When present I attended as much of machine learning and AI talks as possible. Despite the fact that I already knew quite a lot about the Google Cloud services, the talks were interesting enough to keep me motivated throughout the day. In total I’ve attended five talks.
Not all where equal interested, but the purpose was most certainly reached: prepare and motivate us for using new technologies concerning AI and machine learning in the cloud. To give you as reader a view of my experience, I made a short summary of the talks I found the most interesting.
Talk 1: Going Beyond the Traditional Enterprise Data Warehouse with BigQuery (Speaker: Robert Saxby, Google)
This was the first talk of the day. After a pretty long keynote, we could start with the real deal. The presentation itself tackled BigQuery, the serverless cloud-native data warehouse service of Google. First, we received a very short introduction on BigQuery. The speaker, I think, was under the impression that everybody already knew what BigQuery was.
However, for me this was not the case. It became clear that this was going to be quite the technical talk. Robert discussed some architectural designs and an internal look into BigQuery. This was backed by some best practices for partitioning. In the demo the theory was put to practice. A complex query is submitted and processed a couple of terabytes in just a few seconds. It’s clear that this is a very powerful tool for processing a lot of data without having to worry about the infrastructure. Just deliver your data to BigQuery and let Google handle the rest of the work.
Of course, I’m also interested in some machine learning. At the end of the talk they quickly tackled BigQuery ML. This is a way to store models as BigQuery datasets. Creating and training the models can be done with queries, and you are only charged for the amount of data that is used.
It’s clear that both BigQuery and BigQuery ML are two technologies that will fit perfect in a big data or machine learning pipeline. Definitely something we have to experiment with and learn more about. With Infofarm we are always looking for building the best and most cost-saving solutions and that’s where Google’s BigQuery fits perfectly.
Talk 3: Atos: How Cloud is fundamental for your AI (Speakers: Wim Los en Robin Zondag, Atos)
Besides speakers from Google, there were also a couple of partners. Some of these partners got their own slot where they shared their experiences and usages with Google Cloud. This could be very interesting for us to see how other big companies make use of Google’s Cloud services for their machine learning projects.
I’ve attended the talk of Wim Los (Senior Vice President Google Alliance) en Robin Zondag (Global Head of Atos AI Labs) from Atos. Atos is a French IT-company and is a global leader in digital transformation. With the use of the cloud it provides end-to-end Orchestrated Hybrid Cloud, Big Data, Business Applications and Digital Workplace solutions.
Atos was proud to announce that they opened their first AI Lab in the UK. The ‘Atos AI-Lab’ is the outcome of the partnership between Google and Atos. The lab is offering experiments/solutions for both public and private companies on artificial intelligence. In this lab they need a standard way for working. This was tackled in the first part of the presentation. Explaining on how a they handle the perfect AI project and how the perfect team for this job looks like.
In the second part of the presentation they covered where the services and technologies of Google Cloud fits in this process.
It is clear that for machine learning projects Google Cloud can offer a big value for your business. After hearing their success stories, I conclude that with Infofarm we can’t fall behind on this! We should shift our expertise's more to the cloud, so that we can keep offering the most innovative solutions for our clients. And hopefully we will be standing on that stage someday.