Machine Learning and Artificial Intelligence in the Cloud

Monalisa Bandyopadhaya
Machine Learning and Artificial Intelligence in the Cloud

DevOps is a set of practices that automates the processes between software development and IT teams, so they can build, test and release software faster and more reliably. The concept of DevOps is founded on building a culture of collaboration between teams that have historically functioned in relative siloes. The promised benefits include increased trust, faster software releases, and the ability to solve critical issues quickly and better manage unplanned work.

DevOps methodologies are increasingly generating large and diverse data sets across the entire application lifecycle – from development, to deployment, to application performance management. Only a robust monitoring and analysis layer can truly harness this data for the ultimate DevOps goal of end-to-end automation.

The recent rise of artificial intelligence (AI)/machine learning (ML) in the cloud – and related capabilities such as predictive analytics – has started to push DevOps organizations to explore the implementation of a new data analysis model that relies on mathematical algorithms. Many public cloud providers, such as Microsoft, Amazon, Google and IBM are currently supporting AI/ML-as-a-Service, enabling DevOps organizations to leverage the agility of those services to help them become more efficient in their application development, including automating many repeatable DevOps tasks.

Automation is the fuel that drives DevOps

Automating routine, repeatable tasks is one of the defining characteristics of DevOps culture. As AI and ML capabilities improve, the scope and complexity of the tasks that can be automated increases, which raises the bar for all DevOps. The humans behind the DevOps can be freed from even more mundane tasks and focus on innovative and creative endeavors. Automating routine tasks is crucial, but there’s another factor that plays into the role that AI and ML have with the future of DevOps: the reality that humans simply can’t do some things as well or as fast as machines – especially at scale.

How Equinix is using AI/ML adoption in DevOps for enterprise applications

In Equinix’s DevOps organization, we leverage AI/ML-as-a-Service to perform the following with greater automation:

Root Cause Analysis & Recommended Action

  • Determines correlation and causality between different alerts
  • Enables grouping together related issues and telling apart root cause from symptoms
  • Improves the overall time-to-resolution

Log Analysis

  • Employs machine learning and big data to comb through logs to find patterns
  • Detects server failures, performance issues, etc.

Predicting Server and Application Failure

  • Employs past (training) data to predict future server failure
  • Uses application failure logs to build a model to accurately predict future failures and identify problems and trends in applications that need fixing

Forecasting Infrastructure Needs

  • Employs past (training) data to predict operational patterns, anticipate capacity needs, raise alerts about security anomalies, and self-monitor and self-heal your environment in the face of failures

Our Equinix Cloud Exchange™ (ECX) Fabric facilitates proximate interconnection around the world, including out at the digital edge, where commerce, population centers and a growing number of business ecosystems, IoT devices, and cloud and network providers meet. The ECX Fabric allows private connection between businesses inside Equinix International Business Exchange™ (IBX®) data centers in North America and EMEA, and, eventually, every Equinix IBX data center in the world. For example, in the oil and gas industry, we are helping energy companies and their cloud partners use AI, ML and the cloud to remotely monitor IoT sensors on distributed oil wells to diagnose potential safety issues.

In the future, we expect that the AI/ML in DevOps trend will continue in all industries as more companies collaborate and leverage integrated interconnection hubs and colocation data centers as a unifying, worldwide platform for innovation. Equinix and its Global Solutions Architects support the development of current- and future-state AI/ML and data procurement on Platform Equinix and are helping today’s digital businesses deploy more AI/ML capabilities as a cloud service.

Learn more about our interconnection platform by reading the Platform Equinix Vision paper.



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