4. 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
5. Democratizing AI
Optimized for Data Scientist Optimized for specific use casesAML Platform
AutoML Cognitive
Services
Power BIVisual Interface Python
Notebooks
More personas building more models
Developers Data ScientistData Professional Data Analyst
6. 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
7. Q: How much is this car worth?
Building your own AI models
Transforming Data into Intelligence
8. SQL DB
Cosmos DB
Datawarehouse
Data lake
Blob storage
…
Building your own AI models
Transforming data into intelligence
Prepare data Build and train Deploy
9. Building your own AI models
Transforming data into intelligence
Prepare data Build and train Deploy
17. What are Hyperparameters?
Adjustable parameters that govern model training
Chosen prior to training, stay constant during training
Model performance heavily depends on hyperparameter
18. The search space to explore—i.e. evaluating all possible
combinations—is huge.
Sparsity of good configurations.
Very few of all possible configurations are optimal.
Evaluating each configuration is resource and time
consuming.
Time and resources are limited.
Challenges with Hyperparameter Selection
19. 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
20. 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
21. Which algorithm? Which parameters?Which features?
Iterate
Model creation is typically a time consuming process
22. 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
24. 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