MLOps Bootcamp: Mastering AI Operations for Success – AIOps

سرفصل های دوره

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

1. Introduction to Complete MLOps Bootcamp

1. What and Why MLOps

2. The Stages of MLOps

2. Python for MLOps

1. About the Section

2. Python Quiz.html

3. Introduction to Python Programming

4. Install Anaconda

5. Hello World - Python

6. Jupyter Lab Quick Tour

7. Variables in Python

8. Variables - Comments - Markdown Cells - Hands On

9. Python Literals - Hands On

10. Operators in Python Programming Language

11. Collection - Strings

12. Python String - Builtin Functions - Hands On

13. Data Structures - List

14. Data Structures - Tuples

15. Data Structures - Dictionary

16. Data Structures - Sets

17. Explicit and Implicit Casting in Python Programming

18. Reading the Input from Keyboard

19. String Formatting

20. Control Statements - Conditional Statements in Python

21. Control Statements - Looping Statements

22. List comprehension

23. Functions

24. Modules in Python

25. Classes in Python

26. File Handling in Python

27. Working with Python Scripts

28. Libraries in Python

3. Git and Github Fundamentals for MLOps

1. Introduction to Version Control Systems

2. Getting Started with git

3. Local Repo vs Remote Repo

4. Git Configurations

5. Getting Started with Local Repo

6. Concept of Working Directory - Staging Area - Commit

7. Git Workflow - Local Repo

8. Git Branch

9. Switching the Branches

10. Merging

11. Checking Out Commits

12. Git Hosting Services

13. Working with Remote Repositories

14. Cloning and Delete Branches

15. 3 way merge

16. Summary

4. Crash Course on YAML

1. YAML Crash Course

5. Packaging the ML Models

1. Introduction to Packaging the ML Models

2. Typical Experimentation with Dataset

3. Model fit and generate Predictions

4. Challenges in Working inside the Jupyter Notebook

5. Understanding the Modular Programming

6. Creating Folder Hierarchy for ML Project

7. Create Config Module

8. Data Handling Module

9. Data Preprocessing part 1

10. Data Preprocessing part 2

11. sklearn pipeline

12. Training Pipeline

13. Prediction Pipeline

14. Fixes on Python Scripts

15. Add Python Path to MacOS

16. Perform Training and Predictions

17. Requirements txt file

18. Testing the New Virtual Environments

19. Create Python tests

20. Running Pytest

21. Create Manifest file

22. Create Version File

23. Create setup.py

24. Packagiing the ML Model & testing

25. Summary

6. Mlflow - Manage ML experiments

1. Introduction to Mlflow

2. Getting System Ready with mlflow

3. Logging Functions of Mlflow Tracking

4. Basic Mlflow tutorial

5. Exploration of mlflow

6. Machine Learning Experiement on MLFlow

7. Create ML Model for Loan Prediction

8. MLFlow Project

9. MLFlow Models

10. Setting Up MySql Database Locally

11. Log Model Metrics in MySql

12. Register the Model & Serve the Model

13. Summary

7. Docker for Machine Learning

1. Docker for Machine Learning

2. Introduction to Docker

3. Installation of Docker Desktop

4. Working with Docker

5. Running the Docker Container

6. Working with Dockerfile

7. Push the Docker Image to DockerHub

8. Dockerize the ML Model

9. Packaging the training code in Docker Environment & Summary

8. Build MLApps using FastAPI

1. What is API, REST and REST API

2. How REST API Works

3. What is FastAPI

4. Crash course on FastAPI

5. Data Validation with Pydantic

6. Deploying the Machine Learning Model with FastAPI

9. Build MLApps using Streamlit

1. Introduction to Streamit

2. Hands On Working with Streamlit

3. Building the ML Model with Streamlit

10. Lnux Operating System for DevOps and Data Scientists

1. Agenda of this section

2. Linux Features & Bash

3. How to Launch EC2 Instances (Quick Refresh)

4. Basic Linux Commands of Linux

11. Working with CI CD Tool Jenkins

1. Introduction to Jenkins

2. How do we Use Jenkins in MLOps

3. Prepare and Package ML Model

4. Deploy as API with FASTAPI

5. Test FastAPI App

6. Create Dockerfile

7. Exposing the Application Port as per Dockerfile

8. Test Locally using Docker Containers

9. Installation of Jenkins on AWS EC2 Instances

10. Installation of Docker in EC2 Instance

11. Configure Github Repo - Webhook - Jenkins Credentials

12. Introduction to Jenkins FreeStyle Projects and Pipeline Jobs

13. Exploration of Jenkins UI

14. Create your first First Jenkins Project

15. Test Github Webhook with Jenkins

