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intelligent platforms

Briefing Notes: Insight Engines Takes AI To Enterprise IT

Krishnan Subramanian · September 27, 2017 · 1 Comment

Insight Engines, the San Francisco based startup focused on making machine data actionable, announced the general availability of Cyber Security Investigator and, also, showcased how Amazon Alexa can be tapped to query from Cyber Security Investigator. In this note, we will do an analysis on this announcement.

Market Overview

AI in enterprise is relatively new. Even though enterprises are slowly embracing machine learning and other AI models to dig deeper into their customer data and make business decisions, there is very little progress in using AI to take advantage of machine data. There are plenty of analytics solutions that helps Operations teams optimize their decision making process. But the market is still in infancy when it comes to using ML to automagically do operations or use AI technologies like NLP to develop a better user experience for the machine data. Imagine how DevOps can be done more optimally if developers or even other stakeholders like business users can take advantage of NLP to interact directly with machine data. These are just beginning and we can’t imagine what AI can do to autonomic computing with our current understanding of the landscape.

SWOT Analysis

Strengths

  • Powerful NLP engine to handle machine data, add context and give customization options to users
  • Starting with Splunk data (with an investment from Splunk, of course) gives them an easy on ramp to enterprise IT
  • Their initial focus on Security will help them get the attention of IT decision makers
  • Autopilot feature that provides a more pro-active approach to security might serve as model for future autonomic computing platforms

Weakness

  • They are a startup pushing a newer technology in the enterprise market. The barrier to entry for startups in enterprise is high. However, their partnership with Splunk should help them
  • They provide on-premise deployments which makes it difficult for the learning engine to continuously improve. But they are tapping into the anonymized meta data to help the learning engines learn from user behaviors. This will work well with modern enterprises but they may have trouble convincing enterprises in highly regulated verticals. It is not a weakness for Insight Engines alone but for any company trying to build AI systems that can be deployed on-premises. To overcome this, Insight Engines, as a pioneer in this space, has to convince the customers to share their metadata with them. It is a potential weakness but also an opportunity for them to emerge as thought leaders

Opportunities

  • Insight Engines has the first mover advantage and they are attacking low hanging fruits (machine data and security) with a more powerful NLP engine. They can easily broaden their product portfolio going forward
  • As we embrace modern stacks with deeper and deeper levels of automation, ML and AI are going to be the next wave of innovation. The early innovations in this kind of autonomic IT will come around user interface and user experience. Insight Engines is well positioned to take advantage of the trend

Threats

  • Many established players collect lot of machine data (including Splunk) and it is a logical next step for them to attack the low hanging fruits like NLP for UI/UX. Though it is a threat, it is also an opportunity
  • Open source NLP engines can come and disrupt the market. OSS need not come from the traditional IT companies but also from end customers who develop ML and AI engines for their internal use. There is also an opportunity for Insight Engines to lead here but OSS by startups is not easy.

Conclusion

Insight Engines is an interesting startup in the up and coming field of AI in enterprise IT. It is too early in the market but offers potential opportunities for enterprises to do IT more optimally and optimize the use of human power in house by involving more stakeholders and by reducing the learning curve.

Briefing Notes: Cloudsoft AMP

Krishnan Subramanian · April 9, 2013 · 1 Comment

This is a briefing note prepared by me on Cloudsoft AMP, a DevOps platform services player offering autonomic management of applications.

Overview:

Cloudsoft AMP is an enterprise application management platform that helps automate the process based on business and performance needs. Deploying an application on any cloud or PaaS is just one part of the application lifecycle management. There are many other aspects of the lifecycle that are equally important. For example, management, monitoring, governance, portability, etc. play a critical role in enterprise IT. Cloudsoft AMP adds a layer of abstraction to platforms/platform services making autonomic management of applications much easier in enterprise IT environments.

Read the report below

Disclosure: Cloudsoft Corporation is a client and sponsor of Deploycon
To download the briefing note, you need to sign up as a free subscriber. Check out this page for signing up as a free subscriber. Once you sign up for your account and log in, you will see a download link to the briefing note.

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Download Link: https://rishidotver3.wpengine.com/?s2member_file_download=RishidotResearchBriefingNotesCloudsoftAMP.pdf
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PaaS Pivot: Big Data At The Core Of Platform Services

Krishnan Subramanian · January 17, 2013 · Leave a Comment

As we go into 2013, I keep thinking about the evolution of the Platform as a Service and wonder what is in store for this segment this year. As Platform Services are one of my core focus areas of research, I thought I will start off this year with a post on this topic. For the past year or so, I have been advocating the need to rethink PaaS offerings in order to fully take advantage of the big data paradigm. I use the term Intelligent Platforms to describe next generation platform services built around big data. In my opinion, we are going to see a pivot in the PaaS market where the focus will shift from the application development platforms focussed on scaling users and meeting the resource demands of large loads to building a robust platform to take advantage of vast amounts of data organizations have or going to acquire in the future.

Intelligent Apps Ver 1

Historically, platform as a service offerings were focussed on modern web applications that handle “smaller quantities” of data. In some cases, PaaS was used for applications that handle (or make use of) large volumes of data. But, in my opinion, most of these applications on these platforms were just scratching the surface. In reality, the PaaS offerings were not built to meet the needs of data hungry organizations. In 2012, PaaS vendors understood the changing needs of enterprises and were slowly starting to focus on the big data use cases. At the same time, we also saw the emergence of big data applications which were built on top of big data infrastructure platforms like Hadoop. As enterprises understand the full impact of big data and start building apps to take advantage of data (collected across the length and breadth of these organizations), they will really feel the need for more sophisticated platform services that run on top of big data infrastructure.

Vendors like Continuuity are trying to attack this problem. VMware’s CloudFoundry spinoff seems to be heading in this direction. In 2013, we are going to see the emergence of more such players and we will also see most of the existing PaaS vendor take steps to boost their platform services so that they are capable of supporting big data applications. Keep in mind that whatever we have seen so far with regards to “big data applications” are mostly focussed on analytics and visualization. What we are going to see in the future are set of services built on top of Intelligent Platforms that will go beyond simple analytics. We are going to see applications (services) that are self evolving and which can tweak itself based on the insights gleaned from various data sources (including the data these applications themselves produce). The underlying platform services needed to support such sophisticated services are going to be much more complex underneath than what we are seeing among the PaaS vendors today.

In short, 2013 will be the year when platform vendors are moving towards building platform services suitable for intelligent self evolving applications (services) of the future. All these services are going to be centered around data. Not just business and governments but the entire human society is going to rely on the data driven services with unprecedented complexity and automation underneath. The key for any vendor in the space is to build Intelligent Platforms that mask all these complexities and offer a simple interface for developers.

PS: The image in the blog post was a simplistic diagram I put forward to highlight the evolution from the current generation of PaaS to the next gen Intelligent Platforms.

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