Data Science and Machine Learning News, November 2018

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Amazon, Anaconda, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, DominoElastic, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, SnapLogic, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.

Datawatch Angoss Simplifies Data Science and Analytic Tasks on the Apache Spark Platform

Datawatch launched Datawatch Angoss KnowledgeSTUDIO for Apache Spark, allowing data science teams to create predictive models via its drag-and-drop interface. Datawatch customers no longer have to move data out of Spark to analyze it in Datawatch; they can manipulate, combine, and profile data within a Spark cluster.

SnapLogic Introduces Self-Service Solution for End-to-End Machine Learning

SnapLogic launched SnapLogic Data Science to speed up the process of developing and deploying machine learning models with minimal coding, while pairing it with the existing SnapLogic data integration platform to create an end-to-end pipeline spanning the data workflow.

Amazon Web Services Announces 13 New Machine Learning Services and Capabilities, Including a Custom Chip for Machine Learning Inference, and a 1/18 Scale Autonomous Race Car for Developers

At AWS re:Invent, Amazon Web Services announced a baker’s dozen of new machine learning services and capabilities, falling into three major categories: infrastructure improvements, enhancements to Amazon SageMaker, and AI services. Infrastructure improvements include new Amazon Elastic Compute Cloud (EC2) GPU instances, an AWS-optimized TensorFlow framework, Amazon Elastic Inference (a cost-effective offering for inference performance), and Amazon Inferentia (a high performance machine learning inference chip, available in 2019). Amazon SageMaker enhancements consist of Ground Truth (data labeling to increase the accuracy of training data sets), AWS Marketplace for Machine Learning (a marketplace of algorithms and models that can be used in SageMaker), DeepRacer (an autonomous race car as a way to teach reinforcement learning), and Neo (a deep learning model compiler). Finally, the new AI services contain Textract (text and data extraction from documents), Comprehend Medical (an NLP service for medical text), Personalize (real-time personalized recommendations), and Forecast (time series forecasting). Most of these capabilities are available today, though others remain in preview; in particular, Inferentia will not be available until 2019.

Along with the debut of the Amazon Web Services Marketplace for Machine Learning, several companies announced products available through the Marketplace at AWS re:Invent:

H2O.ai Launches Full Suite of Artificial Intelligence Platforms on Amazon Web Services Marketplace for Machine Learning

H2O.ai debuted both Driverless AI and the open-source H2O on the AWS Marketplace for Machine Learning. H2O.ai customers using AWS will now be able to deploy models created in these H2O products into Amazon SageMaker.

KNIME released several new models to the AWS Marketplace for Machine Learning; these models can be deployed to Amazon SageMaker as Docker containers. The models include a Simple Income Predictor, a Simple Chemistry Binding Predictor, a Basic Credit Score Predictor, and a Basic Churn Predictor.

TIBCO Brings Advanced Data Science and Machine Learning Solutions to AWS

TIBCO announced the general availability of TIBCO Data Science on the AWS Marketplace for Machine Learning. This will allow TIBCO customers to process data from Amazon EMR and RedShift using TIBCO algorithms on AWS.

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