Skip to main content

Hackathons

Hackathon Problem Statement: Automated Root Cause Analysis with ML​

Overview​

Organizations often face challenges in quickly identifying and resolving incidents within their IT infrastructure. Manual root cause analysis can be time-consuming and may lead to extended downtimes. The goal of this hackathon is to develop an automated root cause analysis system using machine learning (ML) to accelerate the incident resolution process.

Problem Statement​

Develop a robust solution that leverages historical data and machine learning models to automatically identify the primary causes of incidents within a system. The system should be integrated into incident response workflows, providing actionable insights for faster issue resolution.

Key Components​

Data Collection​

  • Gather historical incident data, including logs, performance metrics, and resolution outcomes.
  • Ensure the dataset represents a diverse range of incidents to train a robust model.

Feature Engineering​

  • Identify relevant features and metrics that contribute to incident analysis.
  • Explore and transform raw data to create meaningful features for model training.

Machine Learning Models​

  • Train machine learning models to predict the primary causes of incidents.
  • Experiment with various algorithms (e.g., decision trees, random forests, or deep learning) to find the most effective model.

Correlation Analysis​

  • Explore correlations between different system metrics and incidents.
  • Develop algorithms that can highlight potential relationships to improve the accuracy of root cause identification.

Integration with Incident Response Workflow​

  • Create APIs or connectors to seamlessly integrate the solution into existing incident response workflows.
  • Ensure the system provides real-time insights during incident investigations.

User Interface​

  • Design a user-friendly interface to visualize the identified root causes and associated insights.
  • Include relevant details, such as severity, affected components, and recommended actions.

Automated Remediation (Good to Have)​

  • Implement a feature that suggests steps to resolve the incident based on historical data.
  • If possible, integrate with scripts or APIs to automate the remediation process in the next step.

Evaluation Criteria​

  • Accuracy of the machine learning models in identifying root causes.
  • Efficiency and speed of the system in providing insights during incident response.
  • User interface design and accessibility for easy interpretation of results.
  • Integration capabilities with existing incident response tools and workflows.
  • Bonus points for the successful implementation of automated remediation steps.

Deliverables​

  • Source code for the automated root cause analysis system.
  • Documentation on model training, system architecture, and integration procedures.
  • Demo showcasing the end-to-end functionality of the solution.

Note​

Teams are encouraged to explore creative solutions and innovations in their approach to addressing the problem. Collaboration and effective communication within the team will be crucial for the successful development and presentation of the solution.

DevOps Themed Problem Statements​

Anomaly Detection in System Logs​

Problem Statement: Develop a machine learning model to detect anomalies in system logs. Utilize open-source tools for log parsing and feature extraction, and implement algorithms such as Isolation Forest or One-Class SVM for anomaly detection. The goal is to identify abnormal patterns indicating potential system issues.

Predictive Scaling for Cloud Resources​

Problem Statement: Build a machine learning model that predicts future resource utilization in a cloud environment. Integrate the model into an autoscaling mechanism to dynamically adjust the number of instances based on predicted demand. Use open-source tools for cloud resource management and ML libraries for model development.

Failure Prediction in CI/CD Pipelines​

Problem Statement: Create a machine learning model to predict failures in CI/CD pipelines. Analyze historical pipeline data, including code changes, build times, and external dependencies, and use algorithms like Random Forest or XGBoost for predictive modeling. The objective is to reduce build failures and improve pipeline reliability.

Automated Root Cause Analysis with ML​

Problem Statement: Develop an automated root cause analysis system using machine learning. Train models to identify the primary causes of incidents by analyzing historical data and correlations between various system metrics. Integrate this solution into incident response workflows for faster issue resolution.

Good to have - Give the steps to perform to fix the problem based on last problem fix, in next step run the scripts or call the api to fix

Optimizing Deployment Strategies with ML​

Problem Statement: Implement a machine learning model to optimize deployment strategies. Analyze past deployment data, including time, day, and code changes, to predict the optimal time for deploying updates with minimal impact. Use open-source tools for deployment orchestration and ML frameworks for model development.

General Problem Statements​

Sentiment Analysis for Social Media Posts​

Problem Statement: Develop a sentiment analysis model to classify social media posts into positive, negative, or neutral sentiments. Use open-source tools like Python, scikit-learn, or TensorFlow. The dataset can be obtained from platforms like Twitter or Reddit.

Medical Image Classification​

Problem Statement: Build a machine learning model to classify medical images (X-rays, MRIs, CT scans) into different categories such as normal, benign, or malignant. Utilize open-source frameworks like TensorFlow or PyTorch and consider datasets available on platforms like Kaggle or NIH.

