The document discusses automated machine learning (Auto ML) which aims to automate the process of applying machine learning. It allows non-experts to develop machine learning models by automating tasks like selecting optimal algorithms and hyperparameters. Popular Auto ML frameworks include auto-sklearn, AutoKeras, Google Cloud Auto ML, and Microsoft AutoML which use techniques like Bayesian optimization and neural architecture search to automate model training and selection. The document demonstrates how Auto ML tools like H2O AutoML and ML.NET can simplify and speed up applying machine learning for both cloud-based and on-premise scenarios.

1. Automated Machine Learning,
or Machine Learning for Lazy
Dmitry Petukhov,
Machine Learning Consultant, Cloud Architect,
Microsoft Most Valuable Professional in AI && Coffee Addicted
#AI #Azure #ainights

2. Automated Machine Learning
 The operator decision – by hands
 Automated machine learning – the automation of automation
of automation.
 If… else – the automation
 Machine learning algorithms – the automation of automation

3. What is Automated Machine Learning?
The research area that targets the automation process of applying ML
 End-to-end process
 Focused on non-experts in ML
 New tools for ML experts
 Also known as Auto ML.

4. Auto ML: Motivation
Picture credit
Bad news 
 Too complex
 Too long.
Because life is hard!
Good news 
 Can be standardized.
Let’s automate it!

5. Auto ML: Frameworks
2 classes by purpose
 Optimal ML algorithm(s) and its hyperparameters search
 Optimal neural network architecture search.
Really?!
Frameworks
 Auto-WEKA (2013)
 auto-sklearn (2014)
 H2O AutoML (2014)
 AutoKeras (2017)
 Google Cloud AutoML (2017)
 Microsoft AutoML in Azure ML Services (2018)
 Microsoft ML.NET (2018)
 to be continued…

6. Source: https://arxiv.org/pdf/1908.05557.pdf

7. Auto ML: Frameworks. Part II
Framework
Open-
source
Cloud-
based
Supports
NN
architecture
search
Techniques
Training
framework
Version
auto-sklearn Y N Classification,
Regression
N Bayesian optimization + automated
ensemble construction
sklearn pre-release
AutoKeras Y N CNN, RNN,
LSTM for
classification
Y Efficient Neural Architecture Search with
Network Morphism
Keras pre-release
Google Cloud
AutoML
N Y CNN, RNN,
LSTM for
classification
Y Reinforcement learning with gradient policy
upgrade
TensorFlow beta
AutoML in Azure
ML Service
N Y Classification,
Regression
N Probabilistic Matrix Factorization + Bayesian
optimization
sklearn GA
H2O AutoML Y N Classification,
Regression
N H2O, XGBoost stable
ML.NET AutoML N N Classification,
Regression
N ML.NET preview

8. ML.NET AutoML Demo
ML.NET used internally by Microsoft for more than 8 years in:
 Bing Ads
 Power BI
 Azure ML Studio
 Windows Defender
 Office Excel
 Office PowerPoint
 and many internal corporate applications.
References:
1. ML.NET AutoML samples.

9. H2O AutoML Demo
References:
1. H2O tutorials.
Picture credit
On-prem or in the Azure, AWS, IBM’s clouds

10. The end is near
 Быстрая проверка гипотезы – да
 State-of-the-art – сложно
 В enterprise требует понимания на уровень абстракции ниже – без понимания это выстрел в себе ногу
 Перебор, композиция и оптимизация поиска, но не изобретение нового
 Часто сырое ПО Open source 

11. © 2019, Dmitry Petukhov. CC BY-SA 4.0 license. Microsoft and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
Thank you!

12. Q&A
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