From model to product: my AI Engineer learning path.

Three stages of progress

1. Understand data and compare models

   From pretrained APIs to regression and classification: preparing data, choosing metrics and explaining predictions.

2. Connect application components

   APIs, RAG, storage and MLOps: connecting models to services, tracking versions and experiments, and examining behaviour.

3. Experiment and draw conclusions

   Vision, RL, multimodal data, orchestration and LLM adaptation. Negative results and protocol limitations are part of engineering work.

The projects, one by one

• P1 — Building my AI learning path
• P2 — Clothing segmentation
• P3 — Predicting building energy use
• P4 — Understanding employee attrition
• P5 — Serving a model through an API
• P6 — Tracking credit-scoring experiments
• P7 — Finding events with RAG
• P8 — Deploying and monitoring scoring
• P9 — Scoping an AI project
• P10 — Learning with few labels
• P11 — Training a LunarLander agent
• P12 — Building a multimodal pipeline
• P13 — Orchestrating a chess assistant
• P14 — Adapting a medical LLM

What I want to improve

Strengthen independent evaluations, RAG response faithfulness and served-version consistency. Better document costs, operations and what I can reproduce independently. The P5 authentication competency remains unvalidated in OC feedback.

My full-stack experience helps me consider the whole application. Training pushes me to make data, model and protocol choices explicit, including their limitations.

Method and evidence

The detailed report uses OC feedback and inspected public repository files. A recorded result, assessor feedback and current functioning are different evidence. No project was run during this audit.

Fourteen validated projects. P14 status was updated following Pierre’s confirmation on 11 October 2026; technical evidence remains from the 9 October audit. Pierre confirmed the award of the RNCP level 7 certification on 11 October 2026.