.
Learn to deploy multi-container applications seamlessly.
Explore tools and strategies for ongoing performance monitoring in MLOps.
Proactively address issues in production ML systems.
Utilize WhyLogs for efficient monitoring and logging of ML data.
Enhance the observability and traceability of ML systems.
Understand crucial steps for maintaining and updating ML models in a production environment.
Implement best practices for ensuring the long-term success of deployed ML systems.

Who is this for?

Data scientists seeking to extend their skills into the operational aspects of deploying and maintaining machine learning models.

Software developers interested in mastering the tools and practices for integrating machine learning into real-world applications.

DevOps professionals aiming to specialize in MLOps and enhance their proficiency in deploying and managing machine learning systems.

Data engineers looking to broaden their skill set by incorporating MLOps practices into data pipelines.

IT professionals wanting to understand the integration of machine learning models within operational workflows.

Individuals passionate about the latest advancements in technology and eager to explore the practical aspects of MLOps.

Entrepreneurs and business professionals seeking to understand how MLOps can drive innovation and competitive advantage in their organizations.

Students and researchers in the fields of computer science, data science, and related disciplines looking to expand their knowledge in MLOps.

Individuals transitioning into roles that involve machine learning operations and deployment.

Enthusiasts who are keen to explore the convergence of machine learning and operations, regardless of their current role or background.

What You Need to Know?

Familiarity with programming concepts is preferred, but we cover in our course as well

Some knowledge of data manipulation and analysis will be beneficial.

Basic understanding of version control concepts, preferably with Git - will be beneficial

Enthusiasm for the intersection of Machine Learning and DevOps practices.

Participants should have access to a computer with a stable internet connection for viewing video content and engaging in practical exercises.

