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Data Science and Machine Learning News Roundup, May 2019

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, Domino, Elastic, Google, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.

Domino Data Lab Champions Expert Data Scientists While Outpacing Walled-Garden Data Science Platforms

Domino announced key updates to its data science platform at Rev 2, its annual data science leader summit. For data science managers, the new Control Center provides information on what an organization’s data science team members are doing, helping managers address any blocking issues and prioritize projects appropriately. The Experiment Manager’s new Activity Feed supplies data scientists with better organizational and tracking capabilities on their experiments. The Compute Grid and Compute Engine, built on Kubernetes, will make it easier for IT teams to install and administer Domino, even in complex hybrid cloud environments. Finally, the beta Domino Community Forum will allow Domino users to share best practices with each other, as well as submit feature requests and feedback to Domino directly. With governance becoming a top priority across data science practices, Domino’s platform improvements around monitoring and making experiments repeatable will make this important ability easier for its users.

Informatica Unveils AI-Powered Product Innovations and Strengthens Industry Partnerships at Informatica World 2019

At Informatica World, Informatica publicized a number of key partnerships, both new and enhanced. Most of these partnerships involve additional support for cloud services. This includes storage, both data warehouses (Amazon Redshift) and data lakes (Azure, Databricks). Informatica also announced a new Tableau Dashboard Extension that enables Informatica Enterprise Data Catalog from within the Tableau platform. Finally, Informatica and Google Cloud are broadening their existing partnership by making Intelligent Cloud Services available on Google Cloud Platform, and providing increased support for Google BigQuery and Google Cloud Dataproc within Informatica. Amalgam Insights attended Informatica World and provides a deeper assessment of Informatica’s partnerships, as well as CLAIRE-ity on Informatica’s AI initiatives.

Microsoft delivers new advancements in Azure from cloud to edge ahead of Microsoft Build conference

Microsoft announced a number of new Azure Machine Learning and Azure AI capabilities. Azure Machine Learning has been integrated with Azure DevOps to provide “MLOps” capabilities that enable reproducibility, auditability, and automation of the full machine learning lifecycle. This marks a notable increase in making the machine learning model process more governable and compliant with regulatory needs. Azure Machine Learning also has a new visual drag-and-drop interface to facilitate codeless machine learning model creation, making the process of building machine learning models more user-friendly. On the Azure AI side, Azure Cognitive Services launched Personalizer, which provides users with specific recommendations to inform their decision-making process. Personalizer is part of the new “Decisions” category within Azure Cognitive Services; other Decisions services include Content Moderator, an API to assist in moderation and reviewing of text, images, and videos; and Anomaly Detector, an API that ingests time-series data and chooses an appropriate anomaly detection model for that data. Finally, Microsoft added a “cognitive search” capability to Azure Search, which allows customers to apply Cognitive Services algorithms to search results of their structured and unstructured content.

Microsoft and General Assembly launch partnership to close the global AI skills gap

Microsoft also announced a partnership with General Assembly to address the dearth of qualified data workers, with the goal of training 15,000 workers by 2022 for various artificial intelligence and machine learning roles. The two companies will found an AI Standards Board to create standards and credentials for artificial intelligence skills. In addition, Microsoft and General Assembly will develop scalable training solutions for Microsoft customers, and establish an AI Talent network to connect qualified candidates to AI jobs. This continues the trend of major enterprises building internal training programs to bridge the data skills gap.

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The CLAIRE-ion Call at Informatica World 2019: AI Needs Managed Data and Data Management Needs AI

Knowledge of the Past, Uncertainty of the Future

From May 20-23, Amalgam Insights attended Informatica World 2019, Informatica’s end user summit dedicated to the world of data management. Over the years, Informatica has transformed from a data integration and data governance vendor to a broad-based enterprise data management vendor offering what it calls the “Intelligent Data Platform,” consisting of data integration, Big Data Management, Integration Platform as a Service, Data Quality and Governance, Master Data Management,  Enterprise Data Catalog, and Data Security & Privacy. Across all of these areas, Informatica has built market-leading products with the goal of providing high-quality point solutions that can work together to solve broad data challenges.

To support this holistic enterprise data management approach, Informatica has developed an artificial intelligence layer, called CLAIRE, to support metadata management, machine learning, and artificial intelligence services that provide context, automation, and anomaly recognition across Informatica’s varied data offerings. And at Informatica World 2019, CLAIRE was everywhere as AI served as a core theme of the event.

CLAIRE was mentioned in Informatica’s Intelligent Cloud Services, automating redundant, manual data processing steps. It was in Informatica Data Quality, cleansing and standardizing incoming data. It was in Data Integration, speeding up the integration process for non-standard data. It was in the Enterprise Data Catalog, helping users to understand where data was going across their organization. And it was in Data Security, identifying patterns and deviations in user activities that could indicate suspicious behavior, while contextually masking sensitive data for secure use.

What’s meant by CLAIRE? It’s the name tying together all of Informatica’s automated smart contextual improvements across its Intelligent Data Platform, surfacing relevant data, information, and recommendations just-in-time throughout various parts of the data pipeline. By bringing “AI” to data management, Informatica hopes to improve efficiency throughout the whole process, helping companies manage the growing pace of data ingestion.

