Artificial intelligence has become a keen talking point in the IT industry, and for a good reason. The manual configuration and management of IT infrastructure is inefficient and expensive. This goes against the desired outcomes of digital transformation in the cloud, which include zero-downtime, expedited implementation, and effortless server scaling in line with business growth.
AI operations management (AIOps) aims to overcome these bottlenecks, using a mixture of artificial intelligence and machine learning to automate the management of your IT systems. It requires a strategic shift from siloed IT environments to a unified big data and analytics platform, which then fuels the automation of continuous integration and deployment functions (CI/CD) on your network.
AIOps has two main components as defined by Gartner, which are:
Big Data - With AIOps, big data is the vital source of constant information that machine learning and AI uses to predict and implement operational changes on your network. The bigger the dataset your AI has access to, the more effective it will become for managing your IT operations.
Artificial Intelligence and Machine Learning – AI and ML are the cornerstones of AIOps, complimenting your existing service and performance management procedures through intelligent system automation. These two are not meant to replace your existing IT staff. Instead, they are built to complement and augment your existing human intelligence, to raise the bar for IT operations management effectiveness against business objectives.
In simple terms, AIOps aims to bridge the performance gap between your human administrative capacity and digital transformations administrative demands in the cloud.
IT Operations Management Complexity – Modern cloud-native IT environments come with a range of infrastructure types, including the managed/unmanaged cloud, third party service integrations, Software-as-a-Service integrations, and more.
The dynamic nature of the cloud has outpaced traditional IT operations management procedures, making it near-impossible for humans to manage without the assistance of AI/ML. As your IT services become more complex, so do the administrative workloads that are vital for maintaining long-term performance and reliability of these systems.
AIOps aims to analyze and algorithmically manage your IT operations. Instead of your staff manually performing low-level tasks, you can build management frameworks in which the AI can take control. These already exist on popular public cloud platforms in the form of auto-scaling, but you can extend these functions to your niche software packages. As an example, you could automate the ingestion of raw data into databases, categorizing and tagging data in line with your specified parameters.
End-Users and Staff Are Intolerant of Downtime – The retroactive remediation of network issues is no longer acceptable in the modern cloud-native world. End-users will not tolerate service interruptions, and they will start to look elsewhere at the first signs of operational incompetence.
AIOps has the potential to allow your systems to self-heal, creating the illusion of service continuity for end-users despite server outages on your network. In the event of a potentially critical server failure, an AI would be performing continuous integration and delivery (CI/CD) operations in the background. It would see the sudden influx of system errors and start proactively spooling data to a new identical server instance. Within seconds or minutes, you will have a replica of your live server instance that is ready for fail-over if your live server stops functioning altogether. Simultaneously, the AI would operate within your designated management framework, attempting to remediate and avoid the outage in the first place automatically.
Trianz is a leading IT operations management consulting firm, with decades of experience helping our clients to implement meaningful IT operations management strategies in the cloud. We understand the looming potential of AI and machine learning as part of IT operations management, setting you up to be architecturally ready for new developments in this field.
Contact Us Today
For decades, Windows served as the workhorse of the business world. In recent years, however, a significant transformation has occurred with the rise of cloud infrastructure platforms. Enterprises now realize that legacy on-premises Windows workloads are impeding their progress. Core challenges include licensing costs, scalability issues, and reluctance to embrace digital transformation.Explore
Connecting more people to data has become imperative for organizations worldwide. In Top Trends in Data & Analytics for 2022, Gartner stated, “Connections between diverse and distributed data and people create truly impactful insight and innovation. These connections are critical to assisting humans and machines in making quicker, more accurate, trustworthy, and contextualized decisions while considering an increasing number of factors, stakeholders, and data sources.”Explore
Since the dawn of business, users have looked for three main components when it comes to data: Search | Secure| Share. Now let's talk about the evolution of data over the years. It's a story in itself if one pays attention. Back then, applications were created to handle a set of processes/tasks. These processes/tasks, when grouped logically, became a sub-function, a set of sub-functions constituted a function, and a set of functions made up an enterprise. Phase 1 – Data-AwareExplore
Practitioners in the data realm have gone through various acronyms over the years. It all started with "Decision Support Systems" followed by "Data Warehouse", "Data Marts", "Data Lakes", "Data Fabric", and "Data Mesh", amongst storage formats of RDBMS, MPP, Big Data, Blob, Parquet, Iceberg, etc., and data collection, consolidation, and consumption patterns that have evolved with technology.Explore
Enterprises have, over time, invested in a variety of tools, technologies, and methodologies to solve the critical problem of managing enterprise data assets, be it data catalogs, security policies associated with data access, or encryption/decryption of data (in motion and at rest) or identification of PII, PHI, PCI data. As technology has evolved, so have the tools and methodologies to implement the same. However, the issue continues to persist. There are a variety of reasons for the same:Explore
Finding Hidden Patterns and Correlations Innovative technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are transforming the way we approach data analytics. AI, ML and NLP are categorized under the umbrella term of “cognitive analytics,” which is an approach that leverages human-like computer intelligence to identify hidden patterns and correlations in data.Explore