If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Quickly shifting to remote work has enterprises looking to meet the ops needs of a suddenly distributed team, and there are open source options to get them there. The recent mad rush to scale to remote work may prove to be a key chapter in DevOps and AIOps evolution. This need for rapid, widescale change is creating a real conundrum concerning AIOps, DevOps, and ITSM, as organizations seek the best monitoring and incident response solution for their now distributed enterprises. The key question both the DevOps and IT service management (ITSM) communities need to answer is how quickly they can pivot and adapt to increasing demands for operational intelligence. Artificial intelligence for IT Operations (AIOps) brings together artificial intelligence (AI), analytics, and machine learning (ML) to automate the identification and remediation of IT operations issues.
AI-driven organizations are using data and machine learning to solve their hardest problems and are reaping the rewards. "Companies that fully absorb AI in their value-producing workflows by 2025 will dominate the 2030 world economy with 120% cash flow growth,"1 according to McKinsey Global Institute. Machine learning (ML) systems have a special capacity for creating technical debt if not managed well. They have all of the maintenance problems of traditional code plus an additional set of ML-specific issues: ML systems have unique hardware and software dependencies, require testing and validation of data as well as code, and as the world changes around us deployed ML models degrade over time. Moreover, ML systems underperform without throwing errors, making identifying and resolving issues especially challenging.
Businesses increasingly solve complex problems with data science. Access to very large data sets, accelerated advances in ML research fields, and inexpensive computing power are driving an AI-fueled transformation across industries. In a crowded market where consumers can have anything at any time, ML/AI applications that prevent fraud, mitigate churn, serve product suggestions in real-time, and manage predictive maintenance on infrastructure can be the critical differentiator. Yet as AI/ML projects come into the mainstream, businesses are finding just how hard it is to go from data science to business value. It's dangerous for any company to think of these AI-driven wins as coming for free.
DevOps and Machine Learning share a powerful alliance with related capabilities like predictive analysis, algorithmic IT operations, Operations Analytics, and AI. Introduction of Machine Learning into DevOps has brought benefits such as checking highly complex data sets. Detect patterns and antipatterns, uncover new ideas, repeat and refine queries with the speed and perfection. For example, delivery processes can be tracked with various DevOps tools, these tools produce a lot of data and any kind of error in this data can be detected by the application of Machine Learning. Large code volume, slow-release rate, and long built times are some of the issues that can be put in check using Machine Learning.
Managing and monitoring a DevOps environment requires a high degree of complexity. The absolute magnitude of data in today's distributed and dynamic application environments has made it challenging for DevOps teams to grasp and apply information to address consumer concerns efficiently. Though AI is accelerating the process of DevOps currently. Imagine a team navigating for information to find significant occurrences that triggered an event they would end up consuming hundreds of hours just attempting to identify the issue. The future of DevOps will be artificial intelligence-driven.
Looking to reduce the delays DevOps teams are challenged with, software development tool providers are accelerating the pace of integrating AI- and Machine Learning technologies into their apps and platforms. Accelerating every phase of the Software Development Lifecycle (SDLC) while increasing software quality is the goal. And the good news is use cases are showing those goals are being accomplished, taking DevOps to a new level of accuracy, quality, and reliability. What's particularly fascinating about the ten ways AI is accelerating DevOps is how effective it is proving to be in assisting developers with the difficult, time-consuming tasks that take away from coding. One of the most time-consuming tasks is managing the many iterations and versions of requirements documents.
DevOps is a methodology aimed at unifying software development and operations to boost a company's ability to deliver applications at high velocity. It's a trendsetting software development approach, but its success is down to its proven ability to breed efficiency, and it brings many quantifiable benefits to enterprises, including shorter development cycles, faster time to market, a higher rate of deployment frequency, and more reliable products. Global Market Insights found that the market valuation of DevOps will reach US$17 billion by 2026, as more and more businesses pile in. Red Hat's chief agilist, Jen Krieger, previously told TechHQ that as all businesses become tech companies, all should be considering embracing this approach to development. DevOps is about the correlation of people, processes, and products to enable continuous delivery of value to end-users, and while it adds automation and consistency to operations, there is still the need for manual, repeatable processes -- and that means there's space for artificial intelligence technologies to enhance that efficiency further, enabling the people to take on more targeted, innovative work.
From startups to enterprises racing to get new products launched, AI and machine learning (ML) are making solid contributions to accelerating new product development. There are 15,400 job positions for DevOps and product development engineers with AI and machine learning today on Indeed, LinkedIn and Monster combined. Capgemini predicts the size of the connected products market will range between $519B to $685B this year with AI and ML-enabled services revenue models becoming commonplace. Rapid advances in AI-based apps, products and services will also force the consolidation of the IoT platform market. The IoT platform providers concentrating on business challenges in vertical markets stand the best chance of surviving the coming IoT platform shakeout.
The use of traditional machine learning methods to solve real-world business problems is time-consuming, resource-intensive, and challenging. Automated machine learning is an incremental shift in the way organizations approach machine learning and data science. The core objective is to make machine learning accessible and easy by generating a data analysis pipeline that includes pre-processing of data, a selection of features, and engineering methodologies along with machine learning methods and parameter settings that are optimized for your data sets. Just imagine an infrastructure that delivers secure, seamless and flexible support that your workloads require. Yes, with managed compute, you can experience a scalable infrastructure that balances on-and off-premise, enabling you to run at peak performance.
Python is the clear winning programming language in data science & machine learning (DSML). With its rich and dynamic open-source software ecosystem, Python stands unmatched in how adaptable, reliable, and functional it is. If you disagree with this premise, then please take a quick detour here. Python has over 8 million users (SlashData) (Image Credit: HackerNoon). Your goal as a lead of a DSML team is to deliver the best return on investment to the business.