MLFlow Model Deployment using Flask | Part 4 | MLFlow2.1.1 | Ashutosh Tripathi AI
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- Опубликовано: 16 ноя 2024
- MLFLow Model Deployment using Flask | MLFLow2.1.1
How to consume MLFLow deployed model within flask web application?
Topics Covered:
1. Model Training
2. Experiment Tracking
3. Model Registration within MLFLow
4. Model Deployment using MLFlow
5. Create a simple Flask Web APP
6. Do the batch prediction within Flask App
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Part 1: Experiment Tracking using MLFlow
• Experiment Tracking Us...
Website Design Complete tutorial using Flask: • How to design a websit...
VS Code Installation: ashutoshtripat...
Python Installation: ashutoshtripat...
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Connect in LinkedIn: / ashutoshtripathi1
#MLFLow #modeldeployment #flask
Website design using Flask: ruclips.net/video/Yrvy8uCqvYU/видео.html
Really appreciate that you are putting such useful content of mlflow continually 👌👌
My pleasure. Thank you.
Thanks you sir for this video. i was waiting for this video.
My pleasure. Just go through and if you get any doubt, just write it here.
sure sir. sir i sent you a msg on linkedin also
I cannot find the notebook for deployment using flask in the github link..please reload the document in the github
how can we run all the code in the background as in run/set it on the server so everything still runs when we leave the environment?
sir please create dvc videos also . you explain the things in a good way.
Sure, will create soon.
thanks for the videos , Please post some video for docker based deployment of model for api serving
Can you send the documentation link of the mlflow for the above deployement?
It will be great if you create Airflow tutorial complete for Machine Learning
Ok will create.
I understand but I have one question. Lets say netflix predicted that next movie I want to watch is Batman. How does it rank Batman movie at no. 1 when I open the netflix application?
So in general while recommending the content to watch, models assign some scores based on our interest from historical watched content. And then based on the score high to low it shows content on the dashboard.
So in recommendation it not just predict one item rather it predicts a set of items and assigns matching scores to each content.
Hope this helps.
@@AshutoshTripathi_AI Thank you Sir
Hey Ashutosh! Amazing content! Kudos to you on that! I wanted to ask if there is an updated git repo for this project.
Hi, have not created specific git repo for this. But have mentioned the file locations in the description box of the video. Let me know if you find difficulty in accessing any of the file or looking for any specific file. Will provide you.
where can I get this flask ipynb file ?
I am unable to find source code of this video. Please help
how to do model monitoring
the code is missing.
please push the code in github for this video
I will do, just give me today's time. Remind me tomorrow in case I forget.
Please download the files from the link below:
ashutoshtripathi.com/2023/03/25/machine-learning-model-deployment-using-docker-container/
@@AshutoshTripathi_AI Content of this website is too awesome Ashutosh ... Too helpful as well.. But this site does not contain flask and fast api files demonstrated.. Can you please share them
@AshutoshTripathi_AI yes!!!!!!!!! Absolutely, the content is well-detailed, and the explanation is thorough. However, I faced difficulties when trying to write the code since the corresponding code is not uploaded on the website and hasn't been committed to Git. :-(