Google never disappoints when it comes to building up excitement, does it? With over 122+ announcements, the recently concluded Google Cloud Next ’19 conference in San Francisco was high-voltage, to put it mildly.
Over three days, Google served generous servings of exciting product and services news. Quite obviously, AI, Machine Learning, and Big Data were big on the menu. Clearly, the tech giant has its finger on the pulse of the market. It knows where enterprises need help and precisely designs solutions to assuage those.
So, what made news at the annual Google event? Let’s take a look.
The biggest announcement unarguably has to be that of Anthos. Anthos sounds like a character right out of an Avengers script. We have to agree and say that Anthos does promise some superhero traits.
Anthos is a Google Cloud Services Platform that allows enterprises to run applications in their private data center and Google’s Cloud. Anthos, available on the Google Kubernetes Engine (GKE), also extends support to AWS and Azure who are Google’s competitor cloud platforms.
With Anthos, Google gives enterprises the power to use a single platform, running on Google’s Cloud, to deploy and manage their applications on any cloud using a single platform, running on Google’s Cloud, to deploy and manage their applications on any cloud.
This helps enterprises immensely as you get a single bill and use a single dashboard to manage all your applications. Google takes a very differentiated approach here as the money in Cloud after all lies in charging fees for storage allocations, compute time, etc. But what Google does is solve a genuine enterprise problem by allowing users to run applications on other cloud platforms.
The Big Data Story
Data also gets to work hard for the enterprise with these new initiatives.
BigQuery Data Transfer Service: Google’s BigQuery changed data analytics forever by unlocking unparalleled price performance and productivity for the enterprise. But Google has taken this a notch above with BigQuery Data Transfer Service. This offering allows enterprises automate data movement from SaaS applications to Google BigQuery and allows them the flexibility to build a robust data warehouse without bothering to write code.
Data Fusion: Google introduces us to Data Fusion, a “Fully managed, code-free data integration at any scale.” Data fusion can be a real game changer in the cloud analytics world as it alleviates the struggle associated with integrating different data silos in a streamlined manner. It ensures code portability and smooth integration with your on-premises and the Cloud platform.
BigQuery BI: The BigQuery BI engine takes long loading times and makes that history. It caches results to enable you to analyze data stored in BigQuery with sub-second query response time and with high concurrency. Fancy filtering a dashboard? That’s no longer needed. Setting up the BigQuery BI engine is also a piece of cake – you’re good to go in a few clicks. While the BI engine is limited to 10Gb tables, it is soon expected to increase to 150Gb.
Connected Sheets: Data analysts also have reason to rejoice for Connected Sheets. With this, Google Sheets gets access to the power of BigQuery. There will be no row limits and a direct connection to datasets in BigQuery. Most essentially, you can just apply the functionality of the sheet. There’s no SQL needed.
Data Catalog: With the Data Catalog, Google gives you the advantage of “a fully managed and scalable metadata management service that empowers organizations to quickly discover, manage, and understand all their data in Google Cloud.” It has the required integrations in place such as Cloud DLP to help you discover and catalog sensitive data assets and Cloud IAM where you source access control lists (ACLs) to simplify access management without compromising on security.
AI for everyone
How can there not be mention of AI in a Google conference? Of course, there was the talk of AI and what a conversation it was!
Google Cloud AI Platform
Google unveiled the Google Cloud AI Platform (now available in beta) that offers AI developers a collaborative platform to test, train, and deploy machine learning and deep learning models. This platform enables collaboration by allowing developers, data science, and data engineering teams to create and deploy deep learning and machine learning models with ease.
Bob O’Donnell, president, and chief analyst at TECHnalysis Research says that with this unified offering, “further extending [its] AI capabilities and reaching out to companies that don’t have the in-house expertise” to build AI models of their own.
The AI platform also aims to democratize AI and Machine Learning by providing all the tools one might need to undertake the AI journey. Google’s AI initiatives take away the complexity of taking an AI project from idea to production by putting every tool and product in a single place. To ease the AI woes of enterprises, Google presents:
- Document Understanding AI: To classify documents, extract crucial information from scanned images, and apply industry-specific, custom analysis to automate processing needs.
- Contact Center AI (beta):To help enterprises improve customer experience and operational efficiency.
- Recommendations AI:To deliver highly personalized product recommendations to customers at scale.
- Visual Product Search: To match customer-generated images with images from your product catalog to reduce purchasing friction
ML for the Masses
Google Machine Learning initiatives aim to make ML more scalable and accelerate both research and industry applications. To that effect, they announced the AutoML Tables at the conference. This service allows users to automatically build and deploy machine learning models on structured, tabular data. The offering provides a scalable end-to-end AutoML solution that:
- Enables full automation
- Provides extensive coverage
- Generates high-quality ML models comparable to ones manually designed by ML experts.
The other benefits of AutoML Tables?
- You can find predictive insights and patterns from structured data.
- It has a codeless interface that guides the user through the full end-to-end machine learning lifecycle making it easy for anyone to build ML models and incorporate them into broader applications
- AutoML Vision Edge, another Auto ML entry, tackles the latency and unreliable connectivity challenge by providing faster inference capabilities of image recognition models
- The AutoML Video (beta) can help you create custom models to automatically classify video content with labels defined by developers. Want to understand traffic patterns? Need to simplify tasks like automatically removing commercials? Want to create highlight reels? You got yourself covered with Auto ML Video.
Google really covered a lot of what enterprises need to make Big Data, AI and ML work more efficiently for the enterprise.
But then, Google’s focus clearly is now all about meeting the ever-changing and ever-growing needs of businesses and industries and launching solutions to mitigate these challenges. And the Google Cloud Next’19 offerings show that Google means business.