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From Software Engineering To Machine Learning
This document provides guidance on transitioning from a software engineering background to machine learning. It recommends learning fundamentals like Python, NumPy, and Pandas first before more complex algorithms. The best way to learn is through hands-on projects, starting with simple algorithms and evaluating models. Deploying models is described as easy for engineers but difficult for data scientists. Community involvement is encouraged to avoid working alone. Real-world projects are presented from domains like car pricing, customer churn, credit risk, and image classification to illustrate learning concepts.
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From Software Engineering To Machine Learning
- 1. From Software Engineering ToMachine Learning Alexey Grigorev Lead Data Scientist at OLX Group Founder at DataTalks.Club
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- 6. Mostly engineering work! HiddenTechnical Debt in Machine Learning Systems https://papers.nips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf
- 7. You already have90% of required skills
- 8. Learning Plan ● Startwith fundamentals ● Learn simple algorithms ● Evaluate your model ● Deploy your model ● Learn complex algorithms
- 9. Learning Plan ● Startwith fundamentals ● Learn simple algorithms ● Evaluate your model ● Deploy your model ● Learn complex algorithms By doing projects!
- 10. The fundamentals ● Python ●NumPy ● Pandas
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- 13. It’s not scary! Matrixmultiplication is just a bunch of for loops!
- 14. It’s not scary! Matrixmultiplication is just a bunch of for loops! Tip + Home task: implement these operations yourself: ● Vector-vector multiplication ● Matrix-vector multiplication ● Matrix-matrix multiplication Bonus points: ● Express each operation using one for loop + previous operation
- 15. The best wayto learn: Learn by doing projects
- 16. Regression Classification Evaluation Tree-Based Models Image Classification Kubernetes and Kubeflow Serverless Deep Learning Chapter 2 Chapter4 Classification Chapter 3 Deployment Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 https://github.com/alexeygrigorev/mlbookcamp-code/
- 17. Pictures from olx.ua Project#1: Car Price Prediction https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/chapter-02-car-price/02-carprice.ipynb
- 18. Project #2: Churn https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/chapter-03-churn-prediction/03-churn.ipynb Churn:10% Churn: 20% Churn: 30% Churn: 40% Churn: 45% Churn: 85% Image source
- 19. Project #2 Cont’d:Evaluation https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/chapter-03-churn-prediction/04-metrics.ipynb IMPORTANT!
- 20. Project #2 Cont’d:Deployment https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/chapter-05-deployment Model /predict Churn service POST /predict { "probability": 0.06, "churn": true } Request Response { "id": "8879-zkjof", "gender": "female", "partner": "no", ... } IMPORTANT! * But easy for devs *
- 21. Project #3: CreditRisk https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/chapter-06-trees/06-trees.ipynb 🚗 Risk Scoring Model Approve Decline
- 22. Project #4: ImageClassification https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/chapter-07-neural-nets/07-neural-nets-train.ipynb inputs base vector outputs T-Shirt 150x150x3 keras.Model(inputs, outputs) Dense(10) Global Average Pooling2D base_model Input
- 23. Project #4 Cont’d:Deploy with Lambda { "tshirt": 0.9993, "pants": 0.0005, "shoes": 0.00004 } https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/chapter-08-serverless
- 24. Project #4 Cont’d:Deploy with Kubernetes Gateway (Resize and process image) Flask Model (Make predictions) TF-Serving Pants Raw predictions Pre-processed image HTTP (JSON) gRPC (protobuf) https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/chapter-09-kubernetes
- 25. Next steps ● Datascience competitions (Kaggle) ● End-to-end projects — your own! ● You don’t have to do it alone. Join a community
- 27. Summary ● You alreadyhave 90% of required skills ● Learn fundamentals: Python, NumPy, Pandas ● Don’t be afraid of math (it’s just for loops) ● The best way to learn is by doing projects ● Learn evaluation metrics and cross-validation ● Deployment is easy for you and difficult for data scientists ● Don’t do it alone!
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