ABOUT MYSELF

+17 YEARS IN TELCO AND IT EXPERIENCE

ABOUT MY START-UP

CS-4V: CLOUD SERVICES FOR VERTICALS

• TRAININGS – AWS SERVICES

• AI/ML

• CLOUD ADOPTION

• CONSULTANCY

• SETUP OF POCS USING AWS SERVICES

Fabian Zhindon Astudillo

AWS ML STACKOVERVIEW

FABIAN ZHINDON ASTUDILLO

CS-4V

DORTMUND AWS MEETUP

06-AUGUST-2019

CONTENT

• AI / ML FUNDAMENTALS

• AWS ML PROCESS & AWS ML STACK

• DEMOS AWS AI SERVICES AND SAGEMAKER NOTEBOOKS

Fabian Zhindon Astudillo

Artificial

Intelligence

Machine

Learning

Deep

Learning

Computing

Data

AlgorithmsUnsupervised

Learning

Supervised

Learning

Re-

inforcement

Learning

AI & ML & DL Propellers of

AI & ML & DL

Categories of

Machine Learning

AI-ML FUNDAMENTALS

• LACK OF EXPERTISE

• SPEED OF IMPLEMENTATION

• DATA READINESS

• MANAGING INFRASTRUCTURE

• CULTURAL ACCEPTANCE

• MANAGING COSTS

• SCALING PRODUCTION WORKLOADS

• ENSURING SECURITY AND COMPLIANCE

ENTERPRISE - ML ADOPTION CHALLENGES

Sources: Presentations @ re:Mars2019

AWS-ML CUSTOMERS (SUBSET)

ML 2019.02 Ref. Fabian Zhindon Astudillo

AWS ML PROCESS MAIN SOURCE AWS RE:INVENT 2018 (SRV420-R1)

Business Problem

ML problem framing Data Collection

Data Integration

Data Preparation & Cleaning

Data Visualization & Analysis

Feature Engineering

Model Training &

Parammeter Tunning

Model Evaluation

Yes

Model Deployment

> Predictions

Monitoring &

Debugging

Re-training

Business Goals met?

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Fabian Zhindon Astudillo

Business Analyst & Data Scientist

Build the Data Platform

• Amazon S3

• AWS Glue

• Amazon Athena

• Amazon EMR, Spark, (Scikit)

• Amazon Redshift Spectrum

• Amazon SageMaker Ground Truth

Data Architect

• Setup and manage Notebook

environment

• Setup and manage Training Clusters

• Write Data Connectors

• Scale ML algorithms to large datasets

• Distribute ML training algorithm to

multiple machines

• Secure Model artifacts

• (Elastic Inference to a Sgm Notebook)

• Setup and manage Model Inference

Clusters

• Manage and Scale Model Inference

APIs

• Monitor and Debug Model Predictions

• Models versioning and performance

tracking

• Automate new model version

promotion to prod (A/B testing)

Create an

endpoint

Batch Processing

Inference

Pipeline

AWS: ELB/AS

EI; NEO

Docker

Containers, EC2

GPUs, Inferentia

Horovod

AMAZON ML STACK: BROADEST & DEEPEST SET OF CAPABILITIES

AI

Services

ML

Services

ML Frameworks

& Infrastructure

Source: AWS InnovateOnline Conference AI -EDITION 05.03.2019 Fabian Zhindon Astudillo

• AI SERVICES

• E.G. AMAZON VIDEO REKOGNITION

• MEDICAL COMPREHEND...

• SAGEMAKER VALUE

• JUPYTER NOTEBOOK...

DEMOS

AWS AI SERVICES: AMAZON VIDEO REKOGNITION

Output from Amazon Video Rekognition – given a short video input

CATEGORIES- AND AWS BUILT IN ML-ALGORITHMS

• BLAZINGTEXT ALGORITHM

• DEEPAR FORECASTING ALGORITHM

• FACTORIZATION MACHINES ALGORITHM

• IMAGE CLASSIFICATION ALGORITHM

• IP INSIGHTS ALGORITHM

• K-MEANS ALGORITHM

• K-NEAREST NEIGHBORS (K-NN) ALGORITHM

• LATENT DIRICHLET ALLOCATION (LDA) ALGORITHM

• LINEAR LEARNER ALGORITHM

• NEURAL TOPIC MODEL (NTM) ALGORITHM

• OBJECT2VEC ALGORITHM

• OBJECT DETECTION ALGORITHM

• PRINCIPAL COMPONENT ANALYSIS (PCA) ALGORITHM

• RANDOM CUT FOREST (RCF) ALGORITHM

• SEMANTIC SEGMENTATION ALGORITHM

• SEQUENCE-TO-SEQUENCE ALGORITHM

• XGBOOST ALGORITHM

• DOCUMENT CONVENTIONS

Supervised

Un-Supervised

Reinforcement

Learning

Source: AWS 05.03.2019https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

Use Cases

Examples

Fabian Zhindon Astudillo

TAKE AWAYS

• A BUSINESS PROBLEM WHEN FRAMED AS ML PROBLEM CAN BE ADDRESSED BY USING AWS

SERVICES THAT COMPOSE THE AWS ML STACK

• AWS ML STACK SERVICES CAN BE INTEGRATED WITH THE AWS SERVICES PORTFOLIO &

AWS MARKET PLACE AND / OR WITH ON-PREM SERVICES

Fabian Zhindon Astudillo

VIELEN DANK