PipelineAI

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City: San Francisco

State: California

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Country: United States

PipelineAI: Real-Time Enterprise AI PipelineAI continuously trains, optimizes, and serves machine-learning models on live-streaming data directly in production. The platform supports all major AI and machine-learning frameworks, including Spark ML, Apache Kafka, Scikit-Learn, Xgboost, R, TensorFlow, Keras, and PyTorch. We treat model optimizing and serving as a first-class citizen in the modern data pipeline - alongside model training. We give data scientists and engineers the freedom to quickly deploy, test, and rollback (if needed) their models directly in production. A concept we practiced heavily at Netflix, this freedom comes with responsibility. PipelineAI provides the tooling, infrastructure, and dashboards necessary to responsibily manage production directly - and with no downtime. We currently support models built with Spark, Tensorflow, Scikit-learn, XGBoost, and R. We are constantly tuning and optimizing our runtime to provide the best price per prediction available - even across multiple data centers and cloud vendors. Our hybrid-cloud “auto-shift” technology compliments the traditional, single-cloud “auto-scale” technology. PipelineAI opens up new ways to increase performance of your predictions, improve the uptime of your model serving infrastructure, and reduce cost per prediction. Media Highlights * Danny Bickson Blog (formerly GraphLab, acquired by Apple late last year): http://bickson.blogspot.com/2017/01/pipelineio-production-environment-to.html * Dylan Raithel from InfoQ (this is on the PANCAKE STACK Workshop that has been driving a lot of our POCs): https://www.infoq.com/articles/fregly-pancake-stack * Jeff Meyerson’s Software Engineering Daily Podcast https://www.podcastchart.com/podcasts/software-engineering-daily-podcast/episodes/pancake-stack-data-engineering-with-chris-fregly

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