AI Model Fine-Tuning & Optimization Training in Vizag | Softenant Technologies
Fine-tuning adapts a pre-trained model to a specific task, style or domain using carefully prepared examples. This course focuses on the decisions that make that process useful: choosing whether fine-tuning is appropriate, preparing high-quality datasets, selecting parameter-efficient methods, defining evaluations, managing cost and avoiding degradation of the original model behaviour.
What makes this technology-specific
Fine-tuning is not a substitute for better prompts, retrieval or data governance. Learners first compare the problem with alternatives such as prompt engineering and RAG, then design training examples, validation tests and safety checks. The technical focus includes supervised fine-tuning concepts, adapters such as LoRA, hyperparameter trade-offs, inference optimisation, regression testing and deployment monitoring.
AI Model Fine-Tuning and Optimization syllabus
When to fine-tune—and when not to
Compare fine-tuning with prompting, RAG, tool use and workflow redesign based on knowledge freshness, behaviour control, cost and risk.
Dataset design and quality
Create instruction-response examples, remove duplicates and sensitive information, define label standards and split training from evaluation data.
Fine-tuning methods
Understand supervised fine-tuning, full versus parameter-efficient adaptation, LoRA/adapter concepts, checkpoints and training-resource trade-offs.
Hyperparameters and optimisation
Explore learning rate, batch size, epochs, sequence length, overfitting signals and the relationship between quality, time and compute cost.
Evaluation and regression checks
Build task-specific test sets, compare a base model with adapted versions, review safety failures and track regressions before release.
Serving and monitoring
Document model versions, prompt templates, latency/cost considerations, feedback collection and a rollback plan for production-style use.
Tools, concepts and working methods
Training datasets
Instruction quality, splits and governance.
LoRA concepts
Parameter-efficient model adaptation.
Evaluation suites
Base-versus-tuned comparisons and regression checks.
Optimisation
Cost, latency, quality and deployment trade-offs.
Portfolio projects with technical evidence
Domain response adapter plan
Design a task dataset, adapter-style training approach, validation examples and base-versus-tuned evaluation.
Extraction-quality improvement study
Compare prompting, RAG and fine-tuning approaches for structured extraction, then justify the selected route.
Model release checklist
Prepare version notes, quality gates, safety cases, monitoring signals and rollback steps for an adapted model.
Career direction
LLM operations trainee, AI model-evaluation trainee, machine-learning engineer foundation learner and AI deployment support trainee. Training improves technical preparation; employment outcomes depend on portfolio quality, interviews, experience and available roles.
Trusted by Softenant Technologies Students
Read Our Google ReviewsAI Model Fine-Tuning and Optimization FAQs
How is fine-tuning different from RAG?
Fine-tuning changes model behaviour using training examples. RAG supplies relevant external information at answer time without changing model weights.
Does the course cover LoRA?
It covers LoRA and adapter concepts as parameter-efficient ways to adapt models, including their trade-offs and evaluation requirements.