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ARGOMENTO
Global AI Night 2019
Intermediate Track - Automated machine learning
Automated Machine Learning
@marco_zamana
MarcoZama
marco-zamana
Marco Zamana
Automated Machine Learning
Data scientist and developers
"magic" machine learning solution
Automated Machine Learning
Machine learning lifecycle
1. Business Understanding
2. Data Acquisition
3. Modeling
4. Operationalization
Every Machine Learning solution should start with the
business problem you are working to solve followed by acquiring and exploring the data
that is needed.
Automated Machine Learning
Source: scikit-learn machine learning library
Decisions
• What ml algorithm would be
best?
• What parameter values should
they use for the chosen
classifier?
And many more…
machine learning pipeline
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
Automated Machine Learning
Model Creation Is Typically Time-Consuming
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
Automated Machine Learning
Model Creation Is Typically Time-Consuming
Which algorithm? Which parameters?Which features?
Iterate
Automated Machine Learning
Model Creation Is Typically Time-Consuming
Automated Machine Learning
The combination of data pre-processing steps, learning algorithms, and hyperparameter settings
that go into each machine learning solution.
Machine learning pipeline
ACCURACY
Automated Machine Learning
Simplifying machine learning
What if a developer or data scientist could access an
automated service that identifies the best machine
learning pipelines for their labelled data?
Automated Machine Learning
Automated ML empowers customers,
with or without data science expertise,
to identify an end-to-end machine
learning pipeline for any problem,
achieving higher accuracy while
spending far less of their time.
And it enables a significantly larger
number of experiments to be run,
resulting in faster iteration towards
production-ready intelligent
experiences.
Enter data
Define goals
Apply constraints
OutputInput Intelligently test multiple models in parallel
Optimized model
Automated Machine Learning
Automated ML Accelerates Model Development
Automated Machine Learning
Automated ML is based on a breakthrough from our Microsoft Research division.
The approach combines ideas from
collaborative filtering and Bayesian
optimization to search
an enormous space of possible
machine learning pipelines intelligently
and efficiently.
It's essentially a recommender system for machine learning pipelines.
Similar to how streaming services recommend movies for users…
Automated Machine Learning
… Automated ML recommends machine learning pipelines for data sets.
Automated Machine Learning
No need to “see” the data
Automated ML accomplishes all this without
having to see the customer’s data, preserving
privacy.
Automated ML is designed to not look at the
customer’s data.
Customer data and execution of the machine
learning pipeline both live in the customer’s
cloud subscription
(or their local machine), which they have
complete control of.
Automated Machine Learning
No need to “see” the data
We trained automated ML’s
probabilistic model by running
hundreds of millions of
experiments, each involving
evaluation of a pipeline on a data
set.
This training now allows the
automated ML service to find good
solutions quickly for your new
problems.
And the model continues to learn
and improve as it runs on new ML
problems – even though, as
mentioned above, it does not see
your data.
Automated Machine Learning
Automated ML is available to try in the preview of Azure Machine Learning.
Currently support classification and regression ML model recommendation on numeric and text data,
with support for automatic feature generation (including missing values imputations, encoding,
normalizations and heuristics-based features), feature transformations and selection.
Data scientists can use automated ML through the Azure Machine Learning Python SDK and Jupyter notebook experience.
Training can be performed on a local machine or by leveraging the scale and performance of Azure by running it
on Azure Machine Learning managed compute.
Customers have the flexibility to pick a pipeline from automated ML and customize it before deployment.
Model explainability, ensemble models, full support for Azure Databricks and improvements to automated feature
engineering will be coming soon.
And NOW?
WorkShop
Automated Machine Learning
Energy Demand
Scenario
This scenario focuses on energy demand forecasting where
the goal is to predict the future load on an energy grid.
It is a critical business operation for companies in the
energy sector as operators need to maintain the fine
balance between the energy consumed on a grid and the
energy supplied to it.
