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What you will learn

  • This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform.
  • This class is intended for experienced developers who are responsible for managing big data transformations.
  • Extracting, Loading, Transforming, cleaning, and validating data Designing pipelines and architectures for data processing.
  • Creating and maintaining machine learning and statistical models Querying datasets, visualizing query results and creating reports

Skills you will gain

  • Shareable Certificate Earn a Cerfiticate upon completion
  • On Demand or Face to Face Start instantly and learn at your own schedule
  • Flexible Schedule Set and maintain flexible deadlines
  • Intermediate For those with working experience or likely to have completed foundation level training  
  • English Subtitles: English

This course consists of 17 lessons

Serverless Data Analysis with BigQuery: What is BigQuery - Advanced Capabilities - Performance and pricing

Serverless, Autoscaling Data Pipelines with Dataflow

Getting Started with Machine Learning: What is machine learning (ML)- Effective ML: concepts, types - Evaluating ML - ML datasets: generalization

Building ML Models with Tensorflow: Getting started with TensorFlow - TensorFlow graphs and loops + lab - Monitoring ML training

Scaling ML Models with CloudML: Why Cloud ML? - Packaging up a TensorFlow model - End-to-end training

Feature Engineering: Creating good features - Transforming inputs - Synthetic features - Preprocessing with Cloud ML

ML Architectures: Wide and deep - Image analysis - Embeddings and sequences - Recommendation systems

Google Cloud Dataproc Overview: Introducing Google Cloud Dataproc - Creating and managing clusters - Defining master and worker nodes - Leveraging custom machine types and preemptible worker nodes - Creating clusters with the Web Console - Scripting clusters with the CLI - Using the Dataproc REST API - Dataproc pricing - Scaling and deleting Clusters

Running Dataproc Jobs: Controlling application versions - Submitting jobs - Accessing HDFS and GCS - Hadoop - Spark and PySpark - Pig and Hive - Logging and monitoring jobs - Accessing onto master and worker nodes with SSH - Working with PySpark REPL (command-line interpreter)

Integrating Dataproc with Google Cloud Platform: Initialization actions - Programming Jupyter/Datalab notebooks - Accessing Google Cloud Storage - Leveraging relational data with Google Cloud SQL - Reading and writing streaming Data with Google BigTable - Querying Data from Google BigQuery - Making Google API Calls from notebooks

Making Sense of Unstructured Data with Google’s Machine Learning APIs: Google’s Machine Learning APIs - Common ML Use Cases - Vision API - Natural Language API - Translate - Speech API

Need for Real-Time Streaming Analytics: What is Streaming Analytics? - Use-cases - Batch vs. Streaming (Real-time) - Related terminologies - GCP products that help build for high availability, resiliency, high-throughput, real-timestreaming analytics (review of Pub/Sub and Dataflow)

Architecture of Streaming Pipelines: Streaming architectures and considerations - Choosing the right components - Windowing - Streaming aggregation - Events, triggers

Stream Data and Events into PubSub: Topics and Subscriptions - Publishing events into Pub/Sub - Subscribing options: Push vs Pull - Alerts

Build a Stream Processing Pipeline: Pipelines, PCollections and Transforms - Windows, Events, and Triggers - Aggregation statistics - Streaming analytics with BigQuery - Low-volume alerts

High Throughput and Low-Latency with Bigtable: Latency considerations - What is Bigtable - Designing row keys - Performance considerations

High Throughput and Low-Latency with Bigtable: What is Google Data Studio? - From data to decisions

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United Kingdom

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