I love events and conferences because they let me indulge in my passion for learning. In my day jobs, I wear a lot of hats, including customer advisor, a researcher on data science, and also a teacher and invited professor at several universities. The common thread running through these is the need to stay curious. I feel that there is an expectation that I will stay up-to-date on all the latest developments in my field and beyond. Fortunately, that suits me perfectly!
September’s SAS Explore event, to be held in Las Vegas, looks like it will have some great papers and sessions. Here is my top pick of the sessions that I’d like to know more about—and which I would suggest to customers and students are well worth exploring at the event.
1. Enhance Forecasting Accuracy with Time-Series Segmentation and Machine Learning
Spiros Potamitis will be running this breakout on improving forecast accuracy by using time series segmentation and individual modelling. He will be starting by talking about the temptation to use automated machine learning. However, I’m interested in his conclusion that although AutoML may be tempting, the key actually lies in aligning the best modelling technique with unique data. This session will be an opportunity to find out more about using demand classification in SAS Visual Forecasting for optimal modelling and optimised accuracy and speed in the cloud.
3. Event Stream Processing open-source integration with GIT and Grafana on Azure Marketplace
This session, run by Daniele Cazzari, is a great opportunity to learn about a fantastic SAS application, SAS Event Stream Processing. I would encourage anyone to attend this to learn more about how to package and store analytical streaming projects, created in SAS Event Stream Processing in GIT. It will also enable you to learn how to visualise streaming data with Grafana, and explore the Lightweight Kubernetes Architecture. Finally, there will be more about how to deploy the application on Azure Marketplace for swift streaming environment setup and preview new features.
3. An end-to-end case study on flooding based on ML, forecasting, optimization, and spatial data
One of the stand-out aspects of this year’s SAS Hackathon—at which I was a mentor—was the sheer number of teams that chose to look at flooding. It shouldn’t be surprising, because understanding the global threat of flooding is vital for human survival and property protection. This session, led by Carlos Pinheiro, will use a case study approach to explain how flooding events can be predicted and mitigated using data analysis and machine learning models. Carlos will cover how optimisation models can help to improve evacuation planning and delivery. The session will also consider how forecasting and what-if analysis prepare areas for the potential impact of flooding, and how spatial regression estimates locations for proactive mitigation actions.
4. Transition from Enterprise Guide and Data Integration Studio to SAS Studio on Viya 4 and go beyond
Alexey Vodilin will be leading a breakout on the automated process for importing Enterprise Guide and Data Integration Studio projects into SAS Studio on Viya. This is another session that provides essential practical information to enable a seamless transition from EG/DI to SAS Studio on Viya. I’m expecting to find out more about how to unlock cutting-edge data engineering and analytics capabilities on a Cloud-native platform. I will be interested to see how the automated process works to convert process flows and Data Integration jobs into the equivalent SAS Studio Flows. Most of all, I’m looking forward to discovering some migration strategies, hearing expert tips, and exploring the latest features in SAS Studio on Viya 4.
5. Machine Learning Meets Retention: A Higher Education End-to-End Guide.
In this breakout session, Amanda Zhao promises to take us to the cutting edge of student retention in higher education. The key in this realm is that institutions need to move beyond descriptive and prescriptive analytics, and instead start to take advantage of the capacity of more advanced AI and machine learning models to predict retention data early enough to change the outcomes for individual students. Amanda will be covering using SAS Visual Data Mining and Machine Learning in this context. I am expecting to hear about success stories, lessons learned, and considerations for predictive modelling. I also expect it to be a very practical session, with hands-on demonstrations focused on techniques such as data preparation, model building, optimisation, and interpretability.
Which sessions have caught your attention?
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