Introduction to ML.NET

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Writing Machine Learning code is now possible with .NET native library ML.NET that has recently reached 1.0 milestole. Let's look what we can do with this lib, which scenarios can be handled.

Software

Artificial
Intelligence
• Conversational
Agents
• Text Analytics
Machine
Learning
• Regression
• Classification
• Anomaly Detection
Deep Learning
• Neural Networks
• Computer Vision

Why now and not before?
• Theory since ’50s/’60s/’70s
• Obstacles
• Strong math skills
• Continuous training
• Now
• computing power
• Commodity and Cloud
• Discipline (components, API, microservices)
• Everyone asks

Everyone is a Data Scientist
«The italian way »

Two roles
• Who create (and train) the model (the new «component» market)
• Who consume the model

Something really happened (a week ago)
• Example IoT scenario
• (another example of «why now»)
• Solution in Python
• Python has won
• .NET (Core) Crysis
• Spikes handled with thresholds (and hysteresys)
• Work on levels
• Failure on this solution
• Thresholds changes, slope not
• I said «why don’t you use scikit learn»?
• Answer: «Too difficult»

Difficulties of «modern» ML
• Derivatives/gradients
• Limits
• Series
• Distributions
“It has exquisite buttons …
with long sleeves …works for
casual as well as business
settings”
f(x) f(x)

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

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
1. Data

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

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

Regression
Infer future values (interpolation?) from the past
Classification
When the data are being used to predict a category
When there are only two choices, it's called binary
classification.
When there are more categories it is called multi-class
classification.
Anomaly Detection
identify data points that are simply unusual

Regression Trees
• A decision (or regression) tree is a binary tree-like flow chart, where at
each interior node one decides which of the two child nodes to continue
to based on one of the feature values from the input.

Which algorithm? Which parameters?Which features?
Iterate

Enter data
Define goals
Apply constraints
OutputInput Intelligently test multiple models in parallel
Optimized model

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

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Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

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