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Feature Store for ML Pipelines

data science & mlIntermediate365d access

₹149onwards

Build a lightweight feature store that computes, caches, and serves ML features. Connect it to both a training pipeline and a real-time prediction API.

Understand the purpose of a feature store and the training/serving skew problem
Compute, version, and store ML features in Redis (real-time) and PostgreSQL (historical)
Connect a feature store to both a training pipeline and a live inference API
Verify consistency between features used in training and features used in serving

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Sentiment Classifier: LSTM vs. LLM

data science & mlIntermediate365d access

₹149onwards

Train an LSTM on real product reviews. Run the same data through a zero-shot LLM classifier. Compare accuracy, latency, and cost — understand where each belongs.

Build, train, and evaluate an LSTM text classifier in PyTorch
Implement a zero-shot LLM classifier and measure its performance
Compare trained models vs LLMs on accuracy, latency, and cost per prediction
Understand the trade-off space: when to use fine-tuned models vs zero-shot LLMs

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LLM Output Evaluation Dataset Builder

data science & mlIntermediate365d access

₹149onwards

Generate a benchmark dataset by prompting an LLM across many scenarios, score outputs on multiple criteria, and produce a structured eval report with failure analysis.

Design a multi-dimensional scoring rubric for evaluating LLM outputs
Generate structured evaluation datasets using async LLM API calls
Build an LLM-as-judge pipeline that scores model responses automatically
Calculate inter-rater agreement between human and automated scoring

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