As you know, I joined Amalgam Insights in September. Amalgam Insights, or AI, is a full-service market analyst firm. I’d welcome the opportunity to learn more about what 2018 holds for you. Perhaps we can schedule a quick call in the next couple of weeks. Let me know what works best for you. As always, if I can provide any additional information about AI, I’d be happy to do so!
On November 29th, Amazon Web Services announced SageMaker, a managed machine language service that manages the authoring, model training, and hosting of algorithms and frameworks. These capabilities can be used by themselves, or as an end-to-end production pipeline.
SageMaker is currently available with a Free tier providing 250 hours of t2.medium notebook usage, 50 hours of m4.xlarge training usage, and 125 hours of m4.xlarge hosting usage for hosting for two months. After two months or for additional hours, the service is billed per instance, storage GB, and data transfer GB.
Amalgam Insights anticipates watching the adoption of SageMaker as it solves several basic problems in machine learning.
On November 15, 2017, TIBCO announced the acquisition of Alpine Data, a data science platform long known for its goals of democratizing data science and simplifying access to data, analytic workflows, parallel compute, and tools.
With this acquisition, TIBCO makes its second major foray into the machine learning space after June 5th acquisition of Statistica. In doing so, TIBCO has significantly upgraded its machine learning support capabilities, which will be especially useful to TIBCO in continuing to position itself as a full-range data and analytics solution.
When this acquisition occurred, Amalgam received questions on how Alpine Data and Statistica would be expected to work together and how Alpine Data would fit into TIBCO’s existing machine learning and analytics portfolio. Amalgam has provided favorable recommendations for both Alpine Data and Statistica in 2017 and plans to continue providing a positive recommendation for both solutions, but sought to explore the nuances of these recommendations.
In our Market Milestone, we explore why Alpine Data was a lower-ranked machine learning solution in analyst landscapes despite being early-to-market in providing strong collaborative capabilities and supporting a wide variety of data sources. We also wanted to explore the extent to which Alpine Data provided some sort of conflict to existing TIBCO customers. Finally, we also wanted to provide guidance on how TIBCO’s acquisition would potentially change Alpine Data’s positioning and capabilities.
To read Amalgam Insights’ view and recommendations regarding this report, use the following link to acquire this report.
Key Takeaway: Amalgam believes that the go-live date of myEinstein will be the most important date for Enterprise AI in 2018 as it represents the day that AI will become practical and available to a broad business audience across industries, verticals, company sizes, and geographies.
On November 6, 2017, Salesforce [NYSE:CRM] announced the launch of myEinstein: services based on Salesforce’s Einstein machine learning platform to support point-and-click-based and codeless AI app development. This announcement was one of several new services that Salesforce built across platform (mySalesforce and myIoT), training (myTrailhead), and user interface development (myLightning).
As the fall season of tech conferences starts to wind down, something is quite clear – machine learning (ML) is going to be everywhere. Box is putting ML in content management, Microsoft in office and CRM, and Oracle is infusing it into, well, everything. While not a great leap forward on the order of the Internet, smartphones, or PCs, the inclusion of ML technology into so many applications will make a lot of mundane tasks easier. This trend promises to be a boon for developers. The strength of machining learning is finding and exploiting patterns and anomalies. What could be more useful to developers?
Last week, I attended Strata Data Conference at the Javitz Center in New York City to catch up with a wide variety of data science and machine learning users, enablers, and thought leaders. In the process, I had the opportunity to listen to some fantastic keynotes and to chat with 30+ companies looking for solutions, 30+ vendors presenting at the show, and attend with a number of luminary industry analysts and thought leaders including Ovum’s Tony Baer, EMA’s John Myers, Aberdeen Group’s Mike Lock, and Hurwitz & Associates’ Judith Hurwitz.
From this whirwind tour of executives, I took a lot of takeaways from the keynotes and vendors that I can share and from end users that I unfortunately have to keep confidential. To give you an idea of what an industry analyst notes, following are a short summary of takeaways I took from the keynotes and from each vendor that I spoke to:
Keynotes: The key themes that really got my attention is the idea that AI requires ethics, brought up by Joanna Bryson, and that all data is biased, which danah boyd discussed. This idea that data and machine learning have their own weaknesses that require human intervention, training, and guidance is incredibly important. Over the past decade, technologists have put their trust in Big Data and the idea that data will provide answers, only to find that a naive and “unbiased” analysis of data has its own biases. Context and human perspective are inherent to translating data into value: this does not change just because our analytic and data training tools are increasingly nuanced and intelligent in nature.
Behind the hype of data science, Big Data, analytic modeling, robotic process automation, DevOps, DataOps, and artifical intelligence is this fundamental need to understand that data, algorithms, and technology all have inherent biases as the following tweet shows: Continue reading “28 Hours as an Industry Analyst at Strata Data”
When I represented Amalgam Insights at Inforum, I was wondering if I would be a fish out of water. After all, I am not an ERP analyst. I am not a retail analyst. I am not an HR technology analyst. And those are the first three things I think of when Infor comes to mind. As an analyst who focuses on technology consumption and bridging gaps between the CIO and CFO, I was wondering what would grab my attention other than Infor’s acquisition of Birst.
Based on Amalgam Insights’ discussions with over two dozen enterprise application solutions over the past six weeks, we believe that a new generation of applications is starting to emerge. Machine Learning has led to the evolution of a new generation of platforms that are transforming expert productivity by providing insights through self-guided logic that improves over time.
Amalgam Insights calls these solutions Role-Based Expert Enhancement Platforms (REEP), an emerging technology made possible by the increasing use of machine learning and artificial intelligence in the business world. We believe that, in the short-term, the emerging value of machine learning will come not from generalized platforms, but from these role-based solutions that will greatly simplify finance, compliance, and sales enablement tasks. As companies start thinking about the role of machine learning in enhancing their organizations, Amalgam recommends that they consider these four key areas of benefit: Continue reading “Machine Learning and the Rise of the REEP: Role-Based Expert Enhancement Platforms”
Amalgam Insights’ Net-Net Summary of Mindbridge Ai
Based on a briefing with MindBridge Ai on June 19, 2017 and MindBridge Ai’s $4.3 million funding round on the same date, this summary provides guidance to auditors and accountants seeking guidance on applying artificial intelligence to risk, compliance, and audit efforts. Amalgam Insights believes that this emerging approach is an important governance step for financial transactions as digital transactions become increasingly diverse and auditors are responding by either building self-built tools or working with internal data science teams.
Amalgam Insights (AI) recently attended IBM Interconnect under the Social Influencer program with the goal of understanding how IBM is planning to position itself in context of technology market changes, investor demands to increase revenue, and the challenges of embracing innovation as one of the largest enterprises on the planet.
In observing IBM over the past few years, AI investigators have noted in the past that IBM faces the challenge of needing to create billion-dollar businesses just to maintain existing revenue. It is not enough for IBM to create a single startup such as Pivotal or Airwatch that ends up becoming a market leader in analytic application development or enterprise mobility. To drive 80 billion+ dollars in annual revenue, IBM needs to grow enough businesses to maintain pace while simultaneously divesting cash cows and declining margin businesses that are not strategic to future growth. Over the past couple of years, this has meant selling off assets such as Salary.com and semiconductor chip manufacturing (and possibly its mainframe division) while investing deeply into systems and capabilities that will drive upcoming business capabilities.
At Interconnect, IBM provided its vision for upcoming success focused on three areas: IBM Cloud, Cognitive computing services highlighted by Watson, and the promise of Blockchain.
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