1.
Data and AI Scientist @ Microsoft
Cloud Solution Architect
US CTO Customer Success
@marktabnet

2.
ML.NET Open Source Momentum
150K+
1,427
1,528
106

3.
ML.NET Customers
Andy Gray, Executive Partner Evolution
Software Design, Inc.

4.
DESKTOP CLOUDWEB MOBILE ML
.NET
Your platform for building anything
IoTGAMING

5.
“It has exquisite buttons …
with long sleeves …works for
casual as well as business
settings”{f(x) {f(x)
Machine Learning
“Programming the UnProgrammable”

6.
f(x)
Model
Machine Learning creates a
using this data
Machine Learning
“Programming the UnProgrammable”

7.
ML.NET 1.0
Machine Learning framework for building custom ML Models
Custom ML made easy
Automated ML and Tools (Model Builder and CLI)
Proven at scale
Azure, Office, Windows
Extensible
TensorFlow, ONNX and Infer.NET
Cross-platform and open-source
Runs everywhere

8.
1. Data
Example
Comment Text Sentiment
Wow... Loved this place. 1
Crust is not good. 0
Not tasty and the texture was just nasty. 0
The selection on the menu was great. 1

9.
Text Featurizer
Featurized Text
[0.76, 0.65, 0.44, …]
[0.98, 0.43, 0.54, …]
[0.35, 0.73, 0.46, …]
[0.39, 0, 0.75, …]
Example
Text
Wow... Loved this place.
Crust is not good.
Not tasty and the texture was just nasty.
The selection on the menu was great.
2. Transformers

10.
Example
Estimator
Comment Sentiment
Wow... Loved this place. 1
Crust is not good. 0
Not tasty and the texture was just nasty. 0
The selection on the menu was great. 0
3. Estimators

11.
Comment Text Sentiment
Wow... Loved this place. 1
Crust is not good. 0
Not tasty and the texture was just nasty. 0
The selection on the menu was great. 1
Yelp review dataset
Features (input) Label (output)
Sentiment Analysis
Is this a positive comment? Yes or no

12.
Building blocks for a Data Science Project
Data
sources

13.
What is automated machine
learning?
© Microsoft Corporation
Automated machine learning (automated ML) automates
feature engineering, algorithm and hyperparameter
selection to find the best model for your data.

14.
Automated ML Mission
Democratize AI Scale AIAccelerate AI
© Microsoft Corporation Azure
Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI
Enable Domain Experts & Developers to
get rapidly build AI solutions
Improve Productivity for Data Scientists,
Citizen Data Scientists, App Developers &
Analysts
Build AI solutions at scale in an automated
fashion

15.
How much is this car worth?
Machine Learning Problem Example

16.
Model Creation Is Typically Time-Consuming
Mileage
Condition
Car brand
Year of make
Regulations
…
Parameter 1
Parameter 2
Parameter 3
Parameter 4
…
Gradient Boosted
Nearest Neighbors
SVM
Bayesian Regression
LGBM
…
Mileage Gradient Boosted Criterion
Loss
Min Samples Split
Min Samples Leaf
Others Model
Which algorithm? Which parameters?Which features?
Car brand
Year of make

17.
Criterion
Loss
Min Samples Split
Min Samples Leaf
Others
N Neighbors
Weights
Metric
P
Others
Which algorithm? Which parameters?Which features?
Mileage
Condition
Car brand
Year of make
Regulations
…
Gradient Boosted
Nearest Neighbors
SVM
Bayesian Regression
LGBM
…
Nearest Neighbors
Model
Iterate
Gradient BoostedMileage
Car brand
Year of make
Car brand
Year of make
Condition
Model Creation Is Typically Time-Consuming

18.
Which algorithm? Which parameters?Which features?
Iterate
Model Creation Is Typically Time-Consuming

19.
Enter data
Define goals
Apply constraints
Output
Automated ML Accelerates Model Development
Input Intelligently test multiple models in parallel
Optimized model

20.
Automated ML Capabilities
• Based on Microsoft Research
• Brain trained with several
million experiments
• Collaborative filtering and
Bayesian optimization
• Privacy preserving: No need
to “see” the data

21.
Automated ML Capabilities
• ML Scenarios: Classification &
Regression, Forecasting
• Languages: Python SDK for
deployment and hosting for
inference – Jupyter notebooks
• Training Compute: Local
Machine, AML Compute, Data
Science Virtual Machine (DSVM),
Azure Databricks*
• Transparency: View run history,
model metrics, explainability*
• Scale: Faster model training
using multiple cores and parallel
experiments
* In Preview

22.
Data
Preprocessing
Feature
Engineering
Algorithm
Selection
Hyper-parameter
Tuning
Model
Recommendation
Interpretability
& Explaining
1. 2. 3. 4. 5. 6.
© Microsoft Corporation Azure
Automated ML

23.
Guardrails
Class imbalance
Train-Test split, CV, rolling CV
Missing value imputation
Detect high cardinality features
Detect leaky features
Detect overfitting
Model Interpretability / Feature Importance

24.
What’s new?

25.
Latest announcements @ MS Build (Blog post with all the announcements)
Automated ML in ML.NET Model
Builder (Preview)
• Train ML models from Visual Studio
• Inference from your application
© Microsoft Corporation Azure
ML.NET Model Builder

26.
ML.NET AutoML

27.
Automated Machine Learning (AutoML)

28.
On the command line, with the ML.NET CLI
mlnet auto-train --task binary-classification --dataset "yelp_labelled.txt" --label-column-index 1 --has-header
false --max-exploration-time 10

29.
With a graphical user interface, with the the ML.NET Model Builder

30.
https://github.com/dotnet/machinelearning-
samples/tree/master/samples/csharp/getting-
started/BinaryClassification_AutoML
Via an application, with the automated ML API

31.
Automated ML Customer Testimonials
• Press-coverage from
public preview:
• CNET
• VentureBeat
• PRNewswire
“I quite like your AutoML function. It gives me good results compared to
other libraries I tested before (tpot and auto-sklearn) that I believe was only
looking at scores and often gave me models that over-trained my data. And
of course the model from your suggested code is better.”
- Big oil company
“I will start with AutoML and use the algorithm that AutoML recommends to
further tune the model”
- Data Scientist
“I actually enjoy being able to use AutoML in a Jupyter notebook. The
DataRobot interface was nice for non-experts, but for someone like me, it
felt a bit basic.”
- Data Scientist

32.
https://dotnet.microsoft.com/apps/data/spark

33.
https://dotnet.microsoft.com/learn/dotnet/architecture-guides

34.
Gitter
https://gitter.im/dotnet/mlnet

35.
http://dot.net/ml
http://aka.ms/mlnetsamples
http://aka.ms/mlnetdocs
http://aka.ms/mlnet