In this presentation you'll find Machine Learning / Deep Learning tools and services from Microsoft. Including Azure Machine Learning Workbench, Azure Notebooks, Azure Data Science Virtual Machines and more.
Here are the demos & resources
https://github.com/ikivanc/Azure-ML-Workbench-Iris-Dataset-Classification
https://github.com/ikivanc/Azure-ML-Resources
4. Agent Applications Services Infrastructure
Microsoft AI Portfolio
Cortana Office 365
Dynamics 365
SwiftKey
Pix
Customer Service
and Support
Skype
Calendar.help
Cortana Intelligence
Cognitive Services
Bot Framework
Cortana Devices SDK
Cognitive Toolkit
Azure Machine
Learning
Azure Notebooks
Azure N Series
FPGA
People
5.
6. Agent Applications Services Infrastructure
Microsoft AI Portfolio
Cortana Office 365
Dynamics 365
SwiftKey
Pix
Customer Service
and Support
Skype
Calendar.help
Cortana Intelligence
Cognitive Services
Bot Framework
Cortana Devices SDK
Cognitive Toolkit
Azure Machine
Learning
Azure Notebooks
Azure N Series
FPGA
People
7. H Y P E R S C A L E ,
E N T E R P R I S E - G R A D E
I N F R A S T R U C T U R E
D E V E L O P E R T O O L S &
S E R V I C E S
O P E N P L A T F O R M F O R
D A T A S C I E N C E
Hardware
Storage management
Software
T H E M L & A I P L A T F O R M
AI Applications (1st & 3rd party) Cognitive Services Bot Framework
Spark AI Batch Training DS VM SQL Server ACS
BLOB Cosmos DB SQL DB/DW ADLS
CPUs FPGA GPUs IoT
Azure Machine Learning
Model deployment & management
Machine Learning toolkits
Experimentation management,
data prep, & collaboration
CNTK
Tensorflow
ML Server
Scikit-Learn
Other Libs.
PROSE
Docker
Cloud – Spark, SQL, other engines
ML Server – Spark, SQL, VMs
Edge
8.
9. Machine Learning & AI Portfolio
When to use what?
What engine(s) do you want to use?
Deployment target
Which experience do you want?
Build your own or consume pre-trained models?
Microsoft ML &
AI products
Build your own
Azure Machine
Learning
Code first
(On-prem)
ML Server
On-prem
Hadoop
SQL Server
(cloud)
AML (Preview)
SQL Server Spark Hadoop Azure Batch DSVM Azure Container
Service
Visual tooling
(cloud)
AML Studio
Consume
Cognitive
services, bots
12. Use your favorite IDE
Leverage all types of data
Use what you want
U S E T H E M O S T P O P U L A R I N N O V A T I O N S
U S E A N Y T O O L
U S E A N Y F R A M E W O R K O R L I B R A R Y
13.
14.
15. AML Workbench
Sample, understand, and
prep data rapidly
Support for Spark + Python
+ R (roadmap)
Execute jobs locally, on
remote VMs, Spark clusters,
SQL on-premises
Git-backed tracking of
code, config, parameters,
data, run history
16. • Column statistics : Numeric
• Histogram
• Value Counts
• Box Plot
• Scatter Plot
• Time Series
• Map
Inspectors
17.
18. Spark
SQL Server
Virtual machines
GPUs
Container services
Notebooks
Azure Machine Learning Workbench
Visual Studio Code Tools for AI
Visual Studio Tools for AI
PyCharm
SQL Server
Machine Learning Server
O N - P R E M I S E S
E D G E C O M P U T I N G
Azure IoT Edge
Experimentation and
Model Management
A Z U R E M A C H I N E L E A R N I N G S E R V I C E S T R A I N & D E P LO Y O P T I O N S
A Z U R E
19. Local machine
Scale up to DSVM
Scale out with Spark on HDInsight
Azure Batch AI (Coming Soon)
ML Server (Coming Soon)
A ZURE ML
EXPERIMENTATION
Command line tools
IDEs
Notebooks in Workbench
VS Code Tools for AI
VS Tools for AI
25. Researchers took a traditional machine learning approach
• Example: HoG Detectors
- Histogram of oriented
gradients (HoG) features
- Sliding window detector
- SVM Classifier
- Very fast OpenCV
implementation (<100ms)
26.
27. Deep Neural Network for Computer Vision
cat? YES
dog? NO
car? NO
Convolutional Layers
Fully
Connected
Layers
Complex Objects
& Scenes
(people, animals,
cars, beach
scene, etc.)
Image
Low-Level Features
(lines, edges,
color fields, etc.)
High-Level Features
(corners, contours,
simple shapes)
Object Parts
(wheels, faces,
windows, etc.)
28.
29.
30.
31. Cognitive Toolkit
Unlock deeper learning
A free, easy-to-use, open-source toolkit that
trains deep learning algorithms to learn like the
human brain.
Microsoft Cognitive Toolkit
32. Clothing texture dataset:
Can we apply transfer learning to accurately classify clothing texture?
LeopardStriped Dotted
33. Pre-Built CNN from General Task on Millions of Images
Output
Layer
Stripped
Outputs of penultimate layer of
ImageNet Trained CNN provide excellent
general purpose image features
cat? YES
dog? NO
car? NO
Classi
fier
e.g.
SVM
dotted?
34. Pre-Built CNN from General Task on Millions of Images
Output
Layer
Stripped
Using a pre-trained DNN, an accurate
model can be achieved with thousands (or
less) of labeled examples instead of millions
cat? YES
dog? NO
car? NO
dotted?
Train one or more
layers in new network