This October 10-11, the Revevol team took to London for the Google Cloud Next 2018 Summit for two full days of innovation, inspiration, and important product announcements.
While we heard echoes of what was presented at Google Cloud Next San Francisco in July earlier this year, there were some breakout sessions and announcements that particularly made our ears perk up.
In case you weren’t able to attend at all or were too busy demoing cool products and collecting free goodies to compile your notes, we’ve put together a shortlist of what to take away (besides the free mugs and t-shirts).
Here’s our far from exhaustive list of top five sessions and announcements:
If to you, ML might as well be spelled UFO (and we’re not talking about this useful application), this one is for you. BigQuery ML, or BigQuery Machine Learning is a new capability that allows data analysts and data scientists to easily build machine learning models directly from BigQuery with simple SQL commands, making machine learning more accessible to all.
Until now, in order to perform machine learning capabilities with datasets, you first had to export the data from your Data Sink to an ML platform (Tensorflow, Jupyter, etc). Conversely, BigQuery ML brings Machine Learning directly to your data. With simple queries, you can easily define ML models and explore new insights. All the ML benefits without the daunting fuss of mastering some hard-to-master tools.
At Revevol we are already big fans of BigQuery and use the technology in some of our products including YAMM and Playengo. You can also discover the role that BigQuery played in this project, a solution built to help digitize logistics controls for Solvay in collaboration with Revevol and Google Cloud Platform.
We look forward to exploring new depths with this feature, now available in beta.
Cloud Composer is the new Google version of the Apache Airflow platform that allows you to build flexible, easy data pipelines on Google Cloud. In other words, it allows you to define and orchestrate different steps within a workflow for a multi-cloud deployment strategy.
As workflow processes are increasingly indispensable in the industry, this is a product to watch closely.
3. Consolidating Operation Data to Your BigQuery Data Lake Using Apache Beam
Big data is far more than just a buzzword, it’s the modern-day reality of the tech world. It is, therefore, more important than ever for industry players to master the topic.
In this breakout session, we saw how a classic ingestion pipeline is built using Cloud Pub/Sub and Cloud Dataflow and how data are later stored and refined. At Revevol, we are no stranger to these methods and use this type of architecture in some of our own projects.
At the crossroads of classical methodologies and Google Cloud technologies, the possibilities are endless.
This solution includes new Dialogflow features alongside other tools like Agent Assist (supports a live agent during a conversation and provides real-time relevant info) and Conversational Topic Modeler (uses Google AI to analyze historical audio and chat logs to uncover customer interaction trends).
A hot topic for contact center operations and IT leaders, Google’s new enhancements to Dialogflow Enterprise Edition will help to design smarter interfaces in order to assist live agents and perform analytics.
Another message that rang loud and clear at Google Cloud Next ‘18: Google is going serverless all the way.
The London edition featured more new breakthroughs in Google’s serverless capacities. From the general availability of Cloud Functions to the announcement of Knative (a Kubernetes add-on that allows users to run serverless workloads on their own Kubernetes instance), Google has sure come a long way since the release of Google App Engine in 2008.