High Throughput and Low-Latency with Bigtable
What is Cloud Spanner? Designing Bigtable schema Ingesting into Bigtable
Streaming Analytics and Dashboards
Streaming analytics: from data to decisions Querying streaming data with
Implementing Streaming Pipelines
Challenges in stream processing. Handle late data: watermarks, triggers, accumulation.
Ingesting Variable Volumes
What is Cloud Pub/Sub? How it works: Topics and Subscriptions
Architecture of Streaming Analytics Pipelines
Stream data processing: Challenges Handling variable data volumes Dealing with
Feature Engineering
Creating good features Transforming inputs Synthetic features Preprocessing with Cloud
Scaling ML Models with CloudML
Why Cloud ML? Packaging up a TensorFlow model End-to-end training
Building ML Models with Tensorflow
Getting started with TensorFlow TensorFlow graphs and loops + lab
Getting Started with Machine Learning
What is machine learning (ML) Effective ML: concepts, types ML
Serverless, Autoscaling Data Pipelines with Dataflow
The Beam programming model Data pipelines in Beam Python Data
Serverless Data Analysis with BigQuery
What is BigQuery Queries and Functions Loading data into BigQuery
Making Sense of Unstructured Data with Google’s Machine Learning APIs
Google’s Machine Learning APIs Common ML Use Cases Invoking ML
Integrating Dataproc with Google Cloud Platform
Customize cluster with initialization actions. BigQuery Support.
Running Dataproc Jobs
Running Pig and Hive jobs. Separation of storage and compute.
Google Cloud Dataproc Overview
Creating and managing clusters. Leveraging custom machine types and preemptible
