MLflow on and inside Azure
During this presentation, after walking through a few ways to use MLflow on Azure directly, we'll cover how upcoming solutions from our group leverage MLflow for core functionality. BenchML is a new repository that aims to provide consumers of prebuilt ML endpoints visibility into the performance of each public offering for a given dataset as well as comparing results across multiple offerings. Using MLflow, BenchML is able to remain cloud-agnostic and offer a delightful local experience while leveraging the aforementioned integration to provide Azure users with a fully managed experience. Speaker Bio: Akshaya is an engineer in the AI Platform at Microsoft, having released both GA versions of Azure Machine Learning over the years and the OSS repo MMLSpark. As the recent version of Azure ML pivoted to become more of an open platform rather than a managed product, his focus has shifted outward for open-source platform definitions for cloud-scale implementations and focused on MLflow for the Azure ML managed tracking store. This talk was presented at the Bay Area MLflow Meetup at Databricks HQs in San Francisco: https://www.meetup.com/Bay-Area-MLflow/events/266614106/
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MLflow on and inside Azure
- 3. Cloud Azure ML Azure Databricks SQL Server Big Data Clusters Edge Azure Cognitive Services Containers SQL Database Edge Frameworks ONNX MLflow
- 5. Machine Learning Accelerate the end-to-end machine learning lifecycle Databricks Best destination for big data analytics and AI with Apache Spark SQL Server Deploy scalable clusters of SQL Server, Spark, and HDFS containers running on Kubernetes
- 6. Built with your needs in mind Support for open source frameworks Managed compute MLOps (DevOps for machine learning) Simple deployment Automated machine learning Seamlessly integrated with the Azure Portfolio Boost your data science productivity Increase your rate of experimentation Deploy and manage your models everywhere Tool agnostic Python SDK
- 7. Experiments Local machine Virtual machine Azure ML Compute Azure Databricks ML Tracking and Model Deployment Azure Machine Learning Experiments and Metrics Tracking Metrics Artifacts Logging API Tracking URI
- 9. • v1.0.0 released ONNX Runtime • ONNX flavor support, discussed at the previous meetup MLflow + ONNX • Preview, Native PREDICT with ONNX models SQL Database Edge • Azure ML + Cognitive Services • Custom Vision as ONNX Exportable Containers
- 15. Measure Quality Platform for measuring quality of cognitive services solving the same problem across vendors Support major evaluation metrics for each service Offer Reproducibility Track and reproduce these results over time Adopt industry-standard, open source implementations Answer Common Questions How well does each Cognitive Service work against my dataset? What is the best choice of parameters, thresholds and normalizations for my scenario? Explain differences between our results and those of our competitors
- 22. 001010101001010010101001010010111011010010010010110101001000 101001000100011110100101010100101001010100101001011101101001 Prepare Data Prepare Register and Manage Model Build Image Build model (your favorite IDE) Deploy Service Monitor Model Train & Test Model
Notas do Editor
- * We’ve shown off a few different
- Azure Machine Learning Services empowers you to bring AI to everyone with an end-to-end, scalable, trusted platform. Boost your data science productivity Python pip-installable extensions for Azure Machine Learning that enable data scientists to build and deploy machine learning and deep learning models Now available for Computer Vision, Text Analytics and Time-Series Forecasting. Increase your rate of experimentation Rapidly prototype on your desktop, then easily scale up on virtual machines or scale out using Spark clusters Proactively manage model performance, identify the best model, and promote it using data-driven insights Collaborate and share solutions using popular Git repositories. Deploy and manage your models everywhere Use Docker containers to deploy models into production faster in the cloud, on-premises, or at the edge Promote your best performing models into production and retrain them when their performance degrades Azure Machine Learning Services are built with your needs in mind, providing: GPU-enabled virtual machines Low-latency predictions at scale Integration with popular Python IDEs Role-based access controls Model versioning Automated model retraining (Optional: other services) Azure Machine Learning Workbench integrates with ONNX models Work with your ONNX models from Visual Studio Code Tools for AI. Build deep learning models and call services straight from your favorite IDE easier with Azure Machine Learning services built right in. Create a seamless developer experience across desktop, cloud, or at the edge. AI Toolkit for Azure IoT Edge MMLSpark is an open-source Spark package that enables you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets by using deep learning and data science tools for Apache Spark. Azure Machine Learning Services seamlessly integrates with the rest of the Azure portfolio. <Transition>: Azure Machine Learning Services allows you to deploy models to many different production environments.
- Here is the data scientist’s inner loop of work