Overview of AWS Sagemaker

1. Introduction to
AWS SageMaker

2. Introduction
Applied
Machine Learning
Data Engineering Training & EducationConversational Interfaces

3. Machine Learning Overview

6. Machine Learning Workflow

7. What is SageMaker?
● Machine Learning as a Service
● A cloud-based training and deployment framework
● Hosted notebooks for interactive development
● A set of optimised algorithms and open-source containers
Notebook Jobs Models Endpoint

8. Jupyter Notebooks
● Open source web app
● Create and share documents
● live code, visualisations,
documentation
● Data exploration, cleaning &
transformation
● Statistical modelling
● Numerical simulation
● … and machine learning

9. Flexible Compute

10. Flexible Storage
● Share workspaces with teams
● Persist data between sessions/instances
● Availability & reliability
● Dynamically scalable storage
● Large file support
● Read-after-write consistency
● Low-ish latency
● High throughput

11. Big Data

12. Clean, Fetch, Prepare Big Data (>100GB)
● Kinesis Firehose & Data Pipeline - ETL
● Glue - Catalogue, Crawl S3, ETL
● EMR/Hadoop/YARN - Distributed Compute and Storage
● Spark - Data Science Friendly Distributed Compute
● Athena / Redshift Spectrum - Use SQL with S3 data

13. Training in SageMaker
● Scalable Algorithms
● Streaming Data
● Incremental Training
● Containerized
● Accelerated Computing

14. Out of the Box Algorithms
Supervised Learning
LinearLearner
XGBoost
FactorisationMachine
Forecasting
Deep AR
Text Mining
BlazingText (word2vec)
Neural Topic Modelling
Latent Dirichlet Allocation
seq2seq
Computer Vision
ImageClassification
Anomaly Detection
Random Cut Forest
Unsupervised Learning
K Means
PCA

15. Exercising SageMaker

16. The Problem

17. NYC Taxicab Dataset
● 1.3 Billion Taxi Trips
● 2009 Onwards
● 330 GB
● Pickup longitude and latitude provided
● AWS Public Dataset (Sits in S3)

18. Demo: Clustering

21. Notebooks vs Distributed Training
scikit-learn
● 1 month of data (2GB)
● 13 million rows
● 7 minutes to train (50c)
● 1 x m4.16xlarge
● sklearn.cluster.KMeans
Sagemaker
● 1 year of data (24GB)
● 172 million rows
● 10 minutes to train ($3)
● 4 x m4.16xlarge
● sagemaker.KMeans

22. Out-of-the-Box vs Custom Algorithms
Out-of-the-Box (SageMaker k-means)
● Pay only for hours used
● Data lives in S3
● Data Scientist provisions
compute
● Easy to host models
● Limited Model Options
Custom (eg. scikit-learn)
● Use any model you like
● Distributed Training is
Hard
● Convert models
● Build Containers

23. TensorFlow

24. Image Classification

25. CIFAR-10
● 60000 32x32 colour
images
● 10 classes
● 6000 images per class
● 50000 training images
● 10000 test images

26. Demo: Train Locally

27. Local Limitations
● Compute
● Storage
● Memory
● Scaling
● Reliability
● Availability

28. More Data,
More Iterations,
More Compute

29. Demo: Train Remotely

30. Performance vs Cost

31. Time vs Cost for 1m Iterations
$2228
>1000 hours!

32. Steps per $

33. Review

34. 6 Months of SageMaker
● Very large datasets
● Incremental training
● Scaling “Time to Solution”
● Simple deployment of models
● Easy hosted notebooks
● No auto hyperparameter
tuning
● Local mode needs
improvement
● Evolving TensorBoard support
Notebook Jobs Models Endpoint

35. Questions?

36. Thanks

37. Pricing

38. Alternatives
Google CloudML
● Integrated into GCP
● Efficient hyperparameter
search
● Mature (as much as things
are in ML)
KubeFlow
● Defines TfJob in K8s
● Allows easy creation of a TF
cluster
● Immature (but promising)
● Cloud Agnostic