Submitted by Anna Wykes
Software Developers/Engineers are being asked more and more to work with various aspects of data, in particular the productionisation of Machine Learning Models. In this session we will talk about various Azure technologies we can use in our day jobs to achieve this, including how to manage Feature Stores and monitoring model life cycles, plus we will discuss the different architectures that can be implemented.
"85% of big data projects fail" (Gartner, 2017). "87% of data science projects never make it to production" (VentureBeat, 2019). "Through 2022, only 20% of analytic insights will deliver business outcomes" (Gartner, 2019). When working with data it is vital that different Data Professionals and Software Developers/Engineers work in harmony together for successful outcomes, consequently we will also cover how we can try to best achieve this and how this has been done when working with clients in our day job.
The session will be delivered by myself, a Data Engineering Consultant with a background in Software Engineering, and my colleague Luke Menzies, who is a Data Science Consultant
A veteran Software & Data Engineer, and a Microsoft Data Platform MVP, with over 16 years of experience. I have tackled projects from real-time analytics with Scala & Kafka, building out Data Lakes with spark and applying engineering to Data Science. She is a senior consultant with Advancing Analytics, helping shape & evolve their data engineering practice. I have a real passion for data and strives to bring the worlds of Software Development and Data Science closer together. Other areas of interest Agile methodologies, helping to organize/run local Code Clubs, and co-organising local data meetups