TensorFlow London: Building a Data Science Platform
TensorFlowLDN 16
Talk Title: Building a Data Science Platform
Abstract: Machine and Deep Learning Frameworks such as Apache Spark, TensorFlow, MXNet, and PyTorch enable anyone to train deep learning models. It is relatively easy to get started on a laptop and train a basic, non-distributed model with sample at-rest data. However, moving from a single laptop setup towards a scalable, production-grade data science platform is a completely different challenge and, arguably, one of the most difficult ones, as it involves collaborating with different teams across an organization. Today, data scientists are dependent on the infrastructure and operations team, waiting for the compute resources to deploy models and then using different languages and tools in development and production environments.In this talk we discuss the challenges of moving from locally developing a model to deploying an integrated data science platform.
Bio: Joel is a systems engineer with a focus on NoSQL and distributed technologies. Working at Mesosphere Joel helps organizations adopt modern technologies including containers, streaming technologies, NoSQL, ML and AI.
Emil A. Siemes is a long-term Java veteran interested in building, running, and managing the next generation of data-driven web and mobile applications. After several years as Java Architect with Sun Microsystems, Aplix, Wily, SpringSource (VMware) and Hortonworks Emil joined Mesosphere, where he helps customers modernize their applications with container, fast- and big-data as well as ML & AI technologies.