Automated machine learning aims to simplify and accelerate the machine learning process by automatically identifying the optimal machine learning pipelines for labelled datasets. It does this by intelligently testing multiple models in parallel and optimizing hyperparameters without needing to see the underlying data. This approach was developed by Microsoft Research and is now available through Azure Machine Learning, where it can recommend pipelines for classification and regression tasks on numeric and text data with automated feature engineering. It allows both experts and novices to benefit from machine learning without extensive data science expertise.
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…
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.
