AI/ML Tools Investigation

Overview

Klaudia's AI/ML Tools enable intelligent investigation and troubleshooting of machine learning and data processing workloads running on Kubernetes. These tools provide deep visibility into complex AI/ML pipelines, helping you identify issues with batch processing jobs, workflow orchestration, and distributed computing frameworks that power your data science and machine learning operations.

AI/ML workloads on Kubernetes present unique challenges - from resource contention and scheduling issues to failed job stages and driver/executor problems. Klaudia's specialized AI/ML investigation capabilities help bridge the gap between Kubernetes infrastructure expertise and data engineering workflows.

Supported Tools

What Klaudia Can Do

General Capabilities

• Workload State Analysis: Understand the current state of AI/ML jobs across your clusters
• Resource Investigation: Identify resource bottlenecks affecting job performance
• Failure Root Cause Analysis: Pinpoint why jobs failed with detailed evidence
• Configuration Validation: Detect misconfigurations in AI/ML workload specifications
• Dependency Tracking: Trace issues across interconnected workflow stages

Apache Flink

• Investigate JobManager and TaskManager pod failures
• Analyze checkpoint failures and state backend issues
• Identify backpressure and throughput bottlenecks
• Troubleshoot Flink cluster scaling problems
• Examine failed job submissions and configuration errors

Apache Airflow

• Investigate DAG execution failures and task retries
• Analyze KubernetesPodOperator task failures
• Identify scheduler and worker pod issues
• Troubleshoot database connection and metadata problems
• Examine resource constraints affecting task execution

Apache Spark

• Investigate Spark driver and executor failures
• Analyze out-of-memory errors and resource allocation issues
• Troubleshoot stage failures and task retries
• Examine executor pod scheduling and termination issues

When Klaudia Uses AI/ML Tools - Usage Examples

Klaudia automatically engages AI/ML investigation tools in the following scenarios:

Root Cause Analysis (RCA)

When an unhealthy state is detected in your AI/ML workloads, Klaudia initiates an investigation to determine the root cause:

• Flink job enters FAILING or FAILED state
• Airflow DAG run fails or tasks enter retry/failed state
• Spark application fails or shows executor losses

Troubleshooting Unhealthy Resources

When you have unhealthy pods or deployments related to AI/ML frameworks:

• CrashLoopBackOff on Flink TaskManager pods
• OOMKilled Spark executors
• Failed Airflow worker pods

Chat Sessions

When you ask Klaudia questions about your AI/ML workloads:

• "Why did my Spark job fail last night?"
• "What's causing my Airflow DAG to be stuck?"
• "Why are my Flink checkpoints failing?"