Speaker "Nisha Talagala" Details Back



The Unspoken Truths of Deploying and Scaling ML in Production


Machine Learning is everywhere. However translating a data scientist's model into an operational environment is challenging for many reasons. Models may need to be distributed to remote applications to generate predictions, or in the case of re-training, existing models may need to be updated or replaced. To monitor and diagnose such configurations requires tracking many variables (such as performance counters, models, ML algorithm specific statistics and more). The situation is further complicated by the need for two very distinct roles (operations and data science) to collaborate, and the varied array of analytic engines that need to be combined for a practical deployment. We discuss the landscape of machine learning and deep learning infrastructure available to production users today and the challenges of addressing the production lifecycle and combining DevOps with ML (MLOps). We illustrate advances made in this space in both industry and academia and specific solutions involving popular engines such as Apache Spark and TensorFlow.


Nisha Talagala is the CEO and founder of Pyxeda AI. Previously, Nisha co-founded ParallelM which pioneered the MLOps practice of managing machine learning in production. Nisha is a recognized leader in the operational machine learning space, having also driven the USENIX Operational ML Conference, the first industry/academic conference on production AI/ML. Nisha was previously a Fellow at SanDisk and Fellow/Lead Architect at Fusion-io, where she worked on innovation in non-volatile memory technologies and applications. Nisha has more than 20 years of expertise in software development, distributed systems, technical strategy and product leadership. She has worked as technology lead for server flash at Intel - where she led server platform non-volatile memory technology development, storage-memory convergence, and partnerships. Prior to Intel, Nisha was the CTO of Gear6, where she designed and built clustered computing caches for high performance I/O environments.  Nisha earned her PhD at UC Berkeley where she did research on clusters and distributed systems. Nisha holds 63 patents in distributed systems and software, is a frequent speaker at industry and academic events, and is a contributing writer to several online publications.