Everyone is working on the fun parts of ML - the core algorithms and infrastructure. What about productionizing aspects of machine learning? How do you build workflows/pipelines that ingest data, transform/enrich it, and deploy to production? In other words, what is your vision of a machine learning platform. How do we do simple experimentation such as parameters sweeps and track the our parameters and outcomes? What are the core algorithms that your customers are interested and using? Is there a wide variation across customers/industries?
Meet the Speakers:
- Avery Ching, Software Engineer at Facebook (moderator)
Avery has a PhD from Northwestern University in the area of parallel computing. He worked at Yahoo! Search for four years on the web map analytics platform, large-scale ad hoc serving infrastructure, and cluster management. During the past year and a half, he has been working at Facebook in the general area of big data computational frameworks (Corona – scalable MapReduce and Giraph – scalable graph processing).
- Ted Dunning, Chief Architect at MapR
Ted Dunning is Chief Applications Architect at MapR Technologies and committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects and mentor for Apache Storm, Apache Spark and Apache Fink. Ted was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems. He built fraud detection systems for ID Analytics (LifeLock) and he has 24 patents issued to date and a dozen pending. Ted has a PhD in computing science from the University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. He also bought the beer at the first Hadoop user group meeting.
- SriSatish Ambati at 0xdata
Sri is co-founder and ceo of 0xdata (@hexadata), the builders of H2O. H2O democratizes bigdata science and makes hadoop do math for better predictions. Before 0xdata, Sri spent time scaling R over bigdata with researchers at Purdue and Stanford. Prior to that Sri co-founded Platfora and was the Director of Engineering at DataStax. Before that Sri was Partner & Performance engineer at java multi-core startup, Azul Systems, tinkering with the entire ecosystem of enterprise apps at scale.
Before that Sri was at sabbatical pursuing Theoretical Neuroscience at Berkeley. Prior to that Sri worked on nosql trie based index for semistructured data at in-memory index startup RightOrder.
Sri is known for his knack for envisioning killer apps in fast evolving spaces and assembling stellar teams towards productizing that vision. A regular speaker in the BigData, NoSQL and Java circuit, Sri leaves trail @srisatish.
- Xiangrui Meng at Databricks
Xiangrui Meng is an Apache Spark PMC member and a software engineer at Databricks. His main interests center around developing and implementing scalable algorithms for scientific applications. He has been actively involved in the development and maintenance of Spark MLlib since he joined Databricks. Before Databricks, he worked as an applied research engineer at LinkedIn, where he was the main developer of an offline machine learning framework in Hadoop MapReduce. His Ph.D. work at Stanford is on randomized algorithms for large-scale linear regression problems.
- Nachum Shacham, Prinicpal Data Scientist at Paypal
Nachum Shacham is a Principal Data Scientist at PayPal where he is working on modeling and extracting business value from large transactional, behavioral, and system performance datasets. Before, he was with eBay, analyzing performance of large data platforms. Prior, he was with SRI, leading research in internet technologies, generation of wireless internet and real-time voice and video communications over mobile networks. As co-founded CTO of Metreo, he developed models for B2B pricing and subsequently created revenue models for online display and search advertising. Nachum holds BScEE & MScEE from the Technion, and PHD in EECS from UC Berkeley. Dr. Shacham is a Fellow of the IEEE.
