A production ML portfolio project must showcase: - Data Ingestion & Preprocessing: Automated cleaning pipelines handling noisy data. - Vector Database Integration: Hybrid search combining dense embeddings (FAISS/Pinecone) with BM25 keyword search. - Evaluation Metrics: Automated test suites benchmarking latency, hallucination rates, and retrieval recall. - Production Containerization: Deployed via Docker with a responsive frontend demo.
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Use quantized models (GGUF, ONNX runtime) running efficiently on free-tier CPU instances or serverless endpoints (Hugging Face Spaces, Modal).
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How to structure a machine learning resume: highlighting model evaluation metrics, production deployment pipelines, and Kaggle/research contributions.
Math fundamentals (linear algebra, calculus), PyTorch, ML algorithms, MLOps tooling, and serving production models at scale.
The newest high-demand role: Prompt engineering, Retrieval-Augmented Generation (RAG), vector databases, autonomous agent workflows, and model evaluations.