We live in an era where everything that we see and work with, has a plausible involvement of Artificial Intelligence (AI) and Machine Learning. There are over 15000 different AI technology providers that are enabling enterprises and customers to make the most efficient use of their time and resources. While we hear so much noise in the broader AI, the world of Operating Systems (OS) seems to be keeping a low profile all this while.
In our newly launched AI Technology RADAR, we will cover some of the leading Ops for AI ML and Big Data platforms that are working like Genie to make AI and ML ubiquitous in everybody’s lives.
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In PART 1 of AI Technology RADAR, we are focusing on the top OS for AI and ML for IT. We will cover their Digital Marketing campaigns, AI Tech reviews, and the impact they foresee in the coming months.
But first —
What is AIOps?
AIOps is an IT specialization in an application-based environment where a team of data scientists, analysts, developers, and programmers work with various IT tools. The aim is to improve IT functions using OS for AI, Machine Learning, Big Data analytics, Data Visualization, and Data Privacy. These would help to cover a wide array of Management, Monitoring, Surveillance, Security and Automation platforms. AIOps have been in the industry for the last two decades but only made to stand out in the last 5 years.
The most reasonable AIOps definition we found online is –
“AIOps is the practice of applying Analytics and Machine Learning to Big data to automate and improve IT operations. AI can automatically analyze massive amounts of network and machine data to find patterns, both to identify the cause of existing problems and to predict and prevent future ones.”
This is courtesy, Splunk, a leader in AIOps.
According to a verified industry source, and a MarTech RADAR prospect, AIOps “play a critical role in eliminating the manual component of identifying issues within the IT landscape, a problem that’s compounded by the still siloed nature of the monitoring environment.”
In this article, we will start with some of the top AIOps developers and providers in the market.
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Let’s start…
#1 IBM AIOps Platform- OpenScale
IBM’s OpenScale powered by Watson AI is a powerful AIOps platform for enterprise and production AI. At IBM, AIOps analysts consider OpenScale as a readymade solution to solve the “black box” phenomenon of data management and analytics.
In its current platform, IBM Watson OpenScale integrates seamlessly with IBM tools for building and running AI models. These include IBM Watson® Studio and IBM Watson Machine Learning. IBM also provides an open development environment for AIOps teams working with TensorFlow, Keras, SparkML, Seldon, AWS SageMaker, AzureML and more.
#2 Google AI
Google AI team is one of the most prolific and scientifically advanced AIOps research and development teams. Not only are they leveraging AI for enterprise IT customers via Google Cloud products, but also providing much needed AI-push to the philanthropical and environmental causes. Google’s TensorFlow is for everyone who wants to think beyond the usual rut of coding and programming. Popular with Python and DevOps teams already working on Mobile applications, Google TensorFlow allows enough room to build a proprietary Machine Learning library.
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For those who want to make a career in Cloud AI ML, or want to start an AIOps company with Google, don’t miss out on checking the various TensorFlow features.
#3 MoogSoft
Moogsoft AIOps is uniquely positioned in this AI RADAR of OS providers. Today. MoogSoft helps to streamline legacy processes and “maximize your IBM Netcool investment.” Algorithmic Clustering Engine (ACE) provides unique capabilities for noise reduction, real-time algorithmic alert clustering, and modern social collaboration technology. Moogsoft AIOps can also provide immediate relief to the stress that can come with being a Netcool administrator.
Using the power of automated anomaly detection, Moogsoft AIOps makes IT real-time and correlate events and alerts from across your application, network, and infrastructure into actionable situations.
#4 Splunk
AI ML would be the new foundation of every IT function. In an era where we are focused on customized services, how could AI ML Ops be left far behind? That’s where Splunk drives in with its diverse AL ML capabilities for IT Ops.
Splunk for AIOps is focused on reducing the ‘data chaos’ that we are familiar with in this space. Splunk Ops for AI platform provides a unique plain to customers who find it hard to manage data and embrace analytics for intelligent decision-making. It’s time to break the traditional CI/CD toolchain – and, AI can help achieve the next phase of coding.
AIOps at Splunk is focused on the 3C’s of IT. These are:
- Continuous Monitoring
- Continuous Service
- Continuous Automation
All three Cs flow in a cycle or perennial fashion.
