The all-in-one AI plugin for AI Engineers

Skills for ai engineers 17

• code debugging assistantTraces a failing pytest run to the bad import and returns a fixed file plus a regression test.
• agency engineering ai engineerTakes a churn model from notebook to a served endpoint with data pipeline and integration steps listed.
• senior ml engineerPlans a RAG service with chunking, vector store, evaluation, drift alerts and rollback for your docs.
• documentationTurns your inference API into a README and endpoint reference with request examples and error codes.
• feature engineering advisorFixes a skewed price column with log transform, imputation and encoding before you retrain the model.
• ml algorithm selection assistantCompares gradient boosting and logistic regression on your 40k row churn set, with metrics and tuning plan.

Plus a start-here assistant, the prompt library and the app builder, and for members the daily brief.

Ready-made prompts 25

• Code Drafting Basics3 prompts, like Generate ML Boilerplate Code
• Debugging and Errors3 prompts, like Error Message Explanation and Resolution
• Explaining Concepts and Papers3 prompts, like Summarize a Research Paper
• Data Preparation and Exploration4 prompts, like Plan Dataset Cleaning Steps
• Model Training and Tuning3 prompts, like Design Training Loop Components
• Deployment and Production3 prompts, like Draft Docker and API Serving Code
• Monitoring and Optimization3 prompts, like Set Up Production Model Monitoring Metrics

Apps you can build with AI 8

• Managed model training and deployment workspaceKeeps training runs, artifacts and deploys in one place so your team stops hand-copying models between tools. Demo included.
• AI model test and monitoring workbenchGives you one defensible record of model tests and live metrics before each release. Demo included.
• Prompt lifecycle testing and deployment workbenchTracks every prompt version and test result so a prompt change does not quietly break production. Demo included.
• Raw-source data preparation and stewardship libraryCuts repeated data cleaning by keeping source rights, review notes and prepared sets in one library. Demo included.
• Production agent trace and evaluation consoleShows where your agent failed in production and scores quality drift without shipping traces to a third party. Demo included.
• Source-linked multi-model text and code workbenchRuns generation, coding and analysis across models in one console with outputs linked to their sources. Demo included.
• Source-linked code review and debugging consoleShortens review cycles while keeping a named engineer's approval on every merged change. Demo included.
• Source-linked language model workbenchKeeps prompts, context and data in one owned place instead of scattered across vendor tools. Demo included.

Your Grok Bot team 12

• Ml EngineerBuild and maintain production ML systems with PyTorch, TensorFlow, and modern MLOps practices.
• Pytorch LightningOrganize PyTorch code into LightningModules and configure Trainers for scalable neural network training.
• Python DevelopmentScaffold production-ready Python projects with modern tooling and type hints.
• Deployment EngineerDesigns and optimizes CI/CD pipelines for faster, safer deployments with automated rollbacks and monitoring, including GitOps and progressive delivery
• Distributed Training Ray TrainScales PyTorch, TensorFlow, and HuggingFace training from one GPU to thousands of nodes across a cluster.
• Data Processing Ray DataProcess large ML datasets with Ray Data across CPU/GPU clusters.
• Remote Gpu TrainerDeploy, monitor, and debug long GPU jobs on rented instances with safe teardown and resumable checkpoints.
• Ml Pipeline WorkflowEnd-to-end MLOps pipeline orchestration from data prep to model deployment and monitoring.
• Machine Learning EngineerDeploy and optimize ML models for production inference at scale.
• Python Performance OptimizationProfile and optimize Python code for speed and memory efficiency.
• Test Driven DevelopmentEnforce the TDD cycle: write failing test first, minimal code to pass, verify both steps.
• Model Architecture TorchtitanPretrains large language models at scale using PyTorch-native torchtitan with 4D parallelism.