GitHub
ai-ml-engineer
Copilot agent that assists with machine learning model development, training, evaluation, deployment, and MLOps Trigger terms: machine learning, ML, AI, model training, MLOps, model deployment, feature enginee...
☆ 3查看技能
tools machine learning AI 技能
发现 tools machine learning 相关 Skills，比较来源仓库和用途，查看适合你工作流的技能说明。
分类目录: tools machine learning
tools machine learning 清除筛选
全部技能
GitHub
performance-optimizer
Copilot agent that assists with performance analysis, bottleneck detection, optimization strategies, and benchmarking Trigger terms: performance optimization, performance tuning, profiling, benchmark, bottlene...
☆ 3查看技能
GitHub
model-deployment
Model deployment strategies including serving infrastructure, containerization, model packaging, versioning, and production deployment patterns.
☆ 3查看技能
GitHub
ml-serving-optimization
ML serving optimization techniques including batching, caching, model compilation, and latency reduction for production ML systems.
☆ 3查看技能
GitHub
dataset-engineering
Building and processing datasets - data quality, curation, deduplication, synthesis, annotation, formatting. Use when creating training data, improving data quality, or generating synthetic data.
☆ 3查看技能
GitHub
ml-workflow
ML development workflow covering experiment design, baseline establishment, iterative improvement, and experiment tracking best practices.
☆ 3查看技能
GitHub
ai-engineering
Building production AI applications with Foundation Models. Covers prompt engineering, RAG, agents, finetuning, evaluation, and deployment. Use when working with LLMs, building AI features, or architecting AI s...
☆ 3查看技能
GitHub
inference-optimization
Optimizing AI inference - quantization, speculative decoding, KV cache, batching, caching strategies. Use when reducing latency, lowering costs, or scaling AI serving.
☆ 3查看技能
GitHub
deployment-paradigms
ML deployment paradigms including batch vs real-time inference, online vs offline serving, edge deployment, and serverless ML.
☆ 3查看技能
GitHub
coding-with-omega-leverage
Applies AI-first development patterns for 10-100x productivity through specification-driven generation and leverage multiplication. Use when developing features with AI assistance or optimizing development work...
☆ 3查看技能
GitHub
dnn-architectures
Deep neural network architectures including CNNs, RNNs, Transformers, and modern architectures for vision, NLP, and multimodal tasks.
☆ 3查看技能
GitHub
finetuning
Finetuning Foundation Models - when to finetune, LoRA, QLoRA, PEFT techniques, memory optimization, model merging. Use when adapting models to specific domains, reducing costs, or improving performance.
☆ 3查看技能
GitHub
ml-systems-fundamentals
Core ML systems concepts including ML lifecycle, system architecture, requirements, and design principles for production ML.
☆ 3查看技能
GitHub
feature-engineering
Feature engineering techniques including feature extraction, transformation, selection, and feature store management for ML systems.
☆ 3查看技能
GitHub
model-development
Model development practices including model selection, training pipelines, hyperparameter tuning, evaluation, and model selection strategies.
☆ 3查看技能
GitHub
foundation-models
Understanding Foundation Models - architecture, sampling parameters, structured outputs, post-training. Use when configuring LLM generation, selecting models, or understanding model behavior.
☆ 3查看技能
GitHub
ml-frameworks
ML framework best practices for PyTorch, TensorFlow, scikit-learn, and modern ML libraries including training patterns and optimization.
☆ 3查看技能
GitHub
model-optimization
Model optimization techniques including hyperparameter tuning, architecture search, training optimization, and performance profiling for ML systems.
☆ 3查看技能
GitHub
evaluation-methodology
Methods for evaluating AI model outputs - exact match, semantic similarity, LLM-as-judge, comparative evaluation, ELO ranking. Use when measuring AI quality, building eval pipelines, or comparing models.
☆ 3查看技能
GitHub
ai-system-evaluation
End-to-end AI system evaluation - model selection, benchmarks, cost/latency analysis, build vs buy decisions. Use when selecting models, designing eval pipelines, or making architecture decisions.
☆ 3查看技能
GitHub
thinking-sequentially
AI agent structures complex reasoning through numbered thought sequences with explicit dependencies. Use when facing multi-step problems, complex debugging, or architectural decisions.
☆ 3查看技能
GitHub
mlops
MLOps practices including CI/CD for ML, experiment tracking, model monitoring, pipeline orchestration, and production ML operations.
☆ 3查看技能
GitHub
data-engineering
ML data engineering covering data pipelines, data quality, collection strategies, storage, and versioning for machine learning systems.
☆ 3查看技能
GitHub
efficient-ai
Efficient AI techniques including model compression, quantization, pruning, knowledge distillation, and hardware-aware optimization for production systems.
☆ 3查看技能
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。