AI TRAINING
NLP Engineering for Product Teams
Build and ship production-grade NLP features by choosing the right model architecture for every use case.
What it covers
This practitioner-level programme equips product-focused engineers with the skills to design, build, and evaluate NLP pipelines covering classification, named entity recognition, summarisation, translation, and semantic search. Participants learn when to leverage fine-tuned task-specific models versus general-purpose LLMs, balancing latency, cost, and accuracy trade-offs in real product environments. The course combines hands-on coding labs with applied case studies drawn from SaaS, e-commerce, and enterprise software contexts. By the end, participants can independently scope, prototype, and productionise NLP features end-to-end.
What you'll be able to do
- Fine-tune a BERT-family model for a custom text classification or NER task and evaluate it with appropriate metrics
- Build a semantic search pipeline using sentence embeddings and a vector store such as Qdrant or Pinecone
- Decide with evidence whether a given NLP task is better served by a fine-tuned task-specific model or an LLM with prompt engineering
- Instrument and monitor an NLP feature in production, tracking latency, throughput, and model drift
- Write a model card documenting data sources, evaluation results, known failure modes, and deployment constraints
Topics covered
- Text classification and multi-label categorisation with transformers
- Named entity recognition (NER) and information extraction pipelines
- Extractive and abstractive summarisation techniques
- Neural machine translation and multilingual models
- Semantic search with dense embeddings and vector databases
- Fine-tuning vs prompting: when to use task-specific models vs LLMs
- Evaluation metrics: F1, BLEU, ROUGE, BERTScore, and human eval
- Serving NLP models in production: latency, caching, and cost control
Delivery
Typically delivered as a four-week blended programme: two live instructor-led sessions per week (90 minutes each) plus asynchronous labs. All labs run in cloud notebooks (Colab or hosted JupyterHub) so no local GPU is required. Roughly 60% of contact time is hands-on coding. A private Slack or Discord channel is maintained throughout for async Q&A. In-person cohort delivery is also available as a five-day intensive bootcamp format for groups of 8–16.
What makes it work
- Pair each training module with a real backlog ticket so engineers immediately apply new skills to actual product work
- Establish a shared evaluation framework and leaderboard so teams develop a consistent standard for 'good enough'
- Include a product manager or tech lead in at least the first and last sessions to align on scoping and success criteria
- Maintain a living decision guide (LLM vs fine-tuned model) that the team updates as new models and pricing emerge
Common mistakes
- Defaulting to a large LLM for every NLP task without benchmarking smaller fine-tuned models that are faster and cheaper
- Skipping offline evaluation and discovering quality problems only after deployment via user complaints
- Under-investing in data labelling quality, leading to models that fit noisy labels rather than the true task
- Treating NLP model serving like a standard API without accounting for tokenisation overhead and batching strategies
When NOT to take this
If the team has no labelled data, no data infrastructure, and needs to ship an NLP feature within two weeks, this programme is not the right fit — a rapid prompt-engineering workshop using an existing LLM API will deliver faster value at that stage.
Providers to consider
Sources
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.