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 1 | <br />
 2 | <div align="center">
 3 |   <a href="https://zenml.io">
 4 |     <img src="assets/anternsvg.svg" alt="Logo" width="400">
 5 |   </a>
 6 | 
 7 |   <h3 align="center">One-stop solution for all your Data Science/AI/MLOps learning needs.</h3>
 8 | 
 9 |   <p align="center">
10 |     All in one place, the best resources to learn Data Science/AI/MLOps with comprehensive and detailed courses.
11 |     <br />
12 |     <a href="https://antern.co/"><strong>Go to website</strong></a>
13 |     <br />
14 |     <div align="center">
15 |       Join our <a href="https://discord.gg/t9uKG2m9" target="_blank">
16 |       <img width="25" src="assets/4373196_discord_logo_logos_icon.png" alt="discord"/>
17 |     <b>Antern Community</b> </a> and ask your questions there. 
18 |     </div>
19 |   </p>
20 | </div>
21 | 
22 | # Machine Learning Operations(MLOps) Roadmap 🤖
23 | 
24 | This repository is a comprehensive guide to anyone who wishes to learn MLOps. It contains a roadmap with various topics and resources. You can find detailed videos on these topics on my YouTube channel (linked in the description). For a more in-depth understanding, consider enrolling in my comprehensive course.
25 | 
26 | ## Table of Contents
27 | 
28 | I will divide the resources into different levels of learning and will also provide the best resources to learn each topic. The levels of learning are:
29 | 
30 | - **Machine Learning and Data Science Basics
31 | - **Software Development Life Cycle (SDLC)
32 | - **DevOps
33 | - **MLOps
34 | - **My Courses
35 | 
36 | ## Machine Learning and Data Science Basics <a name="ml-ds-basics"></a>
37 | 
38 | Before you start your journey, it's essential to have a solid base in machine learning and data science. Please refer to my Machine Learning Roadmap Video for a structured approach to these subjects.
39 | 
40 | ## Software Development Life Cycle (SDLC) <a name="sdlc"></a>
41 | 
42 | Understanding the Software Development Life Cycle is the next step:
43 | 
44 | - Requirement Gathering Analysis: Understand what the client needs and determine the feasibility of the requirements.
45 | - Design: Plan and design the software-based on the requirement analysis.
46 | - Implementation or Coding: Build the software by integrating the pieces of code and libraries.
47 | - Testing: Test the software for any bugs and ensure it works as expected.
48 | - Deployment: Release the software on live servers where actual users will use it.
49 | - Maintenance: After deployment, introduce updates and improvements to the software.
50 | 
51 | You can find the detailed resources related to SDLC in this [GitHub Repository.](https://github.com/rajeevranjancom/SDLC) 
52 | 
53 | ## DevOps <a name="devops"></a>
54 | 
55 | Next, learn about DevOps. The important topics include:
56 | 
57 | - Continuous Integration / Continuous Delivery (CI/CD)
58 | - Infrastructure as Code (IAC)
59 | - Version Control Systems (like Git)
60 | 
61 | ## MLOps <a name="mlops"></a>
62 | 
63 | Once you have a good grasp of DevOps, it's time to dive into MLOps:
64 | 
65 | - Data Versioning: Tools like [DVC (Data Version Control)](https://dvc.org/)
66 | - Model Versioning: Tools like [MLflow](https://mlflow.org/) and DVC
67 | - Model Packaging: Tools like Docker
68 | - Model Validation and Testing: Tools like [TensorFlow Extended (TFX)](https://www.tensorflow.org/tfx) and [PyCaret](https://pycaret.org/)
69 | - Continuous Integration for Machine Learning: Tools like [Jenkins](https://www.jenkins.io/) and [GitHub Actions](https://github.com/features/actions)
70 | - Continuous Deployment for Machine Learning: Tools like Jenkins, GitHub Actions, and Azure DevOps
71 | - Model Monitoring and Retraining: Tools like ModelDB, MLflow, and TFX
72 | - Governance and Regulatory Compliance: Tools like IBM OpenPages and Collibra 
73 | 
74 | ## My Courses <a name="my-courses"></a>
75 | 
76 | * Core Machine Learning Course: For a comprehensive, paid course on Machine Learning, please visit this [link](https://youtu.be/PhB5CkbZHB8).
77 | * Free 10-hour Video Course: For a free, 10-hour long introduction to Machine Learning, please watch this [video](https://youtu.be/0g-XL0WV2xo).
78 | 
79 | ### Contributions 🤝
80 | 
81 | We are open to contributions, if you want to contribute to this repository, you can check out the [contributing guidelines](#). You can also contribute by sharing this repository with your friends and colleagues.
82 | 


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