Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. In this workshop, you learn how to build an ML model using Amazon SageMaker's built-in algorithms and frameworks. We then train the model using automatic model tuning to quickly achieve a high level of accuracy. Finally, we deploy the model in production where you can start generating predictions to achieve the best results. When finished, you will understand how Amazon SageMaker removes the complexity and barriers that typically slow down developers using ML.

1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Machine learning for developers &
data scientists with Amazon SageMaker
Vikrant Kahlir
Enterprise solution architect
Amazon Web Services
A I M 3 0 2

2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Put machine learning in the
hands of every developer
Our mission at AWS
Our mission at AWS
Put machine learning in the
hands of every developer

3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
The Amazon ML stack:
Broadest and deepest set of capabilities
AI services
Easily add intelligence to applications without machine learning skills
Vision | Documents | Speech | Language | Chatbots | Forecasting | Recommendations
ML services
Build, train, and deploy machine learning models quickly and easily
Data labeling | Pre-built algorithms and notebooks | One-click training and deployment
ML frameworks &
infrastructure
Flexibility and choice, highest-performing infrastructure
Support for ML frameworks | Compute options purpose-built for ML

4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
1
2
3
Amazon SageMaker:
Build, train, and deploy ML models at scale
Pre-built notebooks
for common problems
Amazon
SageMaker Ground
Truth
Collect and prepare
training data
Choose and optimize
your ML algorithm
Built-in,
high-performance
algorithms
AWS Marketplace for
machine learning
One-click training on
the highest-performing
infrastructure
Set up and manage
environments for
training
Amazon EC2 P3
instances
Amazon
SageMaker RL
Train and
tune models
Model
optimization
Amazon
SageMaker Neo
Scale and manage
the production
environment
Fully managed with
auto-scaling for 75%
less
Amazon Elastic
Inference
One-click deployment
Deploy models in
production
Deploy everywhere
Flexibility and choice with modules for ML developers and data scientists

5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Problem statement
Health care insurance fraud is a pressing problem, causing substantial and
increasing costs in medical insurance programs.
Due to large amounts of claims submitted, review of individual claims becomes a
difficult task and encourages the employment of automated pre-payment controls
and better post-payment decision support tools to enable subject matter expert
analysis.
We will demonstrate the application of unsupervised anomalous outlier
techniques on a minimal set of metrics made available in the CMS Medicare
inpatient claims from 2008.

6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Dataset
CMS Medicare inpatient claims from 2008
• Medicare inpatient claims from 2008
• Each record is an inpatient claim incurred by a 5% sample of Medicare
beneficiaries
• Beneficiary identities are not provided
• ZIP Codes of facilities where patient was treated are not provided
• The file contains eight variables: one primary key and 7 analytic variables
• Data dictionary required to interpret codes in dataset are provided

7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Data variables

8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
1
2
3
End-to-end process for workshop
Apply Word2Vec for
feature extraction on
procedure and
diagnosis
Feature
engineering
Train to identify
principal components
in training dataset
Train with Amazon
PCA algorithm
Calculate anomaly
score for test claims
and analyze results
Inference and
analysis
Deploy trained model
Deploy model
Download workshop
resources
Launch Amazon
SageMaker Jupyter
Notebook
Workshop
preparation
Download data
Clean data
Preprocess data
Data
processing
Objective: Flag healthcare claims as a targeting method for further investigation

9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Techniques and algorithms used in workshop
• Outlier detection
• Word embedding and Word2Vec
• Principal component analysis

10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Outlier detection
“Observation which deviates so much from other observations as to arouse suspicion it was generated
by a different mechanism”
Douglas M. Hawkins
• Modeling normal objects and outlier effectively. The border between data normality and abnormality
(outliers) is often not clear-cut.
• Outlier detection methods could be application specific. For example, in clinical data small deviation could
be an outlier, but in a marketing application large deviation is required to justify an outlier.
• Noise in data may be present as deviations in attribute values or even as missing values. Noise may hide an
outlier or may flag deviation as an outlier.
• Providing justification for an outlier from understandability point of view may be difficult.
Challenges

11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Word embedding and Word2Vec
Word embedding
Word2Vec
• Text converted into numbers, and there may be different numerical
representations of the same text
• Many techniques exist; Continuous Bag of Words (CBOW) and skip-gram are
popular and effective for large corpus of documents
• Example: CBOW predicts the probability of a word given a context
• Shallow neural networks that map word(s) to the target variable, which is also a
word or words
• Learn weights, which act as word vector representations

12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Principal component analysis
When
• Do you want to reduce the number of variables, but aren’t able to identify variables to completely
remove from consideration?
• Do you want to ensure that your variables are independent of one another?
• Are you comfortable making your independent variables less interpretable?
How
• A measure of how each variable is associated with one another (covariance matrix)
• The directions in which our data are dispersed (eigenvectors)
• The relative importance of these different directions (eigenvalues)

13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Complete the workshop
http://bit.ly/2LX4sRf

14. Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Vikrant Kahlir
Contact information