Matthew Tovbin

5 Abonné

Versatile software engineer with deep knowledge in search engines, distributed systems, scalable architectures, data processing, data management systems and web technologies. Research interests include search engines, distributed computing, image processing, computer vision, natural language processing and machine learning. - Programming languages: expert in Java, Scala and C#; proficient in OCaml, Python, M-Code; competent in C/C++ and JavaScript - Frameworks: WCF, LINQ, Entities, [N]Hibernate, Hive, Play!, Spray.io, Akka, ZeroMQ, Lwt - Databases: Cassandra, HBase, Riak, MongoDB, Redis, MySQL, SQL Server, Kafka - Distributed computing: Hadoop, Spark - Machine learning: SparkML, Optimus...

spark mllib apache spark machine learning einstein salesforce sparkml salesforce einstein feature engineering transmogrif.ai transmogrifai model selection feature selection machine learning pipelines ml type safety optimus prime automl sbtb data pipelines scala big data lambda etl spark+ai summit 2019 spark summit 2019 2019 spark summit spark+ai platform scalatest relational data structured data sbtb2018 mleap pmml pfa dreamforce2018 dreamforce18 dreamforce scalasf scalebythebay sbtb2017 functional programming scaladays sbtb2016 scala by the bay scalabythebay scalae2016 scalae

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Matthew Tovbin

Présentations

Doubt Truth to be a Liar: Non Triviality of Type Safety for Machine Learning (at Salesforce)

Fantastic ML apps and how to build them

Implementing AutoML Techniques at Salesforce Scale

Meet TransmogrifAI, Open Source AutoML That Powers Einstein Predictions

Making Spark ML Models Portable - Know Your Options

Meet TransmogrifAI, Open Source AutoML That Powers Einstein Predictions

The Rule of 10,000 Spark Jobs - Learning from Exceptions and Serializing Your Knowledge

J’aime

Fairness-aware Machine Learning: Practical Challenges and Lessons Learned (WSDM 2019 Tutorial)