From idea to production in a day – Leveraging Azure ML and Streamlit to build and user test machine learning ideas quickly
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How to leverage Azure ML, automated machine learning, and Streamlit to build and test machine learning apps quickly? Find out about our favorite Hackathon stack and walk away with some code to build and user-test your own machine learning ideas fast. Experimentation, bringing machine learning ideas in front of users, is essential to innovation. Yet, in our corporate hackathons, our data science team has struggled many times with how to build and deploy user-facing machine learning ideas in just a single day. Over the past 2+ years, we have developed a routine around using Azure Machine Learning, automated machine learning, and Streamlit to build and user test machine learning ideas quickly. The aim of this talk is to pass on practical, technical knowledge to fellow data scientists about how to leverage this stack to achieve high build and user test speeds. During the talk, we will walk through the process of building a computer vision system for identifying trash in images via an app using the open-source TACO dataset (http://tacodataset.org/). Working through a Jupyter notebook, we will load the data into Azure Machine Learning and trigger an automated machine learning run on the data. In this context, we will quickly get to know the training and testing metrics available in Azure ML to evaluate the model. We will then download the machine learning model as a file packaged in the open-source ONNX format (https://onnx.ai/). Using the open-source Python web application framework Streamlit (https://github.com/streamlit/streamlit), we will program an application in which users can upload images and embed the machine learning model in it to identify trash in these images. Using a to-be-published infrastructure-as-code pipeline on Azure DevOps, we will deploy the application to the public internet on the Azure platform. From here, users can test it. The stack and code presented in this talk will enable fellow data scientists to accelerate their data science development, leading to quicker experimentation and, therefore, to faster innovation of products with machine learning at their core.
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From idea to production in a day – Leveraging Azure ML and Streamlit to build and user test machine learning ideas quickly
- 1. 1 From idea to production in a day Leveraging Azure ML and Streamlit to build and user test machine learning ideas quickly Florian Roscheck PyCon DE & PyData Berlin 2024
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- 4. 4 How do we use it to build + test quickly? What is our tech stack? What are we building?
- 5. 5 Hi, I’m Florian! Sr. Data Scientist Florian Roscheck • Sr. Data Scientist at Henkel • Instructor for Apache Spark with 7k+ students • Vice President NumFOCUS Affiliated Project Selection Committee • Active on LinkedIn
- 6. 6 WHAT TO BUILD IN ONE DAY? A Minimum Viable Product • Enough features to be usable • Ability to collect user feedback
- 7. 7 WHAT TO BUILD IN ONE DAY? A Minimum Viable Product BUILD M E A - S U R E LEARN To learn about users quickly, we want to implement build-measure-learn loop To make users happier over time, we aim to create data flywheel
- 8. 8 Ready?
- 9. 9 Ready? Data not in place Environment issues Lost in modeling Inappropriate user interface No feedback about use Difficult collaboration
- 10. 10 BUILD M E A - S U R E LEARN Data not in place Environment issues Lost in modeling Inappropriate user interface No feedback about use Difficult collaboration GET DATA BEFOREHAND
- 11. 11 HOW TO BUILD IN ONE DAY? A Time-Saving Stack Environment issues Lost in modeling Inappropriate user interface No feedback about use Difficult collaboration Azure ML Notebooks Automated ML on Azure Streamlit Azure Application Insights + Streamlit
- 12. 12 Let’s build!
