Day 1 - Project Introduction & Setup | MLOPs Production Ready Machine Learning Project

🚀MLOps Production Ready Machine Learning Project 🚀

🚀 Welcome to our comprehensive guide on creating a production-ready machine learning project using MLOps! In this video, we will walk you through every step needed to successfully deploy and manage your machine learning models in a production environment.

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This channel focuses on providing content on Data Science, Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Natural language processing, Python programming, etc. in Bangla and English.

My mission is to provide inspiration, motivation & good quality education to students for learning and human development, and to become an expert in Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Natural language processing, Python programming, and so on.

#dswithbappy aims to change this education system of Bangladesh.
I believe that high-quality education is not just for the privileged few. It is the right of everyone who seeks it. My aim is to bring quality education to every single student. All I need from you is intent, a ray of passion to learn.

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#dswithbappy

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[moshiurrahman]

Bro, u r simply amazing. Inn Shaa Allah one day u will shine. just move on my dear younger bro. wish you a great success in future days to come. (sorry, I m logged in with the email of my younger bro).

[utkar]

07:13 - Prerequisites
13:56 - Setup
19:57 - Project discussion
24:42 - Problem statement
27:22 - Features
28:14 - Solution scope
29:04 - Solution approach
31:56 - Solution proposed
35:19 - Project setup
36:08 - MongoDB setup
37:24 - Github repository setup
41:50 - Folder structure setup
01:20:38 - requirements.txt

[aliaamir]

dear Bappy, i also saw that you are part of ineuron team along with krish naik sahab, just wanted to request to please take one multivariate timeseries regression problem and solve it as a end to end MLOPS deep learning project, because i am only able to explore all of your and krish naik projects based on computer vision and machine learning, but anyhow i really appreciate your efforts, this is rare content on youtube. Love from Pakistan,

[akashjoy.V]

Thank you brother. Excellent content. Can you please drop a schedule to something before you go live so that we can be ready ?

[utkar]

Thanks Bappy, appreciate your efforts! Looking forward to the next one!

[mahetsiedahi]

thnks Bappy for your contribution, you have a great way of explaining, it motivates me to keep learning and overcome certain fears professionally. I hope you continue sharing knowledge and thank you again for the inspiration...Greetings from Mexico!

[mechxp-pben]

bro i have now understood what all this you are genious

[jatinsareen]

Great session!!
But why did you leave ineuron and why others are also leaving it?? will ineuron get closed??

[LomeshSoni-hi]

THanks for the session. Its very informative. I just have one question.
Is this the way how companies setup the things and have these folders for the codebase while working on real world problems

[kashifsadiq]

Sir plzzz more focus on ops or deployment part. Kubeflow. Air flow kubernetes seldon monitoring Kserve

[soumyajitsahu]

can you please start genai project parallely . Thank you for your so much valuable videos. Your Videoes are so valuable somuch details.

[binnypero]

54:25 - Importance of project organization and folder structure.
01:00:47 - Creating a project folder structure automatically using Python scripts.
01:01:01 - Creation of essential components like configuration and constants.
01:03:16 - Differences in path handling between operating systems.
01:08:04 - Automation through scripts to reduce manual effort.
01:10:51 - Introduction of a website for creating flowcharts and the tool Evidently.
01:11:14 - Creating diverse flowcharts for better understanding data workflows.
01:13:22 - Evidently for monitoring AI applications and detecting data discrepancies.
01:14:39 - Focused approach in using ML tools for project simplicity.
01:20:55 - Guide on using Git commands and creating a Python environment.
01:20:56 - Demonstration of basic Git commands for version control.
01:21:11 - Discussion on creating a project environment with conda.
01:22:40 - Installation of necessary packages for the project.
01:30:59 - Importance of understanding Python's stable versions.
01:31:13 - Release cycle of Python versions and their support.
01:34:39 - Incorporating CI/CD tools like GitHub Actions and Jenkins.
01:38:40 - Importance of coding in environments like Jupyter notebooks.
01:03 - Mastering modular coding in Python for machine learning projects.
01:52 - Importance of installing packages like XGBoost and CatBoost.
06:02 - Plans to integrate Jenkins into future projects.
07:43 - Significance of setting up a proper development environment.
01:55:42 - Exploration in learning to broaden knowledge.
01:58:44 - Importance of GitHub for sharing code and collaboration.
01:58:51 - Practical application of learned skills through hands-on projects.
01:59:09 - Upcoming sessions on MongoDB setup and data management.

[sumankumar]

Stuck at environment creation. Can someone help pls

[sumankumar]

@dswithbappy You have not instructed to activate Conda environment inside git bash. none of the python commands would run. Please add that part in the video.

[rezamahmoudi]

thanks, please share powerpoint share ?