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Day 2 (Lecture 5): A Practitioner's Perspective on Building Machine Product in Real Life

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Day 2 (Lecture 5): A Practitioner's Perspective on Building Machine Product in Real Life

  1. 1. Machine Learning In Real Life Richard Ackon @esquire_gh
  2. 2. About Me ● Software Engineer @ mPharma ● Technical Reviewer, Data Science @ Packt ● Writer @ AnalyticsVidhya
  3. 3. Disclaimer I’m not going to talk about: ● What machine learning is ● Applications of machine learning ● What algorithms to use ● What Frameworks or libraries to use
  4. 4. What I will talk about ● The stuff that most tutorials don’t cover ○ Version Control ○ Testing ○ Performance Metrics ○ Reproducibility ○ Going to Production ○ Ethics ● Lessons learned from building and deploying models to production ● My 2 pesewas on how to get started
  5. 5. Overly Simplified Diagram image_by : Jade Abbott, @alienelf
  6. 6. Version Control Recording changes to certain components of the machine learning process so you can recall specific versions from later. What to Version? ● The general idea is to try versioning anything that requires iteration and continuous improvement ● Most important components to version ● Code ● Data ● Models
  7. 7. Versioning tools
  8. 8. Testing ● Data cleaning , Modelling, Deployment are all done with code; so treat them as such. ● Test your Data if possible An idea for the brave ● Continuous Integration for data
  9. 9. Performance Metrics It’s always good to have one number that tells you how good your model is. But, In some cases, you need to select your evaluation metric based with some amount of domain expertise.
  10. 10. Reproducibility The ability to replicate a data science experiment using the same data and code running in the same environment, producing the same results. “non-reproducible single occurrences are of no significance to science.” - Karl Popper
  11. 11. Reproducibility - How Docker
  12. 12. Interpretability
  13. 13. Going to Production Production means getting your application used by its intended audience in a real world situation. Requirements: ● Accessibility ● Performance ● Fault Tolerance ● Scalability ● Maintenance
  14. 14. ETHICS
  15. 15. Some General Lessons Learned
  16. 16. Not everything is a machine learning problem
  17. 17. Sometimes bad data is all you have ……… and that’s OK!
  18. 18. Choosing ML libraries and Frameworks ● Focus on people over tools ● Think of stability in production ● If you’re still tied, Follow the crowd
  19. 19. Choosing Deep Learning Architectures ● A good place to start: Research papers ● General Advice: Try to overfit, and add regularization to generalize
  20. 20. My 2 pesewas on how to get started in ML/DS ● Understand what data science is and how it can be used ● Learn the basics ○ Data Science from Scratch from O’Reilly ○ Doing Data Science by Cathy O’Neil and Rachel Schutt ● Work on projects ○ Kaggle ○ Zindi ● Read other people’s work ○ Paperswithcode ○ Medium ○ ArXiv ● Attend events like this and continue solving more problems ● Learn the rest as you go
  21. 21. Thank You!

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