The era of big data generation is upon us. Devices ranging from sensors to robots and sophisticated applications are generating increasing amounts of rich data (time series, text, images, sound, video, etc.). For such data to benefit a business’s bottom line, insights must be extracted, a process that increasingly requires machine learning (ML) and deep learning (DL) approaches deployed in production applications use cases.
Production ML is complicated by several challenges, including the need for two very distinct skill sets (operations and data science) to collaborate, the inherent complexity and uniqueness of ML itself, when compared to other apps, and the varied array of analytic engines that need to be combined for a practical deployment, often across physically distributed infrastructure. Nisha Talagala shares solutions and techniques for effectively managing machine learning and deep learning in production with popular analytic engines such as Apache Spark, TensorFlow, and Apache Flink.
This session is sponsored by ParalleI Machines.
Nisha Talagala is CTO and vice president of engineering at Parallel Machines, where she focuses on production machine learning and deep learning solutions from the edge to the cloud. Nisha has more than 15 years of expertise in software development, distributed systems, I/O solutions, persistent memory, and flash. Previously, Nisha was a fellow at SanDisk; a fellow and lead architect at Fusion-io, where she drove innovation in nonvolatile memory, including the industry’s first persistent memory solution; technology lead for server flash at Intel, where she led server platform nonvolatile memory technology development, storage-memory convergence, and technical partner engagements; and CTO of Gear6, where she designed and built clustered computing caches for high-performance I/O environments. Nisha holds 48 patents in distributed systems, networking, storage, performance, and nonvolatile memory. She has authored many technical ad research publications and serves on multiple academic and industry conference program committees. Nisha holds a PhD from UC Berkeley, where her research focused on software clustering and distributed storage.
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