Sep 23–26, 2019

Schedule: Automation in data science and data sessions

11:20am12:00pm Wednesday, September 25, 2019
Location: 1A 23/24
Moty Fania (Intel)
Moty Fania details Intel’s IT experience of implementing a sales AI platform. This platform is based on streaming, microservices architecture with a message bus backbone. It was designed for real-time data extraction and reasoning and handles the processing of millions of website pages and is capable of sifting through millions of tweets per day. Read more.
5:25pm6:05pm Wednesday, September 25, 2019
Location: 1E 06
venkata gunnu (Comcast), Harish Doddi (Datatron)
Machine learning infrastructure is key to the success of AI at scale in enterprises, with many challenges when you want to bring machine learning models to a production environment, given the legacy of the enterprise environment. Venkata Gunnu and Harish Doddi explore some key insights, what worked, what didn't work, and best practices that helped the data engineering and data science teams. Read more.
2:05pm2:45pm Thursday, September 26, 2019
Location: 1A 12/14
Mumin Ransom (Comcast), Nick Pinckernell (Comcast)
Mumin Ransom gives an overview of the data management and privacy challenges around automating ML model (re)deployments and stream-based inferencing at scale. Read more.
2:05pm2:45pm Thursday, September 26, 2019
Location: 1A 21/22
Diego Oppenheimer (Algorithmia)
Machine learning (ML) will fundamentally change the way we build and maintain applications. Diego Oppenheimer dives into how you can adapt your infrastructure, operations, staffing, and training to meet the challenges of the new software development life cycle (SDLC) without throwing away everything that already works. Read more.
3:45pm4:25pm Thursday, September 26, 2019
Location: 1A 21/22
Sireesha Muppala (Amazon Web Services), Shelbee Eigenbrode (Amazon Web Services), Randall DeFauw (Amazon Web Services)
As an increasing level of automation becomes available to data science, the balance between automation and quality needs to be maintained. Applying DevOps practices to machine learning workloads brings models to the market faster and maintains the quality and integrity of those models. Sireesha Muppala, Shelbee Eigenbrode, and Randall DeFauw explore applying DevOps practices to ML workloads. Read more.

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