AIOps - Transform IT operations, Predict incidents and Eliminate Downtime by Chandra Gundlapalli

AIOps definition - AIOps empowers businesses to leverage the power of artificial intelligence and machine learning to transform their IT operations, providing real-time insights and automation capabilities that enhance efficiency, reduce downtime, and ultimately drive business success.

AI operations (AIOps) combine tons of operational data with machine learning to proactively support all primary IT operations. AIOps to grow from $2.8B in '21 to $19B by '28 at a CAGR of 32%, per Insight Partners.

In my latest Forbes article, I shared the AIOps (Artificial Intelligence for IT Operations) business benefits and challenges, here is the article link. Let’s dig deeper into how AIOps can Transform IT Operations, Predict Incidents, and Eliminate Downtime. Most importantly keep the business running without any system outage impacts.

• I have been reading Pupper labs DORA (DevOps Research and Assessment) reports since 2015 and ensured my TEAM followed DevOps best practices starting in 2016 (my executive roles at Thomson Reuters, Charles Schwab, Unisys, and CriticalRiver ($50-$100M revenue).

• I strongly suggest reading the latest report, focusing on the KPIs, here is the link

ITSM (Information Technology Service Management) use cases, every technology executive top responsibility.

• Anomaly Detection Predictive Incident Management (and alerting)

  • K-means and Gaussian Mixture Model (GMM)

• Automated Root Cause Analysis (with built-in anomaly detection)

  • ML Models - Decision trees, Random Forests, Support vector machines (SVM). Keras lib has builtin TensorFlow to help accelerate model development.

• Automated Incident Categorization (remediation)

  • SVM & Naive Bayes

• System Load Forecasting (to adhere to the business SLAs in peak times)

  • Recurrent Neural Network (RNN) & long short-term memory (LSTM) for Time Series Forecasting

• Self-Service Chatbot (to answer common FAQs)

  • SVM, LSTM, and Latent semantic analysis (LSA)

Solution: A lot of organizations are still figuring out how to implement AI into IT Operations, my suggestion is to start leveraging the SaaS platforms already built to accelerate AIOps adoption. Yes, making sure all the sensitive data is encrypted in motion and in rest integrating with SaaS cloud platforms. The ideal option is to build your own AIOps SaaS platform on your current private cloud.

30-second key takeaway: As I mentioned in my recent Forbes article, it is very critical to build the model with real data, and pick the model with high accuracy (no one-size-fits model, depends on organization data and data quality).

For the past couple of years, I am fortunate to partner with CriticalRiver org ITMS domain experts and build AIOps AWS SaaS platform hiring data scientists, MLOps, and machine learning experts, part of this I got my Andrew Ng DeepLearning AI machine learning specialization certificate to guide the TEAM on the ground with data exploration, model selection & training. We are helping global organizations adopt the newly built AIOps as we speak with 20-30% cost savings eliminating manual inefficiencies.

PS: Please check back, I will be updating the above article on the weekend(s). soon, I will write a blog on each Machine Learning model I mentioned above.