- 13. 13 Example: Trash Recognizer App bottle • Customer: Waste management company • Need: Want to evaluate computer vision solutions for recognizing trash • Idea: Waste management professionals manually evaluate performance through app with feedback functionality
- 14. 14 BUILD M E A - S U R E LEARN 1 Get Data 2 Train Model 3 Build App 4 Deploy App with Model 5 Collect Feedback Our Plan
- 15. 15 BUILD M E A - S U R E LEARN 1 Get Data 2 Train Model 3 Build App 4 Deploy App with Model 5 Collect Feedback Our Plan
- 16. 16 Training Data: TACO Trash Image Dataset • TACO: Trash Annotations in Context • Dataset of 1.5k images with 4.7k+ annotations • Annotations for 60 categories, incl. backgrounds • Open source Proença, P . F., & Simões, P . (2020). TACO: Trash Annotations in Context for Litter Detection. arXiv Preprint arXiv:2003.06975. tacodataset.org Source: tacodataset.org
- 18. 18 What is Azure ML? • Cloud-based ML platform by Microsoft • Run ad-hoc analyses with Jupyter Notebooks • Run and track machine learning experiments through tight integration with MLFlow • Version data and models • Build complex and reproducible modeling pipelines • Deploy models as API Screenshot of Azure ML web app
- 19. 19 Basics of Getting Data Data Asset TACO-annotations • Azure ML-managed • Like a mask for files • Sharable • Version controlled • Interactively explorable TACO GitHub repo ! Azure ML Workspace Azure blob storage Azure ML Notebook 0_prepare_dataset.ipynb • Like Jupyter Notebook • Managed environment • Sharable • Runs on compute in workspace Reproducible environment Collaboration- ready ENABLERS
- 20. 20 Movie
- 21. 21 BUILD M E A - S U R E LEARN 1 Get Data 2 Train Model 3 Build App 4 Deploy App with Model 5 Collect Feedback Our Plan
- 23. 23 !
- 24. 24 Automated Machine Learning on Azure ML • Automated ML: Try different models and hyperparameters that are automatically selected • We have very little time for modeling! • Depending on data and problem type, automated machine learning can provide a reasonable starting point for modeling with a high return on time investment • Azure ML offers automated ML pipelines for several common tasks, incl. classification, regression, forecasting, NLP , and computer vision Modeling time saver ENABLERS
- 25. 25 Setting Up AutoML Through Code Create compute cluster 1 Define training job 2 Submit job to compute cluster 3 Azure ML Notebooks 1_training.ipynb Azure ML Workspace TACO-annotations TACO-training
- 26. 26 Compute Cluster Creation Tips & Tricks Save Money Shut down unused instances 120 seconds to auto shutdown Pick auto-evictable machine (own case: 80% cheaper) 4 experiments can run in parallel Tesla T4 GPU w/ 16 GB memory, 56 GB RAM, 8 vCPUs, but many options available! Pick a Fitting Compute
- 27. 27 Increasing Efficiency for Automated ML on Azure • Set ML parameters based on your data science knowledge • Train/test/validation split, cross-validation, etc. • Hyperparameter selection strategy • Restrict hyperparameter search space • Set job limits • Max no. of trials • Max runtime per trial or of all trials • Termination based on score
- 28. 28 3 Hours Later
- 29. 29 Our annotations, linked to the job MLFlow model!
