CPB100: Google Cloud Platform Big Data & Machine Learning Fundamentals

CPB100: Google Cloud Platform Big Data & Machine Learning Fundamentals
Event on 2017-03-29 09:00:00
CPB100: Google Cloud Platform Big Data & Machine Learning Fundamentals Course Description This 8 hour instructor-led class introduces participants to the Big Data & Machine Learning capabilities of Google Cloud Platform. It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. (For a more general overview of Google Cloud Platform, see CP100A) Audience This class is intended for: Data analysts Data scientists Business analysts It is also suitable for IT decision makers evaluating Google Cloud Platform for use by data scientists. This class is for people who do the following with big data: Extracting, Loading, Transforming, cleaning, and validating data for use in analytics Designing pipelines and architectures for data processing Creating and maintaining machine learning and statistical models Querying datasets, visualizing query results and creating reports Prerequisites Before attending this course, participants should have roughly one (1) year of experience with one or more of the following: A common query language such as SQL Extract, transform, load activities Data modeling Machine learning and/or statistics Programming in Python Duration 1 day (8 hours) Delivery Method Instructor-led  Objectives At the end of this one-day course, participants will be able to: Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform Employ BigQuery and Cloud Datalab to carry out interactive data analysis Choose between Cloud SQL, BigTable and Datastore Train and use a neural network using TensorFlow Choose between different data processing products on the Google Cloud Platform Modules Module 1: Introduction In this module you will be introduced to Google Cloud Platform and the data handling aspects of the platform. What is the Google Cloud Platform? GCP Big Data Products Usage scenarios Lab: Sign up for Google Cloud Platform Module 2: Foundation of GCP (Compute and Storage)  In this module, we introduce the foundations of the Google Cloud Platform: compute and storage and introduce how they work to provide data ingest, storage, and federated analysis. CPUs on demand (Compute Engine) GCE: the value proposition Lab: Start GCE instance, ssh access A global filesystem (Cloud Storage) Google Storage, data centers, zones, regions GS and its role in data processing Lab: Set up a Ingest-Transform-Publish data processing pipeline CloudShell Module 3: Data Analytics on the Cloud In this module we introduce the common Big Data use cases that Google will manage for you. These are the things that are widely done in industry today and for which we provide easy migration to the cloud.  Stepping stones to the cloud Where GCP started Towards no-ops CloudSQL: your SQL database on the cloud A no-ops database Lab: importing data into CloudSQL and running queries on rentals data Dataproc Managed Hadoop + Pig + Spark on the cloud Lab: Machine Learning with SparkML Module 4: Scaling data analysis This module is about the more transformational technologies in Google Cloud platform that may not have immediate parallels to technologies that attendees are using (“what’s next”). Fast random access Datastore: Key-Entity BigTable: wide-column Datalab Why Datalab? (interactive, iterative) Demo: Sample notebook in datalab BigQuery Interactive queries on petabytes Lab: Build machine learning dataset Machine Learning with TensorFlow TensorFlow Lab: Train and use neural network Fully built models for common needs Vision API Translate API Lab: Translate Genomics API (optional) What is linkage disequilibrium? Finding LD using Dataflow and BigQuery Module 5: Data processing architectures  In this module we will introduce you to data processing architectures in Google Cloud Platform. Asynchronous processing with TaskQueues Message-oriented architectures with Pub/Sub Creating pipelines with Dataflow Module 6: Summary  Why GCP? Where to go from here Resources  

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