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![[Image from https://scikit-learn.org/stable/modules/neighbors.html#classification]
As we can see here two
features (sepal_length and
sepal_width) is not enough
features to separate the three
different varieties (setosa,
versicolor, virginica).
Classify Iris Flowers with K-Nearest Neighbors](https://image.slidesharecdn.com/pyconsweden2022-dowling-serverlessmlwithhopsworks-221104124102-9ce2103f/75/PyCon-Sweden-2022-Dowling-Serverless-ML-with-Hopsworks-pdf-25-2048.jpg)
Uploaded byJim Dowling
PyCon Sweden 2022 - Dowling - Serverless ML with Hopsworks.pdf
This document discusses building machine learning systems using serverless services and Python. It introduces the Iris flower classification dataset as a case study. The key steps outlined are to: create accounts on Hopsworks, Modal, and HuggingFace; build and run feature, training and inference pipelines on Modal to classify Iris flowers; and create a predictive user interface using Gradio on HuggingFace to allow users to input Iris flower properties and predict the variety. The document emphasizes that serverless infrastructure allows building operational and analytical ML systems without managing underlying infrastructure.
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PyCon Sweden 2022 - Dowling - Serverless ML with Hopsworks.pdf
- 1. Build ML Systemswith just Python and free Serverless Services Jim Dowling – CEO and Co-Founder @ Hopsworks
- 2. ❌ Static Datasets ❌Data is downloaded from a single URL ❌ Features for ML are correct and unbiased ❌ Use a model evaluation metric (accuracy) to communicate the value of your model 💚 Data never stops coming 💚 Data comes from different heterogeneous data sources 💚 Write code to extract and validate features from input data 💚 Integrate your model with a UI or app/service 💚 Follow MLOps best practices for operating ML Services (versioning, testing) 2 Production Machine Learning is hard
- 3. Enterprise Data andEnterprise AI Infrastructure is Complex and Big Model Serving MySQL / Postgres KV Store Object/GraphDB Elastic Operational DBs Snowflake / Databricks / BQ Hopsworks / Feast / etc Feature Store Images, Docs, Parquet, etc S3 /ADLS / GCS ETL Bronze Sliver Gold Data Lake Data Warehouse Kafka Streaming Feature Pipelines ETL Feature Pipelines ELT or ETL Model Registry Batch Inference Pipelines Online Inference Pipelines Predictions Online App Training Pipelines Analytical Data Stores ML Infrastructure
- 4. There has tobe an easier way to build production ML Systems…. Show love with a star! www.serverless-ml.org https://github.com/featurestoreorg/serverless-ml-course ⭐
- 5. Serverless infrastructure, fromserverless databases to lambda functions, enables developers to build such operational and analytical services without having to install, configure, and operate the infrastructure that powers them. Serverless infrastructure is often synonymous with cloud infrastructure ■ The cloud makes it easier to build services that are highly available, elastic (scale up and down resource usage in response to demand), can be upgraded, backed up and restored, and are pay-per-use. 5 Yes, with Serverless Machine Learning
- 6. Serverless Machine Learning Requirements& Sprint Tasks Working ML System Validate Model/Features Validate UI / API iterate iterate Serverless ML should improve both iteration speed and quality when developing ML products by enabling seamless integration of best practices from MLOps. Serverless infrastructure enables developers to build such operational and analytical ML systems without having to operate the infrastructure that powers them.
- 7. Any ML Systemcan be composed as a set of Feature/Training/Inference Pipelines Feature Pipelines Inference Pipeline Training Pipeline Feature & Model Store Prediction Service/UI features & Labels features & Labels model Batch/Streaming Batch/Online On-Demand features & model New Data Historical Data Logs (predictions) monitor Monitoring
- 8. Why add aFeature Store & Model Registry to the Serverless ML Stack? To Manage State ● Stores features, models, and files so that they can be passed between pipelines For Online Models ● History and Context for Online Models - needed by Stateless Applications ● Eliminate Training/Inference Skew For Batch Models ● Central repository to share, discover and use versioned, secure features ● Decouple feature engineering and batch inference pipelines
- 9. Feature Pipelines A featurepipeline is a program that orchestrates the execution of a dataflow graph of model-independent data transformations to prepare features. V A C Input Data Feature(s) VACE = Validate, Aggregate, Compress (dimensionality reduction), Extract (Binning, Crosses, etc) Feature Store feature-pipeline.py E
- 10. Training Pipelines C Feature(s) Feature Store E Atraining pipeline is a program that takes input features, applies model-specific transformations, and then trains a model. During development, there may be additional hyperparameter tuning and model architecture search steps. The output is a model that is stored in a model registry. H A T Untransformed Input Features Model T-HATE = Transform features, Hyperparameter tuning, model Architecture, Train model (fit to data), Evaluate your model. E T training-pipeline.py Feature Store Model Registry
- 11. Batch Inference Pipelines Abatch inference pipeline is a program that takes input features and a model, performs model-specific transformations, makes predictions using the model and the transformed features, and writes the output predictions to some sink. P O Untransformed Input Features Predictions TPO = Transform features, Predict, Output. T batch-inference-pipeline.py Feature Store Prediction Results Model Registry model
- 12. Online Inference Pipelines Results TP P Request predictions Feature Store Application precomputed untransformed features transformed features TPP = Transform the input request into features, Predict using input features and the model, Post-process predictions, before output results. Model Registry model predictor.py transformer.py model-deployment An online inference pipeline is a program that takes input from a client app, retrieves features from a feature store, transforms them, and then makes predictions with the features and the model, finally returning the predictions to the client.
