1.
Introduction
to Nimbus
ML

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

4.
© Microsoft Corporation

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

6.
Agenda
About NimbusML
Demos
Action

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

9.
Demo
https://github.com/ganik/NimbusML-
Presentation

10.
Demo
https://github.com/Microsoft/NimbusML

11.
Enterprise training and
deployment

12.
Training of Python scikit-learn models on Azure

13.
Deploy Azure ML models at scale
Azure Machine Learning Service

14.
Model deployment

15.
https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/

16.
Action

17.
Nimbus ML Documentation
https://docs.microsoft.com/en-us/NimbusML/overview

18.
NimbusML on Github
https://github.com/Microsoft/NimbusML

19.
Gitter
https://gitter.im/dotnet/mlnet

20.
The Next Talk
https://attendee.gotowebinar.com/register/4993774405253843981