The document discusses Microsoft's Azure AI offerings, emphasizing advanced machine learning and data analytics capabilities that enable data science teams to develop and deploy AI models efficiently. It highlights the use of popular frameworks such as TensorFlow and PyTorch, as well as services like Azure Databricks and Azure Machine Learning, to support data preparation, training, and model management across cloud and edge environments. The text promotes various tools and integrations to simplify data science workflows, improve collaboration, and enhance productivity in AI application development.

1. SATO Naoki (Neo) - @satonaoki
Microsoft Confidential

2. Vision Speech Language

3. Data AI

4. Data AI
Data modernization to Azure
Globally distributed data
Cloud Scale Analytics
Data Modernization on-premises AI apps & agents
Knowledge mining

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
Azure Notebooks Jupyter
Familiar Data Science tools
To simplify model development
Visual Studio Code Command line
CPU GPU FPGA
From the Intelligent Cloud to the Intelligent Edge

6. Infuse apps with powerful, pre-trained AI models
Customize easily and tailor to your needs
Vision
Speech
Language
Bing
Search
…
Computer Vision | Video Indexer | Face | Content Moderator
Speech to Text | Text to Speech | Speech Translation | Speaker Recognition
Text Analytics | Spell Check | Language Understanding | Text Translation | QnA Maker
Big Web Search | Video Search | Image Search | Visual Search | Entity Search |
News Search | Autosuggest

7. https://azure.microsoft.com/blog/bringing-ai-to-the-edge/

8. https://azure.microsoft.com/updates/cognitive-services-custom-vision-is-now-available/

9. https://azure.microsoft.com/blog/azure-media-services-the-latest-video-indexer-updates-from-nab-show-2019/

10. https://azure.microsoft.com/blog/azure-media-services-the-latest-video-indexer-updates-from-nab-show-2019/

11. https://azure.microsoft.com/services/cognitive-services/anomaly-detector/

12. 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

13. Choose any python development environment
And improve data science productivity
PyCharmAzure NotebooksVisual Studio Code Command lineZeppelin
Interactive widgets for Jupyter Notebooks Azure Machine Learning for Visual Studio Code extension
Jupyter
Get started with AML on Azure Notebooks: http://aka.ms/aznotebooks-aml

14. 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

15. Build advanced deep learning solutions
Use your favorite deep learning frameworks without getting locked into one framework
ONNX
Community project created by Facebook and Microsoft
Use the best tool for the job. Train in one framework
and transfer to another for inference
TensorFlow PyTorch Scikit-Learn
MXNet Chainer Keras

16. https://azure.microsoft.com/blog/world-class-pytorch-support-on-azure/

17. https://azure.microsoft.com/blog/microsoft-joins-the-scikit-learn-consortium/

18. Frameworks Azure
Create Deploy
Services
Devices
Azure Machine Learning services
Ubuntu VM
Windows Server 2019 VM
Azure Custom Vision Service
ONNX Model
Windows devices
Other devices (iOS, etc.)

19. https://azure.microsoft.com/blog/onnx-runtime-is-now-open-source/

20. 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

21. +
To empower data science and development teams
Develop models faster with automated machine learning
Use any Python environment and ML frameworks
Manage models across the cloud and the edge.
Prepare data clean data at massive scale
Enable collaboration between data scientists and data engineers
Access machine learning optimized clusters
Azure Machine Learning
Python-based machine learning service
Azure Databricks
Apache Spark-based big-data service

22. Bring AI to everyone with an end-to-end, scalable, trusted platform
Built with your needs in mind
Support for open source frameworks
Managed compute
DevOps for machine learning
Simple deployment
Tool agnostic Python SDK
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

23. https://azure.microsoft.com/blog/azure-machine-learning-service-a-look-under-the-hood/

24. Fast, easy, and collaborative Apache Spark™-based analytics platform
Built with your needs in mind
Optimized Apache Spark environmnet
Collaborative workspace
Integration with Azure data services
Autoscale and autoterminate
Optimized for distributed processing
Support for multiple languages and libraries
Seamlessly integrated with the Azure Portfolio
Increase productivity
Build on a secure, trusted cloud
Scale without limits

25. https://azure.microsoft.com/blog/spark-ai-summit-developing-for-the-intelligent-cloud-and-intelligent-edge/

26. Leverage your favorite deep learning frameworks
AZURE ML SERVICE
Increase your rate of experimentation
Bring AI to the edge
Deploy and manage your models everywhere
TensorFlow MS Cognitive Toolkit PyTorch Scikit-Learn ONNX Caffe2 MXNet Chainer
AZURE DATABRICKS
Accelerate processing with the fastest Apache Spark engine
Integrate natively with Azure services
Access enterprise-grade Azure security

