The AI kit for Machine Learning Engineers

Sets up MLOps pipelines and RAG systems, for example wiring drift alerts and a rollback path for a recommender model.

Guides cleaning, encoding, scaling, and imbalance handling, for example fixing skewed timestamps before training a demand model.

Turns a trained model into fast, cheap inference, for example quantizing a vision model to hit 50ms latency.

Finds duplicates, missing values, and format drift, for example flagging 4,000 duplicate customer rows before analysis.

Like "Write a Pandas Data Cleaning Script"

Like "Feature Engineering Ideas"

Like "Compare AI Model Architectures"

Like "Debug A Failing Training Run"

A tested reproduction bundle, with any remaining failures stated.

A fair, source-linked methods comparison.

Reach a production deploy where all checks pass and a tested rollback exists.

A current register of live models with owners, reviews and risk status

Lets your AI look up MLflow experiments, runs, models, and traces and summarize them for you.

Lets your AI estimate, run and check GPU jobs on Jungle Grid, then fetch the results.

Your AI can gather fresh research papers, trending code, and FDA device news, then write you a short newspaper-style briefing.

Let your AI open, edit and run Jupyter notebooks for you while you watch.

Evidently AI is an open-source framework for evaluating, testing, and monitoring AI applications with 100+ built-in checks, supporting offline and live assessme

Data Labeling Platform streamlines dataset annotation for machine learning, enabling AI engineers to upload, label, and monitor progress efficiently—ideal for c

The DVC Extension for VS Code lets you run, track, and compare ML experiments and manage reproducible pipelines directly within your IDE, streamlining model dev

RunPod offers flexible access to GPU-based compute resources with pay-per-second serverless options. Enjoy features like AI endpoints, Cloud Sync, and persisten

Keeps training runs and releases in one place, so you stop hand-copying model artifacts between tools.

Gives you a repeatable test and drift record before release, cutting failed rollbacks.

Stores your cleaning steps once, so every project starts from the same trusted raw-data pipeline.

Keeps notebook analysis tied to its evidence, so reviewers can follow your reasoning without a re-run.

Build and maintain production ML systems with PyTorch, TensorFlow, and modern MLOps practices.

Orchestrate a multi-agent MLOps pipeline from data ingestion to production serving.

Deploy and optimize ML models for production inference at scale.

End-to-end MLOps pipeline orchestration from data prep to model deployment and monitoring.