Comments (13)
Other slides are up for grabs
- Business and Data Understanding - @sahithi02
- ML Model Engineering - @sayantikabanik
- ML Model Evaluation - @anuraagbhavaraju
- Model Monitoring and Maintenance - @nitinjethwani7
- Data engineering - @sayantikabanik
- Model Deployment - @nitinjethwani7
- Flow chart creation - @sahithi02 @sayantikabanik
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Business and Data Understanding
Knowing the amount of food needed for the wellbeing of citizens of the country is critical discussion point of health ministry.
- why we choose this project?
- @nitinjethwani7 sharing a you tube link
ML Model Engineering /Data engineering
- Referenes
- Models selection
- Complexity
- Feature selection
- Flow chart (make one @sayantikabanik)
ML Model Evaluation
- Accuracy parameters etc
- Hyperparmeteres if any
- Model robustness
Model maintenance/ deployment
- ML flow (check if this requies any external deloyment requirement)
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@sayantikabanik iop.org check and nitin's groups ppt - for the flow chart
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Flowchart
Let me know if more tweaks are required
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Adding the checklists here for reference
ML_Production_Readiness_Checklist.xlsx
FP2.CRISP-.ML.Q.xlsx
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Have added details to the slides assigned to me
Please take a look and let me know if any modifications are required.
@nitinjethwani7 @anuraagbhavaraju @sahithi02
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Report for submission (please copy-paste your content)
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Have added details to the slides assigned to me Please take a look and let me know if any modifications are required. @nitinjethwani7 @anuraagbhavaraju @sahithi02
Flow chart looks great,we can add anything from this blog (Detailed view section)
https://medium.com/analytics-vidhya/fundamentals-of-mlops-part-1-a-gentle-introduction-to-mlops-1b184d2c32a8
.Also i have added ML flow screenshots,you can take those screenshots and keep it under the slide (instead of the current screenshot).Also we might need some content under model engineering. The current one has only screenshots and subparts,if we can have something like this with subheadings,will look great ,appealling to present.
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@sahithi02 - We are missing on this slide,this is the heart of business section,we cant miss this section under any circumstances. For mid review take any number(feasible) and mention it under different sections. Reach out to me in case of any confusion.
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@nitinjethwani7 after mid review please add the notebooks to respective folders (refer readme) also update environment.yml file
here please don't add any screenshots , its a package and not meant to have static multimedia content (instead add them to an issue)
from fp2.
Have added details to the slides assigned to me Please take a look and let me know if any modifications are required. @nitinjethwani7 @anuraagbhavaraju @sahithi02
Flow chart looks great,we can add anything from this blog (Detailed view section) https://medium.com/analytics-vidhya/fundamentals-of-mlops-part-1-a-gentle-introduction-to-mlops-1b184d2c32a8
.Also i have added ML flow screenshots,you can take those screenshots and keep it under the slide (instead of the current screenshot).Also we might need some content under model engineering. The current one has only screenshots and subparts,if we can have something like this with subheadings,will look great ,appealling to present.
I kept it simple, not sure which part I should be adding from the article. Let me know what you have in mind.
Also I deleted the screenshot from the repo as it was showing empty (byte size 0) . Would be great if you could add it here
@nitinjethwani7
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feedback added #11
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Here is a link to a presentation (have edited and added the slide titles)
This is on canva platform
Up dated on intro part
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Related Issues (20)
- Clean the repo HOT 2
- Mid review feedback [FP2] HOT 1
- final review left over parts HOT 2
- Turn the notebook into python script [model] HOT 4
- Update env.yml file HOT 2
- Need the logic you have used for the extraction from Raw format HOT 3
- fix github actions HOT 4
- Dependencies/pickle/code for the best model HOT 8
- Final review Presentation HOT 4
- Model/data/concept drift
- Project selection HOT 5
- deployment (with UI) HOT 1
- Resources [Git] HOT 1
- Mid review HOT 4
- Add FP1 [reviews/feedbacks received from prof] HOT 4
- Data collection HOT 19
- Task allocation for new folks HOT 4
- kaggle [modelling] HOT 6
- Data Engineering HOT 10
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