27. What to use when?
+
Customer journey Data Prep Build and Train Manage and Deploy
Apache Spark / Big Data
Python ML developer
Azure ML service
(Pandas, NumPy etc. on AML Compute)
Azure ML service
(OSS frameworks, Hyperdrive, Pipelines,
Automated ML, Model Registry)
Azure ML service
(containerize, deploy,
inference and monitor)
Azure ML service
(containerize, deploy,
inference and monitor)
Azure Databricks
(Apache Spark Dataframes,
Datasets, Delta, Pandas, NumPy etc.)
Azure Databricks + Azure ML service
(Spark MLib and OSS frameworks +
Automated ML, Model Registry)

28. 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

29. Accelerate deep learning
General purpose
machine learning
D, F, L, M, H Series
CPUs
Optimized for flexibility Optimized for performance
GPUs FPGAs
Deep learning
N Series
Specialized hardware
accelerated deep learning
AML hardware accelerated
models (Project Brainwave)

30. Simplify deployment + accelerate inferencing
with ONNX runtime
Track models in production
Capture model telemetry
From the Intelligent Cloud to the Intelligent Edge
On-premisesCloud

31. https://azure.microsoft.com/blog/accelerated-ai-with-azure-machine-learning-service-on-azure-data-box-edge/

32. Automated machine learning Machine learning DevOps
Machine Learning
95
%
Accelerated model building Azure DevOps integration for CI/CD

33. Automated machine learning Machine learning DevOps
Machine Learning
95
%
Accelerated model building Azure DevOps integration for CI/CD

34. How much is this car worth?
Azure Machine Learning
Automated machine learning

35. 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

36. 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

37. Which algorithm? Which parameters?Which features?
Iterate
Model creation is typically a time consuming process

38. 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

39. 70%95%
Azure Machine Learning accelerates model selection
with model explainability
Feature importance
Mileage
Condition
Car brand
Year of make
Regulations
Model B (70%)
Mileage
Condition
Car brand
Year of make
Regulations
Feature importance Model A (95%)

40. Automated machine learning Machine learning DevOps
Machine Learning
95
%
Accelerated model building Azure DevOps integration for CI/CD

41. ServeStore Prep and trainIngest
Batch data
Streaming data
Azure Kubernetes
service
Power BI
Azure analysis
services
Azure SQL data
warehouse
Cosmos DB, SQL DB
Azure Data Lake Storage
Azure Data Factory
Azure Event
Hubs
Azure Databricks Azure Machine
Learning service
Apps
Model Serving
Ad-hoc Analysis
Operational
Databases

42. Register and
Manage Model
Build Image
Build model
(your favorite IDE)
Deploy Service
Monitor Model
Train &
Test Model
Integrated with
Azure DevOps

43. Prepare
Data
Register and
Manage Model
Train &
Test Model
Build
Image
…
Build model
(your favorite IDE)
Deploy Service
Monitor Model
Prepare Experiment Deploy
DevOps loop for data science

44. DevOps loop for data science
Prepare
Data
Prepare
Register and
Manage Model
Build
Image
…
Build model
(your favorite IDE)
Deploy Service
Monitor Model
Train &
Test Model

45. Model management in Azure Machine
Learning Create/retrain model
Create scoring
files and
dependencies
Create and register image
Monitor
Register model
Cloud Light
Edge
Heavy
Edge
Deploy
image

46. Use leaderboards, side by side run comparison
and model selection
Conduct a hyperparameter search on
traditional ML or DNN
Leverage service-side capture of run metrics,
output logs and models
Manage training jobs locally, scaled-up or
scaled-out
Experimentation
95
%
80%
75%
90%
85%

47. Azure Databricks Remote support for other IDEs outside of native notebooks
MLFlow for better DevOps with Azure Databricks and other ML pipelines
Azure Machine Learning Python SDK support for popular IDEs & notebooks, including Azure Databricks
Azure Machine Learning managed compute capabilities
Introduce new models for FPGA scoring
Robust ONNX support - runtime engine in AML, model operationalization in SQL Server
Automated machine learning
Deploy and manage models to IoT edge
Extend Machine Learning services to SQL DB

48. Microsoft Learn https://docs.microsoft.com/learn/azure/

49. AI School https://aischool.microsoft.com/

50. Microsoft AI Lab https://www.ailab.microsoft.com/

51. AI for Good Idea Challenge https://www.ailab.microsoft.com/challenge

52. Build 2019 https://www.microsoft.com/build

53. de:code 2019 https://www.microsoft.com/ja-jp/events/decode/2019/