#5 AppDynamics
While reviewing the AI Ops ecosystem, we had to filter tons of content on AI ML and their role in transforming the IT Operations. That’s when we came across a blog from AppDynamics and their product, Central Nervous System. The company has a technical collaboration with another Tech RADAR company, Cisco. Together with Cisco, AppDynamics provides AIOps package in complex multi-cloud environments, optimized for flawless workflow performance and user experience.
We were particularly excited about AppDynamics Cognition Engine. Cognition Engine uses sophisticated ML Algorithms to automate anomaly detection, drastically reduce MTTR with instant root cause diagnostics, and correlate software and business performance metrics so IT teams can swiftly diagnose application performance problems.
# 6 Micro Focus
Micro Focus is an IT Operations Management company that has diversified into an Analytics and Big Data and collaboration solutions provider in recent times. COBOL is where we can find Micro Focus beating the competition hands-down. COBOL Analyzer provides well-reported analysis, intelligence, and visualization tools designed for Micro Focus COBOL applications. AI developers, analysts, and DevOp supervisors could achieve a deeper understanding of the IT application portfolio by using Micro Focus’ secure, centralized repository.
Their Automated AIOps for the digital enterprise platform senses, analyzes and adapts to various levels of data management for better business transformation.
#7 Baidu DuerOS
Baidu DuerOS for AI IT processes was launched in February 2017. Baidu brands DuerOS as an independent team built from the original Duer Team. Currently, the Duer BU team is fully responsible for the technology and product innovation of DuerOS – working extensively across the AI ecosystem in the US and China. As a conversational AI Ops platform, DuerOS offers a versatile computing platform that can be integrated with most connected devices, toys, and computer systems. This allows OEMs to reduce production costs and yet deliver on the AI promises to the new-age customers who want technology at the tip of their fingertips.
The website says,
“DuerOS synthesizes the best of Baidu technologies — speech recognition, image recognition, natural language processing, user profile, and other advanced technical skills — to create one of the most advanced conversational computing platforms available today.”
# 8 HPE
Hewlett Packard Enterprise is driving AI-trained IT operations to simplify architectures, enabling IT teams to better manage and support various functions in a multi-Cloud framework. HPE InfoSight is an AIOps platform for Hybrid Cloud that analyzes and correlates with billions of sensors and connected devices deployed all over the systems. Currently, HPE InfoSight runs with HPE SimpliVity HCI offerings. InfoSight AI operations provide customers with global visibility into detailed system, performance and capacity utilization, powered by predictive data analytics and recommendations for real-time performance optimization.
#9 BMC Software
In 2017, BMC Software strengthened its AIOps platform, TrueSight. TrueSight added ML capabilities for multi-Cloud management, empowering digital enterprises to prepare for the next hyper-growth phase in the Industrial 5.0. We reviewed BMC’s Automated Mainframe Intelligence (AMI) for AI RADAR and found out some very interesting features that AIOps teams can learn.
For example, BMC AMI connects the mainframe data and information to other Big Data engines and dashboards, enabling IT companies to manage end-to-end applications in real-time. It is constantly ingesting billions of data points and analyzing them for Predictive Intelligence using the most advanced AI ML and Big Data Analytics capabilities.
#10 StackState AIOps
Earlier this year, StackState caught our eyes when they announced few industry-shaking updates for IT OS and DevOps. StackState decided to connect their AIOps with VMware vSphere and Google Analytics. VMware vSphere (previously VMware Infrastructure) is a top-end Private Cloud for many large sized IT companies. By adding Google Analytics to StackState’s AIOps platform, OS teams can track website telemetries such as page views, unique visitors and online transactions per minute. This integration enables I&O leaders to get more control on critical business processes and to create rapid feedback loops from Business teams to DevOps teams.
Bonus Entry: BigPanda.io
We included BigPanda in this AIOps RADAR by reviewing their Open Box ML capability. It has a unique open integration hub and an autonomous anomaly detection layer called “LØ”.
We would continue to hunt for the top AIOps providers and enablers in the industry, look out for our AI RADAR 2019 in the coming weeks.
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