You will evaluate model output quality using automated metrics and structured human review. You will compare quantitative scores with qualitative feedback to identify performance gaps and refine results.

You will apply tokenization, embedding, and encoding techniques to build transformer-based natural language processing pipelines. You will convert raw text into encoded representations suitable for downstream tasks such as classification or summarization.

You will evaluate model output quality using automated metrics such as ROUGE and structured human evaluation frameworks. You will interpret results to assess reliability, safety, and alignment with task objectives.

You will apply TensorFlow 2.x tools to build an end-to-end machine learning workflow using tf.data pipelines and Keras models. You will structure data ingestion, model definition, training, and checkpointing into a reproducible system.

You will create optimized machine learning model deployments using TensorFlow Lite. You will evaluate inference latency, apply quantization techniques, and improve performance for mobile and edge environments.

In this project, you will design and evaluate two production-style machine learning pipelines for a financial services risk intelligence scenario: A computer vision pipeline that converts a multi-class image dataset into a binary risk classification task. A transformer-based NLP pipeline that classifies customer complaint text into low-risk or high-risk categories. You will implement both workflows using TensorFlow and transformer libraries, evaluate performance using appropriate classification metrics, perform structured error analysis, and apply at least one optimization to improve workflow performance. The final deliverable is a portfolio-ready Python script and structured analysis demonstrating your ability to design, evaluate, and refine AI workflows in a professional setting.