@cloudgen_verona
#GlobalAINight
Grazie
Domande?
MarcoZama @marco_zamana marco-zamana

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Automated machine learning - Global AI night 2019

  • 1. 1 ARGOMENTO Global AI Night 2019 Intermediate Track - Automated machine learning
  • 3. Automated Machine Learning Data scientist and developers "magic" machine learning solution
  • 4. Automated Machine Learning Machine learning lifecycle 1. Business Understanding 2. Data Acquisition 3. Modeling 4. Operationalization Every Machine Learning solution should start with the business problem you are working to solve followed by acquiring and exploring the data that is needed.
  • 5. Automated Machine Learning Source: scikit-learn machine learning library Decisions • What ml algorithm would be best? • What parameter values should they use for the chosen classifier? And many more… machine learning pipeline
  • 6. 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 Automated Machine Learning Model Creation Is Typically Time-Consuming
  • 7. 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 Automated Machine Learning Model Creation Is Typically Time-Consuming
  • 8. Which algorithm? Which parameters?Which features? Iterate Automated Machine Learning Model Creation Is Typically Time-Consuming
  • 9. Automated Machine Learning The combination of data pre-processing steps, learning algorithms, and hyperparameter settings that go into each machine learning solution. Machine learning pipeline ACCURACY
  • 10. Automated Machine Learning Simplifying machine learning What if a developer or data scientist could access an automated service that identifies the best machine learning pipelines for their labelled data?
  • 11. Automated Machine Learning Automated ML empowers customers, with or without data science expertise, to identify an end-to-end machine learning pipeline for any problem, achieving higher accuracy while spending far less of their time. And it enables a significantly larger number of experiments to be run, resulting in faster iteration towards production-ready intelligent experiences.
  • 12. Enter data Define goals Apply constraints OutputInput Intelligently test multiple models in parallel Optimized model Automated Machine Learning Automated ML Accelerates Model Development
  • 13. Automated Machine Learning Automated ML is based on a breakthrough from our Microsoft Research division. The approach combines ideas from collaborative filtering and Bayesian optimization to search an enormous space of possible machine learning pipelines intelligently and efficiently. It's essentially a recommender system for machine learning pipelines. Similar to how streaming services recommend movies for users…
  • 14. Automated Machine Learning … Automated ML recommends machine learning pipelines for data sets.
  • 15. Automated Machine Learning No need to “see” the data Automated ML accomplishes all this without having to see the customer’s data, preserving privacy. Automated ML is designed to not look at the customer’s data. Customer data and execution of the machine learning pipeline both live in the customer’s cloud subscription (or their local machine), which they have complete control of.
  • 16. Automated Machine Learning No need to “see” the data We trained automated ML’s probabilistic model by running hundreds of millions of experiments, each involving evaluation of a pipeline on a data set. This training now allows the automated ML service to find good solutions quickly for your new problems. And the model continues to learn and improve as it runs on new ML problems – even though, as mentioned above, it does not see your data.
  • 17. Automated Machine Learning Automated ML is available to try in the preview of Azure Machine Learning. Currently support classification and regression ML model recommendation on numeric and text data, with support for automatic feature generation (including missing values imputations, encoding, normalizations and heuristics-based features), feature transformations and selection. Data scientists can use automated ML through the Azure Machine Learning Python SDK and Jupyter notebook experience. Training can be performed on a local machine or by leveraging the scale and performance of Azure by running it on Azure Machine Learning managed compute. Customers have the flexibility to pick a pipeline from automated ML and customize it before deployment. Model explainability, ensemble models, full support for Azure Databricks and improvements to automated feature engineering will be coming soon. And NOW?
  • 19. Automated Machine Learning Energy Demand Scenario This scenario focuses on energy demand forecasting where the goal is to predict the future load on an energy grid. It is a critical business operation for companies in the energy sector as operators need to maintain the fine balance between the energy consumed on a grid and the energy supplied to it.