Predictive Maintenance for Equipment​

Problem Statement: Create a predictive maintenance model that forecasts equipment failures based on historical data. Use open-source tools such as Python, scikit-learn, or Apache Spark. You can find relevant datasets on platforms like NASA Prognostics Center or UCI Machine Learning Repository.

Fraud Detection in Financial Transactions​

Problem Statement: Develop a machine learning model to detect fraudulent activities in financial transactions. Utilize open-source tools like Python, scikit-learn, or Apache Flink. Consider datasets from platforms like Kaggle or the IEEE-CIS Fraud Detection dataset.

Natural Language Processing (NLP) for Customer Support​

Problem Statement: Build an NLP model to automate customer support ticket categorization and routing. Use open-source NLP libraries like spaCy or NLTK and consider datasets from platforms like Zendesk or Kaggle.

Problem Statement: End-to-End MLOps Pipeline Optimization​

Description​

Organizations are increasingly adopting machine learning for various applications, and MLOps plays a crucial role in streamlining the machine learning lifecycle. The challenge is to design and optimize an end-to-end MLOps pipeline for deploying and managing machine learning models efficiently.

Tasks​

Source Code Versioning Integration​

Integrate a version control system (e.g., Git) into the MLOps pipeline for managing and tracking changes in machine learning models and associated code.

Automated Model Training and Evaluation​

Implement an automated model training and evaluation system. The pipeline should trigger model training based on new data, conduct evaluation, and automatically select the best-performing models for deployment.

Continuous Integration for ML Code​

Set up a continuous integration system specifically tailored for machine learning code. Ensure that the pipeline automatically checks the integrity of the ML codebase and dependencies.

Model Deployment Strategies​

Explore and implement different model deployment strategies, such as canary releases or blue-green deployments, within the MLOps pipeline. Optimize for zero-downtime deployments and rollback mechanisms.

Monitoring and Logging for ML Models​

Develop a comprehensive monitoring and logging solution for deployed ML models. Implement alerts based on model performance metrics, drift detection, and other relevant indicators.

Automated Model Retraining​

Design an automated retraining mechanism that triggers retraining when performance metrics degrade over time or when new labeled data becomes available.

Infrastructure Orchestration for ML Workloads​

Integrate infrastructure orchestration tools (e.g., Kubernetes) into the MLOps pipeline for scaling and managing the infrastructure required for ML workloads efficiently.

Security and Compliance Measures​

Implement security measures within the MLOps pipeline, including data encryption, access controls, and compliance checks, to ensure the integrity and confidentiality of ML models and data.

Evaluation Criteria​

Teams will be evaluated based on the efficiency of their end-to-end MLOps pipeline, including automation, scalability, robustness, and adherence to best practices in MLOps.

This problem statement encourages participants to address the complete lifecycle of machine learning models, from versioning and training to deployment, monitoring, and security, in a real-world MLOps scenario.

Generative AI Hackathons​

For a hackathon in view of Generative computer based intelligence, here are some great and moderately simple to-carry out projects:

  1. Text Age: Construct a language model that produces innovative text, like brief tales, sonnets, or melody verses.
  2. Picture Subtitling: Make a model that produces inscriptions for pictures, depicting their items.
  3. Style Move: Foster a framework that applies imaginative styles to pictures utilizing generative models like GANs.
  4. Doodle Age: Make a model that produces practical pictures from straightforward doodles or outlines.
  5. Music Sythesis: Construct a generative model that makes music in light of given info or explicit classes.
  6. Symbol Maker: Plan a framework that produces customized symbols or animation characters in light of client inclinations.
  7. Craftsmanship Age: Foster a model fit for producing interesting works of art or conceptual plans.
  8. Voice Blend: Make a generative model that can combine discourse in various voices or accents.
  9. Face Transforming: Foster an instrument that transforms one face into another easily utilizing generative strategies.
  10. Logo Configuration: Construct a model that produces custom logos in light of the given organization name or industry.
  11. Text Generation: 📝 Build a language model that generates creative stories, poetry, or dialogue based on a given prompt.
  12. Image-to-Image Translation: 🖼️ Create a model that converts sketches to realistic images or turns day scenes into night scenes.
  13. Music Generation: 🎵 Develop an AI system that composes melodies or generates music in different genres.
  14. Artistic Style Transfer: 🎨 Implement a model that applies the style of one image to another, creating artistic effects.
  15. Face Generation: 👤 Train a model to generate realistic human faces or anime characters.
  16. Handwriting Generation: ✍️ Create an AI that generates handwritten-like text based on a user's input.
  17. Data Augmentation: 📊 Use generative models to augment datasets for training other machine learning algorithms.
  18. Chatbot with Personality: 💬 Develop a chatbot that responds in different personalities or mimics famous personalities.

Questions / Problem Statements / Hackathons​