There was a core theme to the conference which was "Data Never Rests". The ways in which we process data is changing, we need to work with more data in heterogeneous formats. As a result we need to process them with different more advanced tools. As developers we need to keep up with the skills to compete. Conferences like SQLBits are a fantastic way to interact with other data professionals, speakers and vendors such as Microsoft. SQLBits is a juggernaut of content.
Azure Databricks training live
We arrived on the Thursday (SQLBits day 2) to deliver a sold out full day training session on Big Data processing with Azure Databricks titled "Data Engineering Vs Data Science". This session was designed to take a data professional through a lot of the key topics they need to know to be able to start working with Azure Databricks. This is a condensed version of our "Zero-to-Hero" in Azure Databricks course. This course is designed to be an applied Databricks course based on what our customers have asked for and the problems we face daily with those customers. We had over 100 attendees and we got some incredible questions. I am in the process of curating those questions in to a blog (there are 70 questions, so it might take a few days). If you want to find out more about that training course you can find out below.
The Friday saw Terry deliver a session on using Deep Learning to generate new sessions for SQLBits. We were very pleased to have this session mentioned in the keynote by Microsoft as one of the most interesting sessions of the event. This session looked at using a Long Short Term Memory model LSTM in Keras to reading in session scraped from the internet and generate a new one. This generated interesting abstracts such as "In this session you will learn how to become a server". As soon as this session is live I will post it here.
On Saturday we had our last session of the conference which looked at how to accelerate models in to production using Azure DevOps and DataOps. This is a condensed version of the training day I offer on deploying machine learning models with Python, Docker and Kubernetes. That session is available to watch now.