ML Model Lifecycle

Queries Hugging Face benchmark leaderboards to find the best AI models for a task, filters by device constraints, and returns a ranked comparison table with scores.

Turn model work into a production ML system with data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.

Manage machine learning models in the Agent Platform Model Registry: list, describe, upload, update, and delete models and their versions.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks, including model serving, feature engineering, A/B testing, and monitoring.

Quantize large language models to 4-bit precision using activation-aware weight quantization, reducing memory footprint and speeding up inference with minimal accuracy loss.

Fine-tune instruction-following LLMs with Unsloth's optimized SFTTrainer, covering dataset formatting, chat templates, training configuration, and thinking-model patterns.

Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Manages the machine learning lifecycle with experiment tracking, model versioning, reproducible runs, and deployment through the MLflow platform.

Build and deploy machine learning models with TensorFlow, covering Keras, data pipelines, and production serving.

Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.