20-Minutes to Kickstart your AWS AI & ML Knowledge
A presentation at Data Natives Unlimited 2020 by Bruno Amaro Almeida
20-Minutes to Kickstart your AWS AI & ML Knowledge Index Data Natives Unlimited 2020 Bruno Amaro Almeida BERLIN · HELSIN K I · LON DON @bruno_amaro · MUN ICH · OSLO · STOCK HOLM · TAMPERE Photo by Christian Englmeier on Unsplash
Hello! Thank you! What can you expect from this session? Kiitos! • Basic Data Science concepts Danke! • AI and Machine Learning landscape in AWS • Data Architecture fundamentals in AWS Tack! • What does it mean to build, train and deploy a ML Bruno Amaro Almeida HE AD OF ARC HITE C TU RE @ FU TU RIC E AWS AP N AMB ASSADOR Cloud, DevOps, Security, Data Engineering & AI model in AWS? Reach out on: @bruno_amaro @brunoamaroalmeida BERLIN · HELSIN K I · LON DON · MUN ICH · OSLO · STOCK HOLM · TAMPERE
W HAT IS W HAT? AI, ML, Data Science, … ? Artificial intelligence (AI): Not a single thing; it is a combination of technologies such as machine learning. Machine Learning (ML): toolbox of algorithms and techniques that learn from data by minimising a cost function. Artificial Intelligence Data Engineering Machine Learning Deep Learning Big Data Data Science: interdisciplinary field to extract knowledge from data. Data Science Data Engineering: software engineering practice of designing, building, integrating, and managing data. @bruno_amaro
AWS Data Engineering, AI and ML Data Preparation • • Visualization & Analytics AWS Glue AWS EMR • AWS QuickSight • AWS Athena AI / Machine Learning Cognitive Services AI/ML API’s • AWS SageMarker • AWS Mechanical Turk / Ground Truth • AWS Greengrass • • • • AWS Lex AWS Rekognition AWS Translate AWS Polly (TTS) • • • • AWS Transcribe (ASR) AWS Textract (OCR) AWS Comprehend (NLP) AWS Forecast (Time-series forecast) @bruno_amaro
Reference Architecture @bruno_amaro
AI & Analytics Cycle Business understanding Data Ingestion Deploy the Model BERLIN · HELSINKI · LONDON · MUNICH · OSLO · STOCKHOLM · TAMPERE Data Preparation Train and Validate the Model Visualization & Analytics
DATA PREPARATION Data Preparation: What, Why, How? A What is data preparation? Transforming our data to make it usable by Machine Learning and / or Analytics B • Over simplification: Algorithms <3 numbers • Algorithms need a lot of data and with good quality to be efficient. How? • Extract: read the data into a usable format (json, csv, parquet, etc) Process: Extract, Transformation, Load (ETL, ELT) • Ways to do Transformation: Categorical Encoding, Feature • Transformation: clean & transform features so that the data can be usable by algorithms or analysis Load: write the new data into a place that can be usable (data warehouse, object storage, etc) Why is this important? Garbage in < Garbage out > C Engineering, … @bruno_amaro
DATA PREPARATION Ways to transform data: Categorical A Categorical Encoding Encoding and Feature Engineering PhoneBrand Brand_Apple Brand_Samsung Apple 1 0 Samsung 0 1 One-hot encoding Text to Numbers: Nominal vs Ordinal B S M L Text Feature N-gram Term Frequency – Inverse Document Frequency (tf-id) Orthogonal Sparse Bigram (OSB) C N-Gram http://recognize-speech.com/language-model/n-gram-model/comparison Numeric Feature 0 1.0 avg Scaling (e.g. Normalization, Standardization) Binning (e.g. Quantile) z-score To deal with outliers! Groups of similar values 0-17 D Missing data? Too many features? Missing data can be dealt in different ways: supervised learning, mean, median, drop values Feature selection: remove useless features! 18-64 65+ Principle Component Analysis (PCA) is an algorithm that reduces the amount of features @bruno_amaro
ALGORITHMS Algorithms & Validation: Building Intelligence on top of our data > Algorithm (consistent) vs Heuristic (rule of thumb, no consistent outcome) > Supervised Learning (training, labels) vs Unsupervised Learning (no training or supervision) How can you use algorithms in AWS? You can use the algorithms built-in Sagemaker B E R L I N · T A M P E R E H E L S I N K I · L O N D O N · M U N I C H · O S L O · You can purchase algorithms from Marketplace S T O C K H O L M · S T U T T G A R T · You can build your own algorithm (& place it in a Docker container) @bruno_amaro
ALGORITHMS (some) Types of Algorithms A B Regression Algorithms that estimate the relation between dependent values (e.g. Linear Regression, Logistics Regression, …) 0.30€ 09/2020 0.35€ 10/2020 0.33€ 11/2020 Classification Algorithms that aim to classify features (e.g. K-means, K-nearest) C Clustering Unsupervised algorithms that aim to group features (e.g. K-means) AWS Sagemaker Ground Truth (Mturk) would help here! source: brickarchitect.com D Reinforcement Learning Algorithms that find the path to the greatest reward (e.g. Markov Decision Process) > AWS DeepRacer! @bruno_amaro
Extended Reference Architecture How can you deploy models in AWS? Offline usage (e.g. ECR/Docker) Online usage (e.g. API) @bruno_amaro
Validation, Measure, Accuracy What is a good model? When is it “good enough” ? Goal of ML is to Generalize and not Memorize Training data (60-70%) Training Error Testing Error Low Low Low High High High 😀 😒 🥺 Testing data (20-30%) • Monitoring and Evaluate Model (e.g. CloudWatch) Offline validation (Sets, K-fold) Online validation (Canary, A/B testing) • Model accuracy and tuning Adjust hyperparameters, data or even change the algorithm. @bruno_amaro
Business understanding Thank you! Kiitos! Danke! Tack! Data Ingestion Data Preparation Deploy the Model Bruno Amaro Almeida HE AD OF ARC HITE C TU RE Train and Validate the Model Cloud, DevOps, Security, Data Engineering & AI Visualization & Analytics Reach out on: @bruno_amaro @brunoamaroalmeida BERLIN · HELSIN K I · LON DON · MUN ICH · OSLO · STOCK HOLM · TAMPERE
AI and Machine Learning are key areas of investment, growth and differentiation for many companies. In recent years cloud technologies and managed services made building solutions using AI/ML affordable and easy to experiment with, propelling its fast adoption and lowering the barrier to engineers and data enthusiasts.
In this session, tailored to engineers and technical folks without prior data science knowledge, we will learn the fundamentals about AI and Machine Learning in AWS. We will have an overview of algorithms and the concepts of data collection and preparation. We will explore different AWS managed services for data science with different levels of abstraction, such as AWS SageMaker - the AI/ML Platform - but also the pre-trained AI/ML model APIs with ready-made capabilities such as AWS Transcribe (Speech-to-Text), Rekognition ( Image & Video Classification) and Comprehend ( Sentiment Analysis ).
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