16. Installation of Docker Plugin & System Readiness

17. Setup Email Notification with Gmail

18. Introduction to CI CT CD Pipeline

19. Create CI CT CD Pipeline - Github Dockerhub

20. Create CI CT CD Pipeline - Training

21. Create CI CT CD Pipeline - Testing

22. Create CI CT CD Pipeline - Deployment

23. Perform Test of Pipeline

24. Summary

12. Monitoring and Debugging of ML System

1. Why Monitoring Machine Learning Models is Important

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

3. Why Monitoring Machine Learning Models is Difficult

4. Challenge - Who Owns what

5. Functional Level Monitoring

6. Model Drift

7. Operational Level Monitoring

8. Tools and Best Practices of Machine Learning Model Monitoring

13. Continuous Monitoring with Prometheus

1. Introduction to Continuous Monitoring

2. Use case on Continuous Monitoring

3. Introduction to Prometheus

4. Architecture of Prometheus

5. Metric Types of Prometheus

6. Installation of Prometheus

7. Introduction Grafana

8. Installation of Grafana

9. Prometheus Configuration file

10. Exploring the Basic Querying Prometheus

11. Monitor the Infrastructure with Prometheus

12. Monitor the Linux Server with Node Exporter

13. Monitor the Client Application using Prometheus

14. Monitor the FastAPI Application using Prometheus

15. Monitor All EndPoints using Prometheus

16. Create Visualization with Grafana

17. Trigger Alerts with Grafana

14. Deploy Applications with Docker Compose

1. Introduction to Docker Compose

2. Hands On - Docker Compose with Flask Application

3. Hands On - Docker Compose Prometheus Grafana

15. Continuous Monitoring of Machine Learning Application

1. Architecture of ML Application Monitoring

2. Hands On Monitoring of ML Application using Prometheus

16. Monitor the ML System with WhyLogs

1. Introduction to ML Monitoring

2. Setting Up WhyLabs

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

4. WhyLogs - Constraints and Drift Reports

5. Summary

17. Post Productionizing ML Models

1. Post-Productionalizing ML Models - What Next

2. Model Security

3. Adversarial Attack

4. Data Poisoning Attack

5. Distributed Denial of Service Attack (DDOS)

6. Data Privacy Attack

7. How to Mitigate Risk of Model Attacks

8. AB Testing

9. Future of MLOps

18. Reference Getting Started with AWS

1. What do we cover in this section

2. Create AWS Account

3. Setting up MFA on Root Account

4. Create IAM Account and Account Alias

5. Setup CLI with Credentials

6. IAM Policy

7. IAM Policy generator & attachment

8. Delete the IAM User

9. S3 Bucket and Storage Classes

10. Creation of S3 Bucket from Console

11. Creation of S3 Bucket from CLI

12. Version Enablement in S3

13. Introduction EC2 instances

14. Launch EC2 instance & SSH into EC2 Instances

15. Clean Up Activity

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

1. Introduction to Numpy Library

2. Basics of numpy array object

3. Import Numpy & Access help

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

5. Attributes of Numpy Array

6. Array Indexing and Slicing

7. Array Creation Functions

8. Copy Arrays

9. Mathematical Operation on Numpy Arrays

10. Linear Algebra Functions in Numpy

11. Shape Modification of Arrays

12. np.arange()

13. Relational Operators & Aggregation Functions on Numpy Arrays

14. Boolean Masking

15. Broadcasting on Numpy Arrays

16. Summary of Numpy Library Journey

17. Introduction to Pandas

18. Working with Pandas Series

19. Mathematical Operation on Pandas Series

20. Dataframes in Pandas

21. Working with Data in Pandas DataFrame

22. Combining the DataFrames

23. Other Functions on Pandas DataFrame

24. Advanced Functions in Pandas DataFrame

25. Introduction to EDA

26. Accessing Google Colab

27. Loading the Large Dataset for Working

28. Preliminary Analysis on DataFrame

29. Null values in the Dataframe

30. Data Cleaning

31. Introduction to Data Visualization

32. Matplotlib Basics

33. Types of Plot - Line plot

34. Line Plots Hands On

35. Adjusting the Plots

36. Plot Adjustment Hands On

37. Scatter Plot

38. Scatter Plot hands on

39. Historgram Plot

40. Introduction to Seaborn

41. Exploring the data

42. Univariate & Bivariate Plots - Continuous Data

43. Plot - Categorical Data

44. Advanced Plots in Seaborn

45. Which Plot to use

20. Appendix

1. MLOps with MLFlow in 1 Hour

2. Kubernetes 101 Part 1

3. Kubernetes 101 Part 2

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تولید کننده: Udemy