Building an ML model with zero code
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This document discusses how to build machine learning models with zero lines of code using Microsoft's Azure Machine Learning service. It introduces domain-specific pretrained models, popular frameworks, productive services, and familiar data science tools available in Azure ML that can simplify and accelerate the model development process. It also demonstrates the visual interface and automated machine learning capabilities of Azure ML that allow users to build, test, and deploy models without writing code.
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Building an ML model with zero code
- 1. Building an ML model with zero lines of code Nick Trogh Developer Audience PMM, Microsoft @nicktrog
- 2. What is Machine Learning? Computing systems that become smarter with experience “Experience” = past data + human input
- 4. Domain specific pretrained models To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn Familiar Data Science tools To simplify model development CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge Azure Notebooks JupyterVisual Studio Code Command line
- 5. Democratizing AI Optimized for Data Scientist Optimized for specific use casesAML Platform AutoML Cognitive Services Power BIVisual Interface Python Notebooks More personas building more models Developers Data ScientistData Professional Data Analyst
- 6. Domain specific pretrained models To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar Data Science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Scikit-Learn Azure Notebooks JupyterVisual Studio Code Command line CPU GPU FPGA
- 7. Q: How much is this car worth? Building your own AI models Transforming Data into Intelligence
- 8. SQL DB Cosmos DB Datawarehouse Data lake Blob storage … Building your own AI models Transforming data into intelligence Prepare data Build and train Deploy
- 9. Building your own AI models Transforming data into intelligence Prepare data Build and train Deploy
- 10. X Y Model Building your own AI models Step 2: Build and Train
- 11. Visual Interface for Azure Machine Learning Service Collaborative, drag-n-drop tool to build, test, and deploy ML models
- 12. Benefits Installation-free solution Streamlined workflow Code-free Data Science Scale from small to large models & datasets 1-click deployment from Cloud to Edge Wide range of ML algorithms
- 13. DEMO Visual Interface for Azure Machine Learning Service
- 14. How much is this car worth? Azure Machine Learning Automated machine learning
- 15. Building your own AI models Step 2: Build and train Model Accuracy
- 17. What are Hyperparameters? Adjustable parameters that govern model training Chosen prior to training, stay constant during training Model performance heavily depends on hyperparameter
- 18. The search space to explore—i.e. evaluating all possible combinations—is huge. Sparsity of good configurations. Very few of all possible configurations are optimal. Evaluating each configuration is resource and time consuming. Time and resources are limited. Challenges with Hyperparameter Selection
- 19. Model creation is typically a time consuming process Mileage Condition Car brand Year of make Regulations … Parameter 1 Parameter 2 Parameter 3 Parameter 4 … Gradient Boosted Nearest Neighbors SGD Bayesian Regression LGBM … Mileage Gradient Boosted Criterion Loss Min Samples Split Min Samples Leaf XYZ Model Which algorithm? Which parameters?Which features? Car brand Year of make
- 20. Which algorithm? Which parameters?Which features? Mileage Condition Car brand Year of make Regulations … Gradient Boosted Nearest Neighbors SGD Bayesian Regression LGBM … Nearest Neighbors Criterion Loss Min Samples Split Min Samples Leaf XYZ Model Iterate Gradient Boosted N Neighbors Weights Metric P ZYX Mileage Car brand Year of make Model creation is typically a time consuming process Car brand Year of make Condition
- 21. Which algorithm? Which parameters?Which features? Iterate Model creation is typically a time consuming process
- 22. Enter data Define goals Apply constraints Azure Machine Learning accelerates model development with automated machine learning Input Intelligently test multiple models in parallel Optimized model
- 24. Domain specific pretrained models To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar Data Science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Scikit-Learn Azure Notebooks JupyterVisual Studio Code Command line CPU GPU FPGA
- 25. How to Get Started • https://aka.ms/devroadshow/ml