1.) DevOps.com explores how AIOps is helping businesses overcome today’s IT challenges.
By leveraging AIOps-powered solutions in use cases like observability, overworked and understaffed technology teams are improving their ability to manage complicated, hybrid and multi-cloud IT environments, automate processes and optimize their businesses. As companies prepare for a potential economic downturn and a reduction in resources, these solutions and AIOps are more important than ever. Here are four ways that AIOps is helping businesses overcome today’s IT challenges:
After implementing AIOps, organizations can streamline workflows by automating repetitive and routine tasks. Doing so can cut down on the amount of overwhelming metrics technology professionals are required to analyze and, instead, empower those individuals and teams to again focus on innovating and completing projects that support key business objectives.
2.) CIO.com offers expert advice for CIO’s looking to accelerate digital transformation.
The pandemic-era push to quickly boost digital touchpoints and services proved that transformation could happen fast. And even as the pandemic recedes, enterprise executives continue to expect CIOs and their IT departments to deliver transformative capabilities at a rapid-fire pace. A study from Gartner found that many CEOs (59%) say digital initiatives take too long and 52% take too long to realize value.
The pressure is on to accelerate digital transformation, according to CIOs, researchers, and analysts. They say growing concerns about economic slowdowns and a possible recession only ratchet up the need for speed. Veteran IT executives and executive advisors offer the following 10 strategies that CIOs can employ to increase the velocity of IT work and the delivery of transformative initiatives:
1.) Shed the legacy mindset;
2.) Go all-in with modern work processes — particularly agile;
3.) Create reusable tools and repeatable processes;
4.) Educate your IT team on key business drivers;
5.) Make your business more digitally literate;
6.) Make IT training a priority;
7.) Invest in modular architecture;
8.) Speed up access to data;
9.) Deal directly with customers; and
10.) Align IT’s pace to each business unit.
3.) Dzone explains why tech leaders are increasingly interested in MLOps and AIOps.
Monitoring and managing a DevOps environment is complex. The volume of data generated by new distributed architectures (such as Kubernetes) makes it difficult for DevOps teams to effectively respond to customer requests. The future of DevOps must therefore be based on intelligent management systems. Since humans are not equipped to handle the massive volumes of data and computing in daily operations, artificial intelligence (AI) will become the critical tool for computing, analyzing, and transforming how teams develop, deliver, deploy, and manage applications.
Machine learning operations (MLOps) refers to the lifecycle management of machine learning (ML) projects. It is a key concept of modern machine learning application development, and its purpose is to make the training, deploying, and maintaining of machine learning applications seamless and efficient. With the application of MLOps principles, data scientists can focus on the core development of machine learning models while the MLOps practices take care of tasks such as data cleaning, quality control, and model versioning.
Organizations throughout the world are increasingly looking to automation technologies as a means of improving operational efficiency. This indicates that tech leaders are becoming more and more interested in MLOps and AIOps. AIOps can play a crucial role in accelerating DevOps efficiency. It is defined as the usage of big data and machine learning to automate operations such as event correlation, determining cause and effect, and identifying unusual events.
4.) Data Science Salon delves into AIOps and how it can revolutionize IT operations.
Many enterprises are either on the path of becoming data-first or already leveraging their data assets. Such a digital journey gets fueled with data – an enormous amount of it, that is generated daily to empower such data-powered insights.
But maintaining data assets is not a trivial task. Infrastructure teams frequently face incidents that disrupt the data pipelines – which are well captured in logs that require an additional layer of analysis. This can also be combined with data from other streams, such as server observational analytics and third-party datasets, providing deeper insights into specific technologies and methodologies. These large datasets possess enormous amounts of valuable information that is yet untapped but is crucial in improving incident management strategies.
The excess of everything is bad, and that applies to data as well. But recognizing and mitigating some risks helps organizations get better results from their data. Some of the challenges encountered while working with big data are listed below:
- Data volume;
- Data variety;
- Data velocity;
- Data veracity;
- Data governance; and
- Data processing.
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