Machine Learning Engineers: the all-in-one AI plugin

Skills for machine learning engineers 17

• agency senior ml engineerSets up MLOps pipelines and RAG systems, for example wiring drift alerts and a rollback path for a recommender model.
• data preprocessing advisorGuides cleaning, encoding, scaling, and imbalance handling, for example fixing skewed timestamps before training a demand model.
• machine learning engineerTurns a trained model into fast, cheap inference, for example quantizing a vision model to hit 50ms latency.
• data cleaning guidance assistantFinds duplicates, missing values, and format drift, for example flagging 4,000 duplicate customer rows before analysis.
• debugReproduces and fixes code bugs with root cause analysis, for example tracing a shape mismatch in a training loop.
• ml algorithm selection assistantCompares algorithms and metrics for your data, for example choosing gradient boosting over a neural net on 20k rows.

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

Ready-made prompts 26

• Data Preprocessing & Cleaning3 prompts, like Write a Pandas Data Cleaning Script
• Feature Engineering & Splits5 prompts, like Feature Engineering Ideas
• Model Architecture & Design3 prompts, like Compare AI Model Architectures
• Training & Debugging Runs3 prompts, like Debug A Failing Training Run
• Evaluation & Error Analysis3 prompts, like Choose Metrics to Train and Evaluate Models
• Deployment & Serving3 prompts, like Write a Model Serving API
• Monitoring, Drift & Optimization3 prompts, like Design a Drift Detection Plan

Apps you can build with AI 8

• Managed model training and deployment workspaceKeeps training runs and releases in one place, so you stop hand-copying model artifacts between tools. Demo included.
• AI model test and monitoring workbenchGives you a repeatable test and drift record before release, cutting failed rollbacks. Demo included.
• Raw-source data preparation and stewardship libraryStores your cleaning steps once, so every project starts from the same trusted raw-data pipeline. Demo included.
• Evidence-backed notebook analysis and reporting workspaceKeeps notebook analysis tied to its evidence, so reviewers can follow your reasoning without a re-run. Demo included.
• Source-linked language model workbenchRuns prompts and context in one owned space, so you spend less time on tool switches and review. Demo included.
• AI inference economics workbenchTracks serving cost against measured quality, so you can cut inference spend without guessing. Demo included.
• Source-linked model workspace and admin consoleKeeps models and admin access in one auditable workspace, reducing rented tools and access sprawl. Demo included.
• Source-linked multi-model text and code workbenchConsolidates generation, reasoning, and coding with linked sources, so outputs stay traceable to inputs. Demo included.

Your Grok Bot team 12

• Ml EngineerBuild and maintain production ML systems with PyTorch, TensorFlow, and modern MLOps practices.
• Machine Learning Ops Ml PipelineOrchestrate a multi-agent MLOps pipeline from data ingestion to production serving.
• Machine Learning EngineerDeploy and optimize ML models for production inference at scale.
• Ml Pipeline WorkflowEnd-to-end MLOps pipeline orchestration from data prep to model deployment and monitoring.
• Mlops MlflowTrack ML experiments, manage model registry, and deploy models using MLflow.
• Distributed Training Ray TrainScales PyTorch, TensorFlow, and HuggingFace training from one GPU to thousands of nodes across a cluster.
• Inference Serving VllmDeploys and tunes vLLM servers for high-throughput LLM inference with quantization and monitoring.
• Mlops Weights And BiasesTrack ML experiments, visualize training, and manage model registry with Weights & Biases.
• Remote Gpu TrainerDeploy, monitor, and debug long GPU jobs on rented instances with safe teardown and resumable checkpoints.
• Performance MonitorTracks system metrics, detects anomalies, and optimizes resource usage across multi-agent environments.
• Deployment EngineerDesigns and optimizes CI/CD pipelines for faster, safer deployments with automated rollbacks and monitoring, including GitOps and progressive delivery
• ShapExplains machine learning model predictions using SHAP values and visualizations.