This course provides advanced-level training on Machine Learning applications developed with Python.
This instructor-led course introduces you to concepts and best practices for developing applications with Google Cloud Platform.
Through a combination of presentations, demos and hands-on labs, participants learn how to develop cloud-based applications using Google App Engine Standard Environment, Google Cloud Datastore, Google Cloud Endpoints, and Google Cloud Repositories.
This course is intended for experienced application developers who want to learn how to migrate applications to the cloud or create native, cloud-based applications on Google Cloud App Engine.
•Benefits of Google Cloud Platform.
•Development tools and services for Google Cloud Platform.
•Google Cloud Platform solution architectures.
•Lab: Google Cloud Source Repositories.
•Cloud Endpoints features.
•Developing APIs using Cloud Endpoints.
•Modular application design and App Engine services.
•Accessing App Engine services.
•Lab: Google App Engine Services.
•Authentication and authorization concepts.
•Securing access through application configuration.
•Authentication with the Users service.
•Authorization with API keys, OAuth, and application default credentials.
•Lab: User Authentication.
•Deploying and managing multiple application versions.
•Traffic splitting, incremental rollouts, and canary releases.
•Budgets and quotas.
•Stackdriver logging and application tracing.
•Lab: Managing Google App Engine Applications.
•Functionality and benefits of Cloud Platform storage options.
•Using Google Cloud Storage for immutable BLOB storage.
•Integrating Google Cloud SQL into App Engine Apps.
•Cloud Datastore fundamentals.
•Lab: Google Cloud Datastore.
•Implementing query filters with Cloud Datastore.
•Single-property and composite indexes.
•Configuring and optimizing indexes.
•Lab: Google Cloud Datastore Queries and Indexes.
•Strong and eventual consistency in Cloud Datastore.
•Ensuring strongly consistent queries.
•Best practices for Cloud Datastore transactions.
•Lab: Google Cloud Datastore Transactions.
•Memcache use cases and implementation patterns.
•Manual, basic, and automatic scaling behavior.
•Configuring application scaling.
•Lab: Google App Engine Performance and Optimization.
•Push and pull queue capabilities and configuration.
•Adding and consuming tasks with push and pull queues.
•Scheduling tasks with the Cron Service.
•Lab: Task Queue API.
•Lab: Deleting Google Cloud Platform Projects and Resources.
• Build scalable and reliable applications using Google App Engine Standard Environment.
• Leverage Google Cloud Endpoints to implement, deploy, and manage API backends.
• Create microservice-based applications using App Engine services.
• Manage application security, versioning, deployment, and monitoring.
• Store application data, optimize query performance, and use transactions in Google Cloud Datastore.
• Provide improved performance and capacity with Memcache and instance scaling.
See why people choose JBI
23/01/2018: Unifying software development and operations has been identified as the most effective way to deliver true digital transformation. DevOps is...
22/01/2018: The adoption of DevOps principles to achieve digital transformation calls for applications that can be developed quickly and efficiently, and...
19/10/2017: Nowadays, there is a significant business advantage in being able analyse, process and visualize "big data". While there is no agreed definition...
19/10/2017: A leading Financial Services organisation was keen to get several of its teams working consistently in an "Agile" way. Agile is an iterative...
11/10/2017: The training was to target a team of developers, with a mix of Java, C# and some C++. The team made heavy use of Java SE with some elements of...
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