Ekeka Abazie Learns How To Create ML Workflows in GCP

Learning about GCP (Google Cloud Platform)

On May 29, I attended the Machine Learning workshop at Google’s office in Venice Beach. I was super excited to be able to visit one of the leaders of the AI industry as well as a major search engine; in fact, since coming to California, this has been a big goal of mine as a CS aspirant.

I attended the workshop with my HPC supervisor Asya Shklyar who also generously provided me with transportation to the event. The workshop was aimed at marketing Google’s AI software such as BigQuery, hosted JupyterLab, and Dataflow to businesses with a consideration of ethics and applications to machine learning at the end. The ethics and applications discussion at the end had representatives from USC Keck School of Medicine, Pluto (which is now in partnership with Google), and a financial consulting business.

Soaking in the tech atmosphere

BigQuery is a software application that Google uses to process business requests to survey large amounts of information (terabytes of data). By using this technology, a business is able to use different machine learning models to, for example, predict whether or not a customer that visits their website will end up buying at the end of their visit. The first practical lab portion of the workshop gave me a chance to use a binary system of “will not buy vs will buy” to pose a query to the software to yield predictions based on trends of input data that it had been fed. The query also listed out in a very user-friendly format that even a layperson would understand better specifics of the data that it had been fed.

Putting it all together

I found Dataflow to be a very interesting development because it was based on real-time data as opposed to the previous model Dataproc that was solely based on fed data. Dataflow allowed for the data to interact with the machine learning model as opposed to the machine learning model interacting with it. It also has much more practical applications, because I could envision more customers being available data sets for the integration of the machine learning model which could increase the accuracy of its predictions and yield better results for the business.

At the end of the workshop, we heard from representatives of different sectors namely health (Keck School of Medicine) and finance. I found it amazing that the USC School of Medicine, one of the premier research-based health institutions in the US, used machine learning models from Google to match patients with appropriate clinical trials. It’s also important to note that this workshop showed me that Google is an AI business and that that is how they make their money which I had always been curious about. There was also a fireside chat, but Senior Shkylar arranged for me to have a tour of the building which I am very grateful for. Also, the food was awesome.

By Ekeka Abazie