201906 03 Introduction to NimbusML
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Nimbus ML is a machine learning framework that provides advanced algorithms and transforms aimed at simplifying model development for developers and data scientists. It is interoperable with popular frameworks like scikit-learn and supports handling large datasets through file-based streaming. The platform offers tools for training, deployment, and collaboration, optimized for performance and scalability within Azure environments.
201906 03 Introduction to NimbusML
- 2. Mark Tabladillo Ph.D. • Science doctorate from Georgia Tech • Analytics career based on SAS, Microsoft, open source • Tech Presentations: • Seattle, Portland, Chicago, Boston, Mountain View, San Francisco, San Antonio, Charlotte, Orlando • London, Hong Kong, Montreal • Social Media LinkedIn Twitter @marktabnet • Cloud Solution Architect • US CTO Customer Success
- 3. Microsoft Atlanta at Avalon
- 5. 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
- 7. About NimbusML • NimbusML provides state-of-the-art ML algorithms, transforms and components, aiming to make them useful for all developers, data scientists, and information workers and helpful in all products, services and devices. • The components are authored by the team members, as well as numerous contributors from MSR, CISL, Bing and other teams at Microsoft. • nimbusml is interoperable with scikit-learn estimators and transforms, while adding a suite of highly optimized algorithms written in C++ and C# for speed and performance.
- 8. NimbusML Features NimbusML trainers and transforms support the following data structures for the fit() and transform() methods: • numpy.ndarray • scipy.sparse_cst • pandas.DataFrame. NimbusML also supports streaming from files without loading the dataset into memory, which allows training on data significantly exceeding memory using FileDataStream. • With FileDataStream, NimbusML is able to handle up to billion features and billions of training examples for select algorithms
- 12. Training of Python scikit-learn models on Azure
- 13. Deploy Azure ML models at scale Azure Machine Learning Service
- 14. Model deployment
- 16. Action