HIGAET LLM Engineering
Go deep on model selection, fine-tuning, inference optimization, and serving, with labs on data preparation, adapters, and production LLM endpoints.
Duration
10 weeks · 6-8 hours/week
Level
Advanced
Delivery
Hybrid
Status
Open for enrollment
Why this technology matters.
LLM engineering goes deep on how language models are chosen, adapted, and served reliably at scale. It matters now because model quality, latency, and cost decide whether an AI feature survives real usage.
Product and engineering teams use it to pick between open and hosted models, prepare instruction and preference data, and run scalable inference endpoints. It solves fit-for-purpose selection, domain adaptation through fine-tuning, and efficient serving with batching and caching, but it does not fix missing use-case data, unclear quality criteria, or a product nobody needs — a well-tuned model on the wrong task still fails.
By the end you will be able to build a model evaluation comparing quality, latency, and cost trade-offs, a cleaned instruction-tuning dataset with deduping, and a fine-tuned adapter plus a scalable inference endpoint with batching and caching.
Why this course exists
The gap is between calling a demo API and running a production LLM that is the right model, trained on clean data, and served fast and affordably. This course teaches the arc from Model selection through Prompt and Context to Evaluation, Security, Infrastructure, and Production, so students can take models from dataset preparation to live endpoints.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python and REST APIs
- Familiarity with LLM APIs and model concepts
- Basic data cleaning and evaluation knowledge
Technologies & tools
Skills you'll gain
A 10 weeks arc, module by module.
- Module 01
Module 01 — Foundations: Model families, tokenizers, and context windows
- Module 02
Module 02 — Core: Model selection, benchmarks, and routing strategies
- Module 03
Module 03 — Core: Dataset design, cleaning, and instruction formatting
- Module 04
Module 04 — Engineering: Adapter-based fine-tuning and checkpoint review
- Module 05
Module 05 — Engineering: Inference servers, batching, and caching
- Module 06
Module 06 — Advanced: Long-context design and structured generation
- Module 07
Module 07 — Production: Serving, scaling, monitoring, and fallback design
- Module 08
Module 08 — Capstone: Fine-tune and serve a task-specialized model endpoint
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Evaluate open and hosted models for quality, latency, and cost trade-offs
- Develop instruction and preference datasets with cleaning and deduping
- Build fine-tuning runs using parameter-efficient adapter methods
- Deploy scalable inference endpoints with batching and caching
- Integrate guardrails, structured outputs, and fallback models
- Architect long-context handling with chunking and summarization
- Secure model artifacts, datasets, and endpoint access
- Optimize throughput, quantization, and serving costs
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Model selection benchmark report
- Project 02
Instruction dataset preparation pipeline
- Project 03
Parameter-efficient fine-tuning run
- Capstone
Production LLM inference endpoint
Speak the language first.
- Model selection
- Comparing open and hosted models on quality, latency, and cost to pick the right fit for a task.
- Instruction datasets
- Collections of prompt-and-answer examples used to teach a model to follow directions.
- Data cleaning and deduping
- Removing errors and duplicate examples so training data is consistent and reliable.
- Parameter-efficient adapters
- Small trainable layers added to a frozen model that adapt behavior without retraining everything.
- Fine-tuning runs
- Training sessions that adjust a model on task data, tracked with settings and checkpoints.
- Inference optimization
- Techniques that make model responses faster and cheaper, such as batching and caching.
- Batching
- Grouping multiple requests together so the model processes them more efficiently.
- Inference endpoints
- Hosted API services that serve a model to applications with scaling and monitoring.
- Latency-cost trade-offs
- Balancing response speed, answer quality, and operating cost when choosing a setup.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Fine-tuning barely improves task quality
Inspect the dataset for noisy or duplicated examples, clean and rebalance it, then rerun with a smaller learning rate.
Inference endpoint is slow under load
Enable request batching and response caching, then check GPU utilization to decide whether to scale replicas.
Model choice exceeds budget
Benchmark a smaller open model against the hosted one on your eval set and switch routine traffic to the cheaper option.
Training run overfits to repeated phrases
Dedupe near-identical examples, add held-out validation checks, and stop training when validation quality plateaus.
Endpoint returns timeouts on long outputs
Raise timeout limits, enable streaming so partial tokens return early, and cap maximum output length.
Before you move on, you should be able to
- Evaluate open and hosted models for quality, latency, and cost
- Develop instruction and preference datasets with cleaning and deduping
- Build fine-tuning runs using parameter-efficient adapters
- Deploy scalable inference endpoints with batching and caching
- Explain model selection trade-offs for production use
- Evaluate fine-tuned models against baseline behavior
- Build data preparation pipelines for training runs
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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Ready to start HIGAET LLM Engineering?
A 10 weeks course — AI & Generative Intelligence.