Latest posts by JP Emelie Marcos (see all)
- Monitorama PDX 2017 Recap - May 31, 2017
- Announcing SignifAI Machine Intelligence for TechOps – Now Generally Available! - May 21, 2017
- Learnings from my discussion with Jyoti Bansal, founder of AppDynamics - May 18, 2017
I am excited to announce that the SignifAI machine intelligence platform is now generally available. For those of you learning about SignifAI for the first time today, our mission is to help TechOps teams increase uptime. We manage to accomplish this by leveraging machine intelligence, TechOps expertise, and learning algorithms to provide fast answers and predictive insights to the most critical issues that threaten system availability.
What is “machine intelligence?”
Machine intelligence is a unified term between artificial intelligence and machine learning. Artificial intelligence (AI) is the science of getting machines to do tasks autonomously that begins to resemble the way humans would act. While machine learning is the process by which machines can derive from a data set subsequent computations to make predictions and decisions according to a predetermined logical framework. In SignifAI’s case, we use machine intelligence to augment an existing TechOps team with a virtual SRE who can correlate and apply predictive algorithms on a very large volumes of log, events and metrics data across every component of the entire system, in real-time, to enable fast root cause analysis and provide answers and insights that might otherwise be challenging for a team to accomplish.
How SignifAI works
SignifAI works by first breaking the silos of data across your company: it intelligently integrates with existing monitoring, collaboration and notification tools, which include Splunk, NewRelic, AppDynamics, AWS and Nagios among over 60 integration points, in real-time. It readies for analysis the output from these disparate sources by transforming time series, events and log data into a uniform data set. SignifAI’s machine intelligence platform then applies a series of analysis appropriate for each situation, following a logical framework that is informed and closely resembles that of a human expert. SignifAI then learns from metrics, users’ behaviors, feedback and human experts to continuously adapt and improve its results. Throughout this process, SignifAI also captures the team’s knowledge and makes it automatically available when the content is needed. This allows a TechOps team to get fast access to accurate answers, predictive insights and leverage a growing knowledge base in order to faster address and prevent issues affecting system uptime.
SignifAI is primarily focused on helping TechOps deliver more uptime, but in the process of doing so, we also give teams a lot of time back to work on more complex problems that require creative solutions — precisely the things that machines can’t do — and add a lot more value to the company.
If you’d like to get in-depth technical details on this release, what’s next and a watch a short demo, check out SignifAI CTO, Guy Fighel’s technical blog. For those of you ready to get started with a FREE trial, we’ve made it super simple. After you are up and running, you’ll automatically see to your first predictive insight in less than 20 mins.
A brief history of SignifAI
SignifAI was founded by a team of TechOps and Devops engineers who faced the challenge of delivering uptime at scale for years. We leveraged best in class open source and commercial monitoring, collaboration and knowledge management tools, as well as a range of automation technologies. We benefited from these technologies greatly in terms of visibility and response time, but we still faced many limitations such as
- Being unable to analyze data across tools
- Facing a lot of alert noise
- The time and effort required to perform root cause analysis
- The inability to carve out time to be proactive
- The manual processes required to create and leverage our incident knowledge base.
These limitations were hampering our ability to deliver outstanding uptime performance. Frustrated, by the limitations of available solutions, we decided to investigate ways to get accurate and fast answers to the most important questions that TechOps typically face:
- What is the root cause of what’s affecting my system performance?
- What actions can we take to prevent future problems from happening? Through an investigative (multi-year) process, we came up with the answer…
We look forward to partnering with you.