create machine learning (ml) models in minutes using automl
TRANSCRIPT
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Ruchita Arora, Principal Product Manager, Amazon SageMaker
May 27, 2021
Create Machine Learning (ML) Models in Minutes Using AutoMLAmazon SageMaker Autopilot
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Agenda
What is AutoML?Amazon SageMaker AutopilotDeep diveDemoCustomer Success
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What is AutoML?
Removes the tedious, iterative, and time-consuming work across the ML workflow From data acquisition to model operationalization Preparing data, engineering features, training and tuning models, deploying models, and ongoing model monitoring and updating
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What is AutoML?
You spend less time on low level details and more time on using ML to improve business outcomes.
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Helping accelerate Machine Learning
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AWS AutoML offerings
AutoML is embedded into virtually all AWS ML services, simplifying the integration of ML within your business
Simplified ML
Whether you’re a business user, data scientist, or developer, AWS has AutoMLsolutions for you
ML for all practitioners
AWS makes it easy to dive deep into the models generated by AutoML
Full transparency
You can apply ML at scale using the 70+ end-to-end solutions
End-to-end solutions
Learn more about AutoML from AWS: https://aws.amazon.com/machine-learning/automl/
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AWS AutoML offerings
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AWS AutoML offeringsAutoML with Amazon SageMaker
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Amazon SageMaker Studio
Collaboration at scale
Share notebooks without tracking
code dependencies
Easy experiment
managementOrganize, track, and compare thousands
of experiments
Automatic model
generationGet accurate models with full visibility and
control without writing code
Higher quality ML models
Automatically debug errors, monitor models,
and maintain high quality
Increased productivityCode, build, train,
deploy, and monitor in a unified
visual interface
Fully Integrated Development Environment (IDE) for machine learning
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Amazon SageMaker Autopilot
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Amazon SageMaker Autopilot
Amazon SageMaker Autopilot automatically builds, trains, and tunes the best machine learning models
based on your data, while allowing you to maintain full control and visibility.
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Automatically create machine learning models with full visibility
Bring Data Run SageMakerAutopilot
Deploy Model Make Predictions
101011010010101010000011110
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Manually create machine learning models
Data Preparation and Feature Engineering
Model Tuning Model DeploymentModel Training
1 3 42
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Automate with Amazon SageMaker Autopilot
Data Preparation and Feature Engineering
Model Tuning Model DeploymentModel Training
1 3 42
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What kind of problems can Amazon SageMaker Autopilot handle?• Works with tabular
datasets• Regression: Predict a
continuous value for a new input
• Classification: Place an observation into one of two (binary) or one of multiple (multi) classes or kinds
• Use cases such as:o Customer churn
predictiono Customer lifetime valueo Price predictiono Rick assessmento Sale predictiono Loan risk classification
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How Amazon SageMaker Autopilot works
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Data Exploration and Preprocessing
Dataset Exploration Notebook:• Dataset statistics: row-
wise and column-wise• Suggested remedies for
common data set problems
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Model Training results
Explainability with Feature importance:• Understand and explain
how models created with SageMaker Autopilot make predictions
• Identify how each attribute in your training data contributes to the predicted result as a percentage
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Using Amazon SageMaker Autopilot for Model Candidates
Fully runnable model candidate notebook:• Data transformers• Featurization techniques
applied• Override points:
• Algorithms considered• Evaluation metric• Hyper-parameter ranges• Model search strategy• Instances used
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Demo
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Automatic model creation for tabular data with full visibility & control
Provide your data in a tabular form &
specify target prediction
Generate ML models with feature
engineering & model tuning automatically
done
Get notebooks for your models with
source code
Get a leaderboard & continue to improve your
model
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Complete integration with Amazon SageMakersecurity and compliance features
Use IAM Policies to control access to data, models, and
endpoints
CloudWatch and CloudTrail
integrationSupport
Support for PrivateLink VPC
endpoints, encryption at rest
and in-transit
SOC, PCI, FedRAMP, HIPAA, and more
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Customer Success – Freddy’s
Freddy’s grows revenue, sees double-digit comp sales growth using Domo, powered by Amazon SageMaker Autopilot, and optimizes pricing
and menu mix
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Customer Success – HawkEye 360
HawkEye 360 uses Amazon SageMaker Autopilot to rapidly generate high-quality AI models for maritime vessel risk assessment
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Skullcandy identifies key themes, products, and friction points impacting customer satisfaction scores using Sisense, powered by
Amazon SageMaker Autopilot
Customer Success – Skullcandy
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Learn MoreAmazon SageMaker Autopilot –https://aws.amazon.com/sagemaker/autopilot/
User Guide and APIhttps://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.htmlhttps://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-reference.html
AWS AutoML Solutions -https://aws.amazon.com/machine-learning/automl/
AWS Machine Learning Summit -https://aws.amazon.com/events/summits/machine-learning/
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Thank you!