Running AI workloads in containers (sponsored by BMC Software)
Developing, deploying and managing AI and anomaly detection models is tough business. Bridging the worlds of data science, data engineering, software engineering, and sustaining engineering is probably the toughest part of implementing AI at scale. Darren Chinen details how Malwarebytes has leveraged containerization, scheduling, and orchestration to build a behavioral detection platform and a pipeline to bring models from concept to production.
Sponsored by BMC Software.
What you'll learn
- Discover how Malwarebytes can bring models from concept to production
Darren Chinen is a senior director of data science and engineering at Malwarebytes. He began his career in data more than 20 years ago. The early part of his career was focused on analytics and building data warehouses for companies like Legato/EMC, E*TRADE, Lucent Technologies, Peet’s Coffee, and Apple. During his time at Apple he was initially focused on Apple Online Store analytics, then moved on to help build the data science platform. It was in the transition that he was forced to evolve from relational databases to big data. He has spent the last six years of his career in big data where he lived through all the big data fads at Apple, Roku, GoPro, and Malwarebytes. It was a charming time when we all thought that map-reduce would save the world, then Hive, then mixed workloads made us invent YARN, Sqoop was a thing, and then came Cassandra which made us all talk about the CAP theorem. Now we live in a time of streaming data, complex multicloud orchestration, real-time processing, next-gen web applications and AI, which Darren is convinced as of this writing will actually save the world and end world hunger.
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