- 13. Today’s workshop isto build Iris as a Serverless ML System
- 14. Iris Flower, blueand yellow, ultra-wide-angle created with Midjourney Course Material: Prof Jim Dowling Lab 1: Iris Flowers as a Serverless ML System
- 15. ● Course Repositoryon Github https://github.com/featurestoreorg/serverless-ml-course/ ● Today’s workshop source code is here: https://github.com/ID2223KTH/id2223kth.github.io/tree/master/src/serverless- ml-intro ● Use Conda or virtual environments to manage your python dependencies on your laptop Source Code for Workshop
- 16. Iris as aserverless Operational ML system for Predictive Analytics iris-feature-pipi peline.py iris/ app.py iris-training-pip ipeline.py Iris UI Iris Flower Data iris.csv features, labels features, labels model model
- 17. Iris as aserverless Analytical ML system iris-feature-pipi peline-daily.py iris-monitor/ app.py iris-training-pip ipeline.py Iris Dashboard & monitoring Iris Flower Data iris.csv features, labels features, labels model model Synthetic Iris Flower Data Logs (predictions)
- 18. ● Case Study:Iris Flower Dataset ● First Steps a. Create a free account on hopsworks.ai b. Create a free account on modal.com c. Create a free account on huggingface.com ● Tasks a. Build and run a feature pipeline on Modal b. Build and run a training pipeline on Modal c. Build and run an inference pipeline with a Gradio UI on Hugging Face Spaces. What will we cover in this lab
- 19. 1. First, createan account on https://app.hopsworks.ai Register and Login to the Hopsworks Feature Store 19 2. Click on “User Settings” 3. Create and Save an “API Key”
- 20. Register to Modaland Set up HOPSWORKS_API_KEY environment variable 20 Add HOPSWORKS_API_KEY as a Environment variable secret Create an account on Modal (might need some time to be approved)
- 21. Register and Createa Hugging Face Space 2 1. Create an account on Hugging Face 2. Create a “Space” 3. Create a Gradio App with the name Iris inside your account
- 22. Add a HOPSWORKS_API_KEYas a secret in your “iris” Space 22 1. Add your HOPSWORKS_API_KEY as a Repo Secret
- 23. Serverless ML with IrisFlower Dataset
- 24. Iris Flower Dataset TabularData Features ● sepal length ● sepal width ● petal length ● petal width Target (label) ● variety This column is the Pandas Index Prediction Problem: Predict the variety, given the length and width of the petal and sepal.
- 25. [Image from https://scikit-learn.org/stable/modules/neighbors.html#classification] Aswe can see here two features (sepal_length and sepal_width) is not enough features to separate the three different varieties (setosa, versicolor, virginica). Classify Iris Flowers with K-Nearest Neighbors
- 26. Communicate the valueof your model with a UI (Gradio) ● Communicate the value of your model to stakeholders with an app/service that uses the ML model to make value-added decisions ● Here, we design a UI in Python with Gradio ○ Enables “predictive analytics” where a user can use the model to as “what-if” i had an Iris Flower with this sepal/petal width/length?
- 27. Run the Feature,Training, Online/Batch Inference Pipelines iris-feature-pipeline.py app.hopsworks.ai iris/app.py iris-training-pipeline.py iris_model features, labels iris_model features, labels iris-batch-inference-pipeline.py features predictions iris-monitoring/app.py logs
- 28. Serverless Surf HeightPrediction: Github Actions/Pages surf-report-features.ipynb swell-features.ipynb batch-predict-surf.ipynb Github Pages Feature Store Lahinch, NOAA Model Registry download model latest_lahinch.png insert DataFrames https://github.com/jimdowling/cjsurf train-model.ipynb add model SERVERLESS COMPUTE SERVERLESS STATE SERVERLESS UI
- 29. SERVERLESS ML Show lovewith a star! www.serverless-ml.org https://github.com/featurestoreorg/serverless-ml-course ⭐