Open Source AIOps on AWS: Top 5 Frameworks You’re Ignoring

We’ve all heard of popular AI frameworks like TensorFlow and PyTorch, but when it comes to self-hosting Artificial Intelligence for IT Operations (AIOps) on Ama

We’ve all heard of popular AI frameworks like TensorFlow and PyTorch, but when it comes to self-hosting Artificial Intelligence for IT Operations (AIOps) on Ama

We’ve all heard of popular AI frameworks like TensorFlow and PyTorch, but when it comes to self-hosting Artificial Intelligence for IT Operations (AIOps) on Amazon Web Services (AWS), some real gems often go unnoticed. AIOps is transforming the way software developers manage IT systems by automating monitoring, predicting incidents, and significantly reducing downtime. 55% of organizations are already using AIOps, and another 19% plan to adopt it within the next year, highlighting its rising importance in IT operations. By self-hosting these solutions on AWS, you gain control, flexibility, and cost savings while leveraging powerful open-source tools that industry leaders trust. 

In this blog, we’ll explore five open-source AIOps frameworks that you might be overlooking. We’ll shine a spotlight on Apache Airflow and Metaflow for orchestrating pipelines, and showcase Feast as a hidden treasure for real-time incident prediction. Join us as we dive into how to self-host AI on AWS and elevate your IT operations to the next level.

What is AIOps?

So, when we talk about artificial intelligence for IT operations, or AIOps, we’re really diving into how AI—think natural language processing and machine learning—can help make IT service management and operational workflows smoother and more efficient.
 

AIOps makes use of big data, analytics, and machine learning capabilities to:

• Gather and combine the vast (and constantly growing) amounts of data created by IT systems, application demands, performance monitoring tools, and service ticketing systems within a company’s tech environment.
• Cut through the clutter to pinpoint key events and patterns that reveal issues with application performance and availability.
• Figure out what’s causing problems and relay that information to IT and DevOps for quick action, or sometimes, resolve these problems all by itself without needing a human touch.

By combining separate manual IT operations tools into a single smart, automated IT operations (ITOps) platform, AIOps allows IT teams to address slowdowns and outages swiftly—and often proactively—while giving them a clear view and context.

This approach helps businesses bridge the gap between the varied, ever-changing, and hard-to-track IT landscapes and disconnected IT teams on one side, and user expectations for app performance and uptime on the other. With digital transformation initiatives popping up across industries, many in the know believe AIOps is where the future of IT operations management is headed.

AIOps components

AIOps can tap into a variety of AI strategies and features, covering everything from data collection and aggregation to algorithms, orchestration, and visualization. Here are the key components:

• Algorithms:
  • Capture IT knowledge, business logic, and goals.
  • Help AIOps platforms focus on relevant security events and performance decisions.
  • Serve as the foundation of machine learning, allowing platforms to set baselines and adjust as they gather new data.

• Machine Learning:
  • Uses methods like supervised, unsupervised, reinforcement, and deep learning.
  • Assists systems in learning from large datasets and adapting to new circumstances.
  • Plays a vital role in identifying anomalies, diagnosing root causes, correlating events, and predicting future trends.

• Data Gathering:
  • Acquires data from diverse network components and sources.
  • Supports the analytics process for improved data interpretation and insight generation.

• Analytics:
  • Transforms raw data into actionable insights and metadata.
  • Helps both systems and teams identify trends, isolate problems, predict capacity demands, and manage events effectively.

• Automation Features:
  • Enable systems to react based on real-time insights.
  • For example, if predictive analytics detects a rise in data traffic, it might initiate an automated process to allocate additional storage space, adhering to established algorithmic rules.

• Data Visualization Tools:
  • Present information via dashboards, reports, and graphics.
  • Allow IT teams to monitor changes and make informed decisions that extend beyond the capabilities of the AIOps software.

What are the Top 10 AIOps Use Cases?

AIOps really stands out in several important areas:

• Spotting Problems: AIOps can swiftly identify issues by recognizing anomalies or deviations from what’s considered normal behavior.
   
• Forecasting Metrics: It predicts important metrics, helping to prevent outages and enhance overall operational readiness.
   
• Alert Grouping: AIOps organizes alerts, events, or logs by similar symptoms or descriptions, making management a lot easier.
   
• Event Correlation: AIOps links related events together so you can make better sense of IT data.  In this way, teams can focus on the actionable insights that really matter.
   
• Health Monitoring: It assesses the health of applications or servers by gathering data from various sensors and telemetry sources.
   
• Speeding Up Root Cause Analysis: AIOps identifies related time series metrics or symptoms to help find the root cause of issues more quickly.
   
• Incident Matching: The system can discover similar incidents, allowing teams to resolve issues faster.
   
• Entity Recognition: Through named entity recognition, AIOps help improve the details of each incident, so it's easier to understand and process.
   
• Incident Assignment Prediction: AIOps can tell which teams can handle specific incidents the best depending on their characteristics, helping to improve response times.
   
• Classification with NLP: AIOps utilizes natural language processing to classify incidents, with the option to integrate with services like IBM Watson NLU or OpenAI's GPT-3.
   

These use cases show how AIOps can boost efficiency and response times in IT operations, leading to smoother, more reliable systems.

Why Self-Host AIOps on AWS?

• Control and Security: By self-hosting AIOps on AWS, you maintain control over your sensitive IT data. This helps you create customized solutions depending on specific needs.
   
• Avoid Vendor Lock-in: With self-hosting, you're not dependent on third-party vendors. This gives you the flexibility to manage your IT operations in a way that works best for you, without restrictions.
   
• Powerful Infrastructure: AWS offers:
  • Compute power through EC2
  • Storage options with S3
  • Scalability with EKS

• Seamless Integration: AWS works well with managed services like CloudWatch for effective monitoring.
   
• Cost-Efficiency: Using open-source frameworks reduces costs and enhances customization, making them great for developers seeking full