GitHub basic commands

Three Levels of ML Software
https://ml-ops.org/content/three-levels-of-ml-software

Gist of the above article:

Building successful ML-based software projects is still difficult because every ML-based software needs to manage three main assets: Data, Model, and Code
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Machine Learning Operations(= MLOps) --> processes around designing, building, and deploying ML models into production.

Mainly 3 Steps:
1.Data —> Data Engineering Pipelines
2.Model —> Machine Learning Pipelines
3.Code —> Deployment Pipelines
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1.Data: Data Engineering Pipelines

1A: Data Ingestion
-Collecting data by using various systems

1B:Exploration and Validation
-Understanding the data
-obtain information about the content and structure of the data.

1C:Data Wrangling (Cleaning)

1D:Data Splitting
-Splitting the data into training (80 %), validation, and test datasets
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2.Model: Machine Learning Pipelines

2A: Model Training
2B: Model Evaluation
2C: Model Testing
2D: Model Packaging
———————————-
3.Code: Deployment Pipelines

3A:Model Serving -
Deploying the ML model in a production environment.

3B: Model Performance Monitoring - The process of observing the ML model performance based on live and previously unseen data, such as prediction or recommendation.

3C:Model Performance Logging - Every inference request results in a log-record.

At Last:
Deploying ML Models as Docker Containers

(Please read main article, I have not covered all things.It is very extensive. You will find this article useful when you will start hand-on-experience)

Internship Alert:

If you have basic knowledge of computer vision, I'm looking for interns at Doctor on Click Pvt Ltd.
(Image manipulation/ Images masking/ Image segmentation/ Image classifications. Anything)

Check out this job at Doctor On Click: https://www.linkedin.com/jobs/view/2721296401

Machine learning Crash course By Google
https://developers.google.com/machine-learning/crash-course

👆It is free. Open for all.

One of the most requested Book for Machine learning. Save for the future reference and doubt solving.

You don't need to master all to START.

Also includes list of Verbs and samples

very easy to understand and good book

I'm hiring for Django Developer (Intern and/or Full time Developer)

Twitter's New CEO: Parag Agarwal

Learn between the gaps. That's how you grow! Gaps won't matter eventually.

This article covers all the things you need to know. Also explains following terms in clear and concise way.

Shells: zsh CMD, power shell

Terminals: MacOS vs Widows

Python environments: Pyenv ,Conda, virtual environments

Package Management: pip, pipenv, poetry conda

Python Interpreters: IPython


https://realpython.com/effective-python-environment/

How to choose an open source license for your code(if it's open)
https://choosealicense.com/