More details

Description
Welcome to our extensive MLOps Bootcamp (AIÂ Ops Bootcamp), a transformative learning journey designed to equip you with the skills and knowledge essential for success in the dynamic field of Machine Learning Operations (MLOps). This comprehensive program covers a diverse range of topics, from Python and Data Science fundamentals to advanced Machine Learning workflows, Git essentials, Docker for Machine Learning, CI/CD pipelines, and beyond.
Curriculum Overview:
1. Python for MLOps:
Dive into the fundamentals of Python tailored specifically for MLOps.
Explore Python's role in streamlining and enhancing Machine Learning processes.
Develop proficiency in leveraging Python for effective MLOps practices.
2. Python for Data Science:
Uncover the power of Python in the context of Data Science.
Learn essential data manipulation and analysis techniques using Python.
Understand how Python enhances the entire data science lifecycle.
3. Git and GitHub Fundamentals:
Master the essentials of version control with Git.
Understand how GitHub facilitates collaborative development in MLOps.
Learn to manage and track changes effectively within MLOps projects.
4. Packaging the ML Models:
Delve into the art of packaging Machine Learning models.
Explore different packaging techniques and their implications.
Ensure your ML models are easily deployable and reproducible.
5. MLflow - Manage ML Experiments:
Learn to effectively manage and track Machine Learning experiments.
Understand the features and benefits of MLflow for experiment tracking and management.
Implement MLflow in your MLOps projects for enhanced experimentation.
6. Crash Course on YAML:
Acquire a solid foundation in YAML, a key configuration language.
Learn how YAML is used in MLOps for configuration and deployment.
Gain practical skills in writing and interpreting YAML files.
7. Docker for Machine Learning:
Explore Docker and its role in containerizing Machine Learning applications.
Understand the advantages of containerization for MLOps.
Learn to build and deploy Docker containers for Machine Learning projects.
8. Build MLApps using FastAPI:
Dive into FastAPI, a modern, fast web framework for building APIs.
Learn to develop ML applications using FastAPI for efficient and scalable deployments.
Implement best practices for building robust MLApps.
9. Build MLApps using Streamlit:
Explore Streamlit, a powerful framework for creating interactive web applications.
Develop hands-on experience in building MLApps with Streamlit.
Understand how Streamlit enhances the user interface for Machine Learning applications.
10. Build MLApps using Flask:
Gain proficiency in Flask, a popular web framework for Python.
Learn to build and deploy Machine Learning applications using Flask.
Understand the integration of Flask with MLOps workflows.
11. CI/CD for Machine Learning:
Explore Continuous Integration and Continuous Deployment (CI/CD) pipelines in the context of MLOps.
Implement automation to streamline the development, testing, and deployment of ML models.
Learn to build robust CI/CD workflows for Machine Learning projects.
12. Linux Operating System for DevOps and Data Scientists:
Understand the fundamentals of the Linux operating system.
Explore how Linux is essential for both DevOps and Data Scientists in MLOps.
Gain practical skills in working with Linux for MLOps tasks.
13. Working with Jenkins:
Dive into Jenkins, an open-source automation server.
Learn to set up and configure Jenkins for automating MLOps workflows.
Understand how Jenkins enhances the efficiency of continuous integration and deployment in MLOps.
14. Monitoring and Debugging of ML System:
Gain insights into effective monitoring and debugging strategies for MLOps.
Learn tools and techniques to identify and address issues in Machine Learning systems.
Implement best practices for maintaining the health and performance of ML systems.
15. Continuous Monitoring with Prometheus:
Explore Prometheus, an open-source monitoring and alerting toolkit.
Learn to set up continuous monitoring for MLOps using Prometheus.
Understand how Prometheus enhances observability in Machine Learning applications.
16. Deploy Applications with Docker Compose:
Extend your Docker skills by mastering Docker Compose.
Learn to deploy multi-container applications seamlessly using Docker Compose.
Understand how Docker Compose enhances the deployment of complex MLOps architectures.
17. Continuous Monitoring of Machine Learning Application:
Dive into continuous monitoring practices specifically tailored for Machine Learning applications.
Explore tools and strategies to ensure ongoing performance monitoring in MLOps.
Implement solutions for proactively addressing issues in production ML systems.
18. Monitor the ML System with WhyLogs:
Explore WhyLogs, a data logging library for Machine Learning.
Learn how WhyLogs facilitates efficient monitoring and logging of ML data.
Implement WhyLogs to enhance the observability and traceability of your ML system.
19. Post Productionizing ML Models:
Understand the crucial steps involved in post-productionizing Machine Learning models.
Explore strategies for maintaining and updating ML models in a production environment.
Gain insights into best practices for ensuring the long-term success of deployed ML systems.
Conclusion:
Embark on this comprehensive MLOps Bootcamp to transform your skills and elevate your proficiency in the dynamic and ever-evolving field of Machine Learning Operations. Whether you are a seasoned professional or just starting your journey in MLOps, this program provides the knowledge, tools, and practical experience needed to succeed in implementing robust and efficient Machine Learning workflows. Join us and become a master of MLOps, ready to tackle the challenges of the modern AI landscape with confidence and expertise.
Who this course is for:
Data scientists seeking to extend their skills into the operational aspects of deploying and maintaining machine learning models.
Software developers interested in mastering the tools and practices for integrating machine learning into real-world applications.
DevOps professionals aiming to specialize in MLOps and enhance their proficiency in deploying and managing machine learning systems.
Data engineers looking to broaden their skill set by incorporating MLOps practices into data pipelines.
IT professionals wanting to understand the integration of machine learning models within operational workflows.
Individuals passionate about the latest advancements in technology and eager to explore the practical aspects of MLOps.
Entrepreneurs and business professionals seeking to understand how MLOps can drive innovation and competitive advantage in their organizations.
Students and researchers in the fields of computer science, data science, and related disciplines looking to expand their knowledge in MLOps.
Individuals transitioning into roles that involve machine learning operations and deployment.
Enthusiasts who are keen to explore the convergence of machine learning and operations, regardless of their current role or background.

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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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