This document discusses Google Cloud Platform tools for big data and machine learning. It provides an overview of BigQuery for cloud data warehousing and big data analytics. It also covers managed Hadoop and data processing tools like Cloud Composer, Dataproc, and Dataflow. Finally, it discusses Google's machine learning tools ranging from high-level APIs to Cloud ML Engine for building custom models and AutoML for automating the machine learning process.

1. BIG DATA AND MACHINE LEARNING
ON GOOGLE CLOUD PLATFORM
Wlodek Bielski | CEO Pure Company

2. AGENDA
 Google Cloud Platform at a glance
 BigQuery: Cloud DWH and Big Data
 Managed Hadoop, Beam, Airflow – overview of GCP PaaS
 Machine Learning: from ML API toTensorflow

3. PURE COMPANY
DATA
AICLOUD

4. GCP AT A GLANCE

5. GARTNER MQ FOR CLOUD IAAS, 2018
„Google has been most differentiated
on the forward edge of IT, with deep
investments in analytics and ML,
and many customers who choose Google
for strategic adoption have applications
that are anchored by BigQuery”

6. GOOGLE CLOUD PLATFORM

7. OPEN-SOURCE INNOVATIONS

8. BIGQUERY: NO-OPS CLOUD DWH
 Near real-time analysis of massive datasets
 Standard SQL syntax (ANSI SQL 2011)
 No-ops for performance and scaling (global black-box)
 Separated storage and compute, linked with petabit network
 Pay-as-you-go: only for queries and storage used
 Automatic discount for long-term storage

9. BIGQUERY ARCHITECTURE

10. BIGQUERY: DEMO

11. COMPLETE BI FLOW ON GCP

12. BIGQUERY ML

13. DATA PROCESSING ON GCP
Cloud Composer – managed AirFlow
Dataproc – Hadoop + Spark
DataFlow – Apache Beam
Matillion – ETL/ELT, mainly for BigQuery

14. DATAPROCVS DATAFLOW
Cloud Dataproc
 Migrating existing Hadoop workloads
 Iterative processing and Notebooks
 ML with Spark ML
Cloud Dataflow
 Better for greenfields
 Batch + streaming in one tool
 Based on Apache Beam
 Multiple runtimes, e.g. Spark, Flink
 Preprocessing for CloudML

15. SAMPLE MATILLION FLOW

16. GOOGLE ML OFFERING
ML APIs AutoML CloudML
Tensorflow
DIY
Data Science expertise required

17. VISION API
 Pretrained models via API
 Label detection
 Face detection
 NO Face recognition
 Logo detection
 REST API
 Cloud Storage integration

18. AUTOML
VISION
 Custom ML models
without coding / ML skills
 Human labeling available
(2-20 labels, up to 5 working
days)
 Powered by Google
research in AutoML
andTransfer Learning

19. CLOUD ML ENGINE
 Complete operationalization service in Cloud
 GCP console, command line gcloud ml-engine, REST API
 TensorFlow, scikit-learn and XGBoost support
 Both Python 2 and Python 3 are supported in v1.4+
 Training with GPUs andTPUs (beta)

20. TYPICAL ML PROCESS

21. EXAMPLE CLOUD ML FLOW

22. CLOUD COMPOSER (AIRFLOW) ORCHESTRATION

23. WRAP-UP
 BigQuery – Cloud DWH
 BigQuery ML – pure SQL, for devs / data analysts
 Set of APIs for developers (e.g.Vision API)
 AutoML for analysts
 CloudML – for data scientists

24. HTTPS://AI.GOOGLE/
wlodek@purecompany.pl
www.purecompany.pl