Data Science and Machine Learning News, October 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, Anaconda, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, Domino, Elastic, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.
Cloudera and Hortonworks Announce Merger to Create World’s Leading Next Generation Data Platform and Deliver Industry’s First Enterprise Data Cloud
Cloudera and Hortonworks announced an all-stock “merger of equals” of the two companies, though Cloudera stockholders will own 60% and Hortonworks stockholders will own 40% of the merged company. The merger is expected to be completed in early 2019.
DataRobot has raised an additional $100M in a Series D funding round, bringing its total funding to $225M. The funding will go towards expanding its products and services portfolio, as well as continuing global growth and meeting the current demand for its existing automated machine learning offerings.
Domino Data Lab launched Domino 3.0, featuring a new module, Launchpad. Launchpad addresses the gap organizations frequently encounter between building models and deploying them into production by simplifying the deployment process and accelerating the process of model iteration. In particular, Launchpad uses Docker for automatic infrastructure provisioning on deployment, and it supports frameworks for building web apps in Python and R such as Dash, Flask, and Shiny. Additional features include a catalog for models and data to expedite the discovery process, and automatic model versioning to track experiments and make them automatically reproducible.
RapidMiner launched the RapidMiner AI Cloud and RapidMiner Auto Model application. Auto Model is a code-free interface that lets users build a predictive model with a few clicks, then output it as a RapidMiner Studio process to visualize, comprehend, and tweak as needed. RapidMiner Auto Model is currently in beta; supplementary capabilities for RapidMiner AI Cloud are planned for the future.
SAP announced a number of improvements to SAP Leonardo Machine Learning. Among the updates is SAP Conversational AI, which aims to simplify the process of rolling out customer support bots, with capabilities such as training and usage analytics, versioning, and automatic bot generation based on a policy document. More machine learning services are in the works for SAP, including text extraction from images, object identification in images, and detecting text patterns to match similar documents, along with text-to-speech and speech-to-text offerings in Google Cloud. SAP’s machine learning offerings will be available on Google Cloud Platform in November, which will enable customers to use SAP Cloud Platform and Google Cloud Platform together.