learning▌

ML model deployment, production serving infrastructure, and real-time inference systems at scale. \n \n Handles model optimization (quantization, pruning, distillation), serving APIs (REST/gRPC), and container orchestration with auto-scaling on Kubernetes or cloud platforms \n Supports real-time inference, batch prediction systems, multi-model serving with intelligent routing, and A/B testing for model comparisons \n Covers edge deployment for IoT and mobile with model compression, offline capab

Interactive step-by-step Medusa development bootcamp where you build a brands feature while learning architecture patterns. \n \n Three progressive lessons (2–3 hours total) covering modules, workflows, API routes, module links, workflow hooks, and admin UI customization \n Checkpoint verification after each major component tests conceptual understanding, code quality, and functionality before proceeding \n Treats errors as teaching opportunities; debugs together with diagnostic questions and ro

You are an expert in deep learning, neural network architectures, and model optimization.

$20

Create evidence-based learning plans that maximize long-term retention through spaced repetition, retrieval practice, and interleaving.

cc-skill-continuous-learning \n Development skill skill.

Design and implement a complete ML pipeline for: $ARGUMENTS

cc-skill-continuous-learning \n Development skill skill. \n When to Use \n This skill is applicable to execute the workflow or actions described in the overview.

You are an expert in machine learning development with JAX and functional programming patterns.

Expert guidance for deep learning, transformers, diffusion models, and LLM development with PyTorch. \n \n Covers PyTorch model architectures, transformers, diffusion models, and LLM fine-tuning with libraries including Transformers, Diffusers, and Gradio \n Emphasizes GPU optimization, mixed precision training, distributed training, and gradient accumulation for efficient workflows \n Includes best practices for data loading, train/validation splits, early stopping, learning rate scheduling, an