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Data Science and Machine Learning News Roundup, April 2019

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, Domino, Elastic, Google, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.

Alteryx Acquires ClearStory Data to Accelerate Innovation in Data Science and Analytics

Alteryx acquired ClearStory Data, an analytics solution for complex and unstructured data with a focus on automating Big Data profiling, discovery, and data modeling.  This acquisition reflects Alteryx’s interest in expanding its native capabilities to include more in-house data visualization tools. ClearStory Data provides a visual focus on data prep, blending, and dashboarding with their Interactive Storyboards that partners with Alteryx’s ongoing augmentation of internal visualization capabilities throughout the workflow such as Visualytics.

Dataiku Announces the Release of Dataiku Lite Edition

Dataiku released two new versions of its machine learning platform, Dataiku Free and Dataiku Lite, targeted towards small and medium businesses. Dataiku Free will allow teams of up to three users to work together simultaneously; it is available both on-prem and on AWS and Azure. Dataiku Lite will provide support for Hadoop and job scheduling beyond the capabilities of Dataiku Free. Since Dataiku already partners with over 1000 small and medium businesses, creating versions of its existing platform more financially accessible to such organizations lowers a significant barrier to entry, and grooms smaller companies to grow their nascent data science practices within the Dataiku family.

DataRobot Celebrates One Billion Models Built on Its Cloud Platform

DataRobot announced that as of mid-April, its customers had built one billion models on its automatic machine learning program. Vice President of Product Management Phil Gurbacki noted that DataRobot customers build more than 2.5 million models per day. Given that the majority of models created are never successfully deployed – a common theme cited this month at both Enterprise Data World and at last week’s Open Data Science Conference – it seems likely that DataRobot customers don’t currently have one billion models operationalized. If the percentage of deployed models is significantly higher than the norm, though, this would certainly boost DataRobot in potential customers’ eyes, and serve to further legitimize AutoML software solutions as plausible options.

Microsoft, SAS, TIBCO Continue Investments in AI and Data Skills Training

Microsoft announced a new partnership with OpenClassrooms to train students for the AI job marketplace via online coursework and projects. Given an estimate that projects 30% of AI and data jobs will go unfilled by 2022, OpenClassrooms’ recruiting 1000 promising candidates seems like just the beginning of a much-needed effort to address the skills gap.

SAS provided more details on the AI education initiatives they announced last month. First, they launched SAS Viya for Learners, which will allow academic institutions to access SAS AI and machine learning tools for free. A new SAS machine learning course and two new Coursera courses will provide access to SAS Viya for Learners to those wanting to learn AI skills without being affiliated with a traditional academic institution. SAS also expanded on the new certifications they plan to offer: three SAS specialist certifications in machine learning, natural language and computer vision, and forecasting and optimization. Classroom and online options for pursuing both of these certifications will be available.

Meanwhile, TIBCO continued expanding its partnerships with educational institutions in Asia to broaden analytics knowledge in the region. Most recently, it has augmented its existing partnership with Singapore Polytechnic to train 1000 students in analytics and IoT skillsets by 2020. Other analytics education partnerships TIBCO has announced in the last year include Yuan Ze University in Taiwan, Asia Pacific University of Technology and Innovation in Malaysia, and BINUS University in Indonesia.

The big picture: existing data science degree programs and machine learning and AI bootcamps are not providing a large enough volume of highly-skilled job candidates quickly enough to fill many of these data-centric positions. Expect to hear more about additional educational efforts forthcoming from data science, machine learning, and AI vendors.

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Tom Petrocelli Clarifies How Cloud Foundry and Kubernetes Provide Different Paths to Microservices

DevOps Research Fellow Tom Petrocelli has just published a new report describing the roles that Cloud Foundry Application Runtime and Kubernetes play in supporting microservices. This report explores when each solution is appropriate and provides a set of vendors that provide resources and solutions to support the development of these open source projects.

Organizations and Vendors mentioned include: Cloud Foundry Foundation, Cloud Native Computing Foundation, Pivotal, IBM, Suse, Atos, Red Hat, Canonical, Rancher, Mesosphere, Heptio, Google, Amazon, Oracle, and Microsoft

To download this report, which has been made available at no cost until the end of February, go to https://amalgaminsights.com/product/analyst-insight-cloud-foundry-and-kubernetes-different-paths-to-microservices

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Microsoft Loves Linux and FOSS Because of Developers

Tom Petrocelli, Amalgam Insights Research Fellow
For much of the past 30 years, Microsoft was famous for its hostility toward Free and Open Source Software (FOSS). They reserved special disdain for Linux, the Unix-like operating system that first emerged in the 1990s. Linux arrived on the scene just as Microsoft was beginning to batter Unix with Windows NT. The Microsoft leadership at the time, especially Steve Ballmer, viewed Linux as an existential threat. They approached Linux with an “us versus them” mentality that was, at times, rabid.