- 30. 30 Models ordered by performance Azure AutoML experimented with a single model type
- 32. 32 How to Dig Deeper • Metrics are comprehensive and look great – but what are we looking at? • More details in the logs: • Tip: Read Azure ML documentation! • You still need data science knowledge to understand what Azure ML is doing here. Section of std_log.txt file in Outputs + logs tab
- 34. 34 BUILD M E A - S U R E LEARN 1 Get Data 2 Train Model 3 Build App 4 Deploy App with Model 5 Collect Feedback Our Plan
- 35. 35 The Power of ONNX for Model Packaging • Great: Azure ML packaged model in MLFlow format • The Issue: Tight MLFlow model dependencies restrict platforms where model can be used • 204 (!) pinned dependencies, incl. 31 Azure-specific packages • Experienced issues installing some (azureml-dataprep-native) on macOS (M1) • The Solution: Use ONNX model file (byproduct of Azure AutoML training) and use it with a single dependency: onnxruntime • ONNX (Open Neural Network Exchange): Open standard for deep learning models, makes models work across frameworks • ONNX Runtime: Cross-platform, open source ML model accelerator You can now use your AutoML-trained model outside of Azure! Flexible model deployment ENABLERS
- 36. 36 BUILD M E A - S U R E LEARN 1 Get Data 2 Train Model 3 Build App 4 Deploy App with Model 5 Collect Feedback Our Plan
- 37. 37 Building an App with Streamlit • Streamlit is an open-source app framework for creating data-based web apps in Python • My experience with Streamlit: • The Good: Very easy and fast to code and use, apps look great and work – Wow! • The Good-to-Know: Complex workflows with state management harder to program, may be perceived as slow by users in comparison to “professional” web apps • Streamlit is perfect for getting a user-facing app off the ground and testing your data-based product ideas! Streamlit logo, see streamlit.io
- 39. 39 Streamlit App Blueprint Trash Recognizer Upload image(s) Detected Trash - 2 items for yellow trash can - 1 item for blue trash can No trash detected. Detected Trash - 1 item for other trash can [Model + data description] Load ONNX model 1 Preprocess images 2 Run model inference 3 Postprocess images 4 What the user sees What the app does Display results 5
- 41. 41 BUILD M E A - S U R E LEARN 1 Get Data 2 Train Model 3 Build App 4 Deploy App with Model 5 Collect Feedback Our Plan
- 43. 43 Deployment Pipeline • Henkel Data Science Engineering team developed pipeline for one-click deployment of data science infrastructure, incl. Streamlit apps, on secure Azure cloud infrastructure • Open sourced via article series “Kickstarting Data Science Projects in Azure DevOps” by Roberto Alonso • Part 1 and 2 already available on Henkel Data & Analytics Blog medium.com/henkel-data-and-analytics medium.com/henkel-data-and-analytics
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- 45. 45 BUILD M E A - S U R E LEARN 1 Get Data 2 Train Model 3 Build App 4 Deploy App with Model 5 Collect Feedback Our Plan
- 46. 46 Collecting Feedback streamlit-feedback Azure Application Insights Python logging Azure Dashboards Open-source feedback plugin for Streamlit Use AzureLogHandler through opencensus logging extension Collect logs from application, query with Kusto language Interactive live dashboards on Azure for application metrics
- 47. 47 Movie
- 49. 49 bottle BUILD M E A - S U R E LEARN Reproducible environment Collaboration- ready Modeling time saver Flexible model deployment Easy and fast measurement Easy-to-use interface Learning culture
- 50. 50 Code, Slides, Details PyData team + sponsors, Henkel, incl. Henkel Data Science CoE team, Open-source contributors for TACO, ONNX, ONNX Runtime, Streamlit, streamlit-feedback, Streamlit for reaching out before talk Learn More • Build-Measure-Learn Loop: The Lean Startup | Methodology • Data Flywheel: Data Flywheel: Scaling a world-class data strategy • Dataset: Tacodataset.org • Automated Machine Learning on Azure: What is automated ML? • ONNX: ONNX Runtime, ONNX File Format • Streamlit: Get started with Streamlit, streamlit-feedback • Azure Tricks for Data Science: Henkel Data & Analytics Blog • Logging to Azure from Python: Monitor Python applications • Azure Dashboards: Dashboards of Azure Log Analytics data • A similar project: Instance Segmentation with Azure Machine Learning github.com/flrs/build_and_test_ml_quickly Thanks Photo credits, in order of appearance: Greg Rakozy, Canva Studio, Sewupari Studio, Massimo Botturi, Charlotte Coneybeer, Desola Landre Ologun, Studio Saiz, Claudio Schwarz, NASA, Alena Darmel, Anna Shvetz, Vadim B, The Lucky Neko, Visual Tag Mx; User icon from “Redefining Women” icon collection by Iconathon
- 51. 51 What are your questions? Sr. Data Scientist linkedin.com/in/florianroscheck github.com/flrs Florian Roscheck Let’s connect!