Google LLC made even more product announcements on day two at its next conference in San Francisco than on the opening day of the event – this time it was mainly on software developers.
Google is now very clear to the core Business customers who flock to larger numbers for their main public slides: Amazon Web Services Inc. and Microsoft Azure. All of the business-focused announcements that it made on day one, had their clear subsequent day two announcements:
- The new Anthos multicloud integration fabric announced today will be enriched and expanded with the new networking features designed to help Google Cloud Platform customers to build, scale and secure increasingly complex masks. They are also protected through the new identity and access management, security and trust, and mobile multi-factor authentication infrastructures that the company has rolled out.
- The new large data-managed open source services launched on day one will take place in the Google Cloud Platform along with the new, fully managed business database subscription services and complemented by the new archive store.
- The new vertical apps in the Google Cloud Platform rolled out today will have many companies near retail and AI infused documents, inventory and customer service applications ̵
But the most important theme of Cloud Next 2019 so far has been Google's clear game for hearts and minds for next-generation developers. On the first day, the top announcement in Google's Cloud Run, showing that Google has made serverless app development a top priority in following these key users.
In the many ads on day two, Google sent a clear signal that it is targeting a new breed of developers who rely on low-code, automated, built-in tools for continuous integration and continuous deployment of cloud-based application code and AI models . Here is the main day two announcements in this regard:
Automation of low-code development of cloud-native apps
For developers of multicloud apps, Google's boss code was Cloud Code, which is a set of plug-ins that support low-code development of public cloud distribution and local platform apps.
This new low-code tool – which is different from the existing Google AppMaker tool for building G Suite productivity programs – has the following features:
- Works with Visual Studio Code (in beta) and IntelliJ (in alpha);
- Helps Kubernete's developers get started by offering an updated set of code templates preconfigured for debugging, construction, and deployment.
- Automates many steps in the process of developing, debugging, compiling and manufacturing code for containerized Kubernetes applications;
- Extends the local code edit compiler debug loop to target all external Kubernet environs, including the Google Kubernetes Engine or GKE;
- Publishes Google Command Line Container Tool Shoffold, Jib and Kubectl to provide programmers with continuous feedback on projects as they are built.
- Allows developers to set up profiles that define different distribution targets, including local development, shared development, testing, or production;
- Offers the flexibility to test and debug code on the developer workstation or in the cloud;
- Includes a built-in library manager that adds the necessary application dependencies, automatically makes the Google APIs on developed apps and manages any necessary app secrets; and
- Integrate with existing DevOps tools and services such as Cloud Build and Stack Driver so that all app configurations are managed as source code in a repo. App log files for any environment can be viewed directly from an IDE containing these plug-ins.
Democratization of the development of sophisticated AI applications by business analysts
Google announced tools that simplify the creation of Cloud-native AI and other advanced analytics apps by business analysts, professionals, and other untraditional developers. These new tools, most of which are in beta or alpha, help you automate and guide new developers in the following analysis pipeline tasks:
- Data Recovery and Management : Computer Directory Helps Organizations to Quickly Detect , manage, manage and understand their data assets. It is a fully managed cataloging system for maintaining technical and business metadata. It provides a search-driven data fund interface. For security and data management, it integrates with Google Cloud Data Loss Prevention to automatically detect, catalog, and edit sensitive data costs, as well as with Google Cloud Identity and Access Management to enforce data access permissions.
- Data Movement : BigQuery Data Transfer Service automates data movement from SaaS applications to Google BigQuery on a scheduled, managed basis. It allows business analysts to fill a data store for downstream AI and analyzes without writing a single line of code. In addition to Google's first-party apps, it supports moving data from more than 100 popular software programs, including Salesforce, Marketo, and Workday.
- Data Entry and Integration : Cloud Data Fusion is a fully managed, cloud-based, no-code data integration service. It allows anyone to easily ingest and integrate data from different sources and transform the data, and mix or join other data sources before using Google's BigQuery to analyze it. It accelerates these integrations with a broad library of open source transformation and more than 100 out-of-the-box connectors for a wide range of systems and data formats. It allows users to explore and manage all data sets and data pipelines in a console, enabling the creation and management of data pipelines, although visual drag and drop without coding.
- Predictive Insight Generation : BigQuery ML, announced last year, enables analysts to use known SQL to build and distribute AI models on massive datasets directly in BigQuery. This week, Google announced that it has upgraded the product with more functionality to meet further business needs. Under the covers, it has added k-meaning clustering and matrix factorization to build customer segmentations and product recommendations. Users can now build and import TensorFlow deep neural network models through BigQuery ML. They can also use machine learning for table data without writing a single line of code, using a new feature called AutoML tables.
- Creation of data pipeline : Cloud Dataflow SQL enables analysts to build their own dataflow pipelines rather than rely on data analysis to handle this task. This tool uses known SQL which also automatically detects the need for batch or stream computing. It uses the same SQL dialect used in BigQuery. This enables analysts to use Dataflow SQL from the BigQuery user interface, and then join Cloud Pub / Sub streams with files or tables across the data infrastructure, and to query the merged data directly for real-time visualization and insight.
Fostering AI pipeline team productivity with robust team workbench
This week, Google's main AI developer message has been the beta release of the AI platform.
AI Platform is an IDE for modeling, training and serving containerized AI applications targeted to Google Cloud, other clouds, or local platforms. It provides a comprehensive, end-to-end environment for teams preparing, building, running, and managing AI projects. It allows coders, computer lovers, and other specialists to collaborate, train models, and scaled AI pipeline loads from a common dashboard. It provides tools for developers to detect and build AI pipelines, laptops, and other project assets that can run unchanged on premises or in Google Cloud.
AI Platform utilizes Google's Cloud AutoML, Cloud Machine Learning Engine, Kubeflow and AI Hub to support automated DevOp's AI-powered apps workflow. Google announced that it is working with various partners – including Accenture, Atos, Cisco Systems Inc., Gigster, Intel Corp., Nvidia Corp., Pluto 7, SpringML, and UiPath Inc. – building Kubeflow pipelines that work with the AI Platform.  Google also announced new automation tools for creating AI models from datasets without coding required, for training and deploying AI computers to peripheral units and for AI-powered discovery and video content classification.
And it's a wrap for day two. For more information and insight on all this, check out interviews at TheCUBE, SiliconANGLE's mobile live streaming studio covering all three days of the show.
Photo: Robert Hof / SiliconANGLE
Since you are here …
… We are here … I will tell you about our mission and how to help us fulfill it. SiliconANGLE Media Inc.'s business model is based on the content of the content, not advertising. Unlike many online publications, we don't have a paywall or run banner advertising, because we want to keep our journalism open, without the impact or need to chase traffic. Journalism, Reporting and Comments on SiliconANGLE – along with live, unwritten video from Silicon Valley Studio and Globe-Trotting Video Team on theCUBE – take a lot of hard work, time and money. Keeping quality high requires the support of sponsors who are adapted to our vision of free journalism content.
If you like reporting, video interviews, and other ad-free content here, please take a moment to check out a selection of video content supported by our sponsors, tweet your support and continue to get back to SiliconANGLE .