This document provides an overview of AWS Sagemaker Autopilot, which is an automated machine learning service. It begins with introductions to machine learning and automated machine learning (AutoML). Key benefits of AutoML are that it allows building ML models without extensive programming knowledge, saves time and resources, and provides agile problem-solving. The document then introduces AWS Sagemaker Autopilot and explains how it works, including analyzing data, feature engineering, and model tuning stages. It provides a hands-on demo overview and recommends learning resources. The presenter's background and contact details are also included.

1. Gentle Introduction to
AWS Sagemaker Autopilot
Vivek Raja P S

2. Source: Tribune India, May 31, 2020

3. A little about myself...
● From Tamil Nadu, India
● Final year CS Undergrad (2020)
● 5x Microsoft Certified and 3x Oracle Certified
● Microsoft Certified Data Scientist Associate, AI
Engineer Associate, Data Engineer Associate
● OCI Certified Solution Architect Professional and
Developer Associate
● 15x Hackathon Winner
● Active Speaker and Mentor (AI & Cloud) - 30+ sessions
● Published 3 research papers, 1 patent (in review)
● Loves to play guitar, Learning French, Binge watching
webseries

4. Agenda
● Introduction to Machine Learning
● What is AutoML (Automated Machine Learning) ?
● AutoML versus Conventional ML practices
● Intro to AWS Automated Machine Learning
● Hands-on demo
● Learning resources
● Conclusion

5. Introduction to
Machine Learning

6. What is Machine Learning?
Machine learning (ML) is the process of using
mathematical models of data to help a computer
learn without direct instruction.
It’s considered a subset of artificial intelligence
(AI). Machine learning uses algorithms to identify
patterns within data, and those patterns are then
used to create a data model that can make
predictions.

7. What is Machine Learning?
To be put into simple words,

8. Machine Learning Techniques
Supervised learning (Input - Target pairs)
Addressing datasets with labels or structure, data acts as a teacher and “trains” the machine,
increasing in its ability to make a prediction or decision.
Unsupervised learning (Input data only)
Addressing datasets without any labels or structure, finding patterns and relationships by grouping
data into clusters.
Reinforcement learning (Reward/Penalty based learning)
Replacing the human operator, an agent—a computer program acting on behalf of someone or
something—helps determine outcome based upon a feedback loop.

9. Benefits of Machine Learning
● Uncover insight
● Improve data integrity
● Enhance user experience
● Reduce risk
● Anticipate customer behavior
● Lower costs

10. Overview of Stages in Machine Learning
Data Collection &
Preprocessing
● Identify data
source
● Data collection
● Data
Transformation
● Anomaly
Detection
● Cleaning the
data
● Domain
understanding
Train the model
● Splitting the data
● Selecting the
model
● Training
● Hyper-parameter
tuning
Validate the model
● Validating on test
dataset
● Evaluating results
● Finalising the data
model
Interpret the results
● Prediction
● Model monitoring
● Visualizations

11. What is AutoML?
(Automated Machine Learning)

12. What is AutoML?
Automated machine learning, also referred to as automated ML or
AutoML, is the process of automating the time consuming, iterative tasks
of machine learning model development. It allows data scientists, analysts,
and developers to build ML models with high scale, efficiency, and
productivity all while sustaining model quality.

13. AutoML process

14. AutoML
Vs
Conventional ML Practices

15. Benefits of AutoML
● Implement ML solutions without extensive
programming knowledge
● Save time and resources
● Leverage data science best practices
● Provide agile problem-solving

16. When to use AutoML?
● A non-programmer or non-professional data
scientist wants to leverage the power of ML
● Handling too complex data
● Lack of data domain knowledge
● Quick Implementation
● Building complex model with huge number of
parameters to finetune

17. Introduction to
AWS Sagemaker Autopilot

18. How AWS Autopilot works?
During training, AWS Autopilot creates a number of pipelines in parallel
that try different algorithms and parameters for you.
The service iterates through ML algorithms paired with feature selections,
where each iteration produces a model with a training score.
The higher the score, the better the model is considered to "fit" your data.
It will stop once it hits the exit criteria defined in the experiment.

19. Set up AWS Console
And Sagemaker Studio
Set up Input
and Output
Data Source
Run Autopilot
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dolor lorem dapibus.
Review the
experiment
runs
Steps to design AWS Autopilot

20. Analyzing Data
The Analyzing Data stage identifies the problem type to be solved (linear
regression, binary classification, multiclass classification). Then, it comes up with
ten candidate pipelines. A pipeline combines a data preprocessing step (handling
missing values, engineering new features, etc.), and a model training step using
an ML algorithm matching the problem type. Once this step is complete, the job
moves on to feature engineering.

21. Feature Engineering
In the Feature Engineering stage, the experiment creates training and validation datasets
for each candidate pipeline, storing all artifacts in your S3 bucket. While in the Feature
Engineering stage, you can open and view two auto-generated notebooks:
● The data exploration notebook contains information and statistics on the dataset.
● The candidate generation notebook contains the definition of the ten pipelines. In
fact, this is a runnable notebook: you can reproduce exactly what the AutoPilot job
does, understand how the different models are built, and even keep tweaking them if
you want.
With these two notebooks, you can understand in detail how data is preprocessed, and how
models are built and optimized. This transparency is an important feature of Amazon
SageMaker Autopilot.

22. Model Tuning
In the Model Tuning stage, for each candidate pipeline and its preprocessed
dataset, SageMaker Autopilot launches an hyperparameter optimization job;
the associated training jobs explore a wide range of hyperparameter values, and
quickly converge to high performance models.
Once this stage is complete, the SageMaker Autopilot job is complete. You can
see and explore all jobs in SageMaker Studio.

23. Hands on Demo

24. Learning Resources
Basics of Machine Learning: https://medium.com/@vivekraja98/machine-
learning-for-beginners-187178d1326d
Automated Machine Learning:
https://aws.amazon.com/getting-started/hands-on/create-machine-learning-model-automatically-
sagemaker-autopilot/

25. Open for QnA

26. Let’s connect
Email ID: vivekraja98@gmail.com
Linkedin: @Vivek Raja P S GitHub: @Vivek0712 Twitter: @vivekraja007