AI, ML & LLMs
11 sections930 questions
The whole stack for building with machine learning and LLMs: training and inference frameworks, model providers and local runtimes, agent and RAG frameworks, AI coding tools, and the MLOps, evaluation and observability layer around them. Interviewers cover this area because shipping an AI feature is now an engineering problem at least as much as a modelling one.
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- ML Frameworks & Libraries198 questions
- PyTorch37 questions
- TensorFlow35 questions
- Keras30 questions
- Scikit-learn36 questions
- OpenCV36 questions
- On-Device PyTorch (ExecuTorch)12 questions
- TFLite12 questions
- LLM Providers & Models260 questions
- OpenAI35 questions
- Anthropic31 questions
- Google Gemini32 questions
- Cohere24 questions
- Mistral19 questions
- xAI5 questions
- DeepSeek19 questions
- Meta Llama34 questions
- Google Gemma5 questions
- Qwen15 questions
- Opus3 questions
- Sonnet3 questions
- OpenAI Image Generation6 questions
- Whisper6 questions
- OpenRouter17 questions
- GitHub Models6 questions
- Vertex AIempty
- Azure MLempty
- Amazon Bedrockempty
- Amazon SageMaker AIempty
- Hugging Faceempty
- Transformers.jsempty
- Local Model Runtimesempty
- AI Coding Toolsempty
- Claude Codeempty
- Cursorempty
- Copilotempty
- Codexempty
- Gemini CLIempty
- Lovableempty
- v0empty
- Agent & RAG Frameworks (has its own guide)258 questions
- LangChain36 questions
- LangGraph33 questions
- CrewAI36 questions
- AutoGen28 questions
- Google ADK24 questions
- Koog17 questions
- LlamaIndex35 questions
- Haystack31 questions
- RAGFlow18 questions
- AI Ops, Eval & Observability121 questions
- LLM Inference Serving93 questions
- Serving Concepts45 questions
- vLLM18 questions
- Text Generation Inference (TGI)12 questions
- NVIDIA Triton and TensorRT-LLM18 questions
- KServeempty
- DDP & Process Groupsempty
→ has its own guide