It’s not news that times have changed and Microsoft with it. Instead of looking to destroy Linux and FOSS, Microsoft CEO Satya Nadella has embraced it.

Microsoft has begun to meld with the FOSS community, creating Linux-Windows combinations that were unthinkable in the Ballmer era.

In just the past few years Microsoft has:

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Lynne Baer: Clarifying Data Science Platforms for Business

Word cloud of data science software and terms

My name is Lynne Baer, and I’ll be covering the world of data science software for Amalgam Insights. I’ll investigate data science platforms and apps to solve the puzzle of getting the right tools to the right people and organizations.

“Data science” is on the tip of every executive’s tongue right now. The idea that new business initiatives (and improvements to existing ones) can be found in the data a company is already collecting is compelling. Perhaps your organization has already dipped its toes in the data discovery and analysis waters – your employees may be managing your company’s data in Informatica, or performing statistical analysis in Statistica, or experimenting with Tableau to transform data into visualizations.

But what is a Data Science Platform? Right now, if you’re looking to buy software for your company to do data science-related tasks, it’s difficult to know which applications will actually suit your needs. Do you already have a data workflow you’d like to build on, or are you looking to the structure of an end-to-end platform to set your data science initiative up for success? How do you coordinate a team of data scientists to take better advantages of existing resources they’ve already created? Do you have coders in-house already who can work with a platform designed for people writing in Python, R, Scala, Julia? Are there more user-friendly tools out there your company can use if you don’t? What do you do if some of your data requires tighter security protocols around it? Or if some of your data models themselves are proprietary and/or confidential?

All of these questions are part and parcel of the big one: How can companies tell what makes a good data science platform for their needs before investing time and money? Are traditional enterprise software vendors like IBM, Microsoft, SAP, SAS dependable in this space? What about companies like Alteryx, H2O.ai, KNIME, RapidMiner? Other popular platforms under consideration should also include Anaconda, Angoss (recently acquired by Datawatch), Domino, Databricks, Dataiku, MapR, Mathworks, Teradata, TIBCO. And then there’s new startups like Sentenai, focused on streaming sensor data, and slightly more established companies like Cloudera looking to expand from their existing offerings.

Over the next several months, I’ll be digging deeply to answer these questions, speaking with vendors, users, and investors in the data science market. I would love to speak with you, and I look forward to continuing this discussion. And if you’ll be at Alteryx Inspire in June, I’ll see you there.

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Cloudera Analyst Conference Makes The Case for Analytic & AI Insights at Scale

On April 9th and 10th, Amalgam Insights attended the fifth Cloudera’s Industry Analyst and Influencer Conference (which I’ll self-servingly refer to as the Analyst Conference since I attended as an industry analyst) in Santa Monica. Cloudera sought to make the case that it was evolving beyond the market offerings that it is currently best known for as a Hadoop distribution and commercial data lake in becoming a machine learning and analytics platform. In doing so, Cloudera was extremely self-aware of its need to progress beyond the role of multi-petabyte storage at scale to a machine learning solution.
Cloudera’s Challenges in Enterprise Machine Learning 
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Blockchain! What is it Good For?

Diamond - Immutable and Hardened
Tom Petrocelli, Amalgam Insights Contributing Analyst

Blockchain looks to be one of those up and coming technologies that is constantly being talked about. Many of the largest IT companies – IBM, Microsoft, and Oracle to name few – plus a not-for-profit or two are heavily promoting blockchain. Clearly, there is intense interest, much of it fueled by exotic-sounding cryptocurrencies such as Bitcoin and Ethereum. The big question I get asked – and analysts are supposed to be able to answer the big questions – is “What can I use blockchain for?”

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Data and Analytic Strategies for Developing Ethical IT: a BrightTALK webinar

BI to AI on Trusted Data - An Amalgam Insights Research Theme
BI to AI on Trusted Data – An Amalgam Insights Research Theme

Recommended Audience: CIOs, Enterprise Architects, Data Managers, Analytics Managers, Data Scientists, IT Managers

Vendors Mentioned: Trifacta, Paxata, Datameer, Datawatch, Lavastorm, Alation, Tamr, Unifi, 1010Data, Podium Data, IBM, Domo, Microsoft, Information Builders, Board, Microstrategy, Cloudera, H20.ai, RapidMiner, Domino Data Lab, Dataiku, TIBCO, SAS, Amazon Web Services, Google, DataRobot.

In case you missed it, I just finished up my webinar on Data and Analytic Strategies for Developing Ethical IT. We are headed into a new algorithmic, statistical, and heterogenous data-defined model of IT where IT ethics and relevance are being challenged. In this webinar, we discussed:

  • Why IT is broken from a support and business perspective
  • The aspects of IT that can be fixed
  • What we can do as IT managers to fix IT
  • Data Prep, Data Unification, Business Intelligence, Data Science, and Machine Learning vendors that can help unlock the Black Boxes and Opt-Out disasters in IT
  • Key Recommendations

This webinar provides context to my ongoing research tracks of “BI to AI on Shared Data” and “IT Management at Scale.” To attend the webinar, please check the embedded view below or click to watch on BrightTALK