• Understand the architecture and mechanics of transformer-based models.

• Learn the principles behind pretraining and fine-tuning large models.

• Explore scaling laws and efficiency techniques for large-scale AI.

• Use distributed training frameworks like PyTorch DDP and DeepSpeed.

• Implement fine-tuning methods such as LoRA and PEFT.

• Optimize inference using quantization and pruning.

• Evaluate model performance using benchmark datasets.

• Deploy foundation models as APIs or cloud microservices.

• Understand model alignment and safety mechanisms.

• Prepare for research or engineering roles in large-scale AI model development.

Course Syllabus

Module 1: Introduction to Foundation Models
Module 2: Transformer Architecture and Attention Mechanisms
Module 3: Pretraining Objectives and Data Preparation
Module 4: Scaling Laws, Compute, and Optimization Strategies
Module 5: Fine-Tuning Techniques – LoRA, PEFT, and RLHF
Module 6: Distributed Training and Parallelization
Module 7: Model Compression, Quantization, and Pruning
Module 8: Evaluation, Benchmarking, and Bias Analysis
Module 9: Deployment Strategies – APIs, Containers, and Cloud Inference
Module 10: Capstone Project – Fine-Tuning and Serving a Foundation Model

Upon successful completion, learners receive a Certificate of Completion from Uplatz, validating their expertise in Foundation Model Engineering. This Uplatz certification demonstrates proficiency in the architecture, training, and deployment of large-scale AI models used in modern generative and predictive systems.

The certification aligns with cutting-edge practices in LLM development, MLOps, and AI model optimization. It is ideal for AI engineers, data scientists, and researchers aiming to build or customize foundation models for text, image, or multimodal applications.

This certificate highlights your readiness to work on high-impact projects in AI product development, applied research, and enterprise-level deployment of large-scale models.

The global demand for professionals skilled in foundation model engineering is skyrocketing, as enterprises and research labs race to build and fine-tune proprietary models.

After completing this course from Uplatz, learners can pursue roles such as:

• AI Research Engineer

• LLM Developer / Model Fine-Tuning Specialist

• MLOps Engineer (LLM Deployment)

• AI Infrastructure Engineer

• Applied Scientist (Generative AI)

Professionals in this domain earn between $120,000 and $220,000 per year, with even higher salaries at leading AI labs and startups.

Career opportunities exist in organizations working on large-scale AI platforms, generative AI startups, enterprise AI integration, and academic research labs. This course equips you with the ability to engineer foundation models responsibly, efficiently, and at scale — the core skill set defining the future of AI innovation.

1. What is a Foundation Model?
   A large AI model trained on diverse data capable of generalizing across multiple tasks.

2. What architecture underlies most foundation models?
   The Transformer architecture, featuring self-attention mechanisms and multi-layer networks.

3. What are scaling laws in AI?
   Empirical relationships describing how model performance scales with data, parameters, and compute.

4. What is LoRA fine-tuning?
   Low-Rank Adaptation — a parameter-efficient fine-tuning method for large models.

5. What is the role of pretraining in LLMs?
   To learn general linguistic or multimodal representations before fine-tuning for specific tasks.

6. What is quantization in AI models?
   Reducing numerical precision (e.g., FP32 → INT8) to make inference faster and more efficient.

7. How does RLHF improve model performance?
   Reinforcement Learning from Human Feedback aligns model outputs with human preferences.

8. What tools are used to train foundation models?
   PyTorch, TensorFlow, DeepSpeed, Megatron-LM, and Hugging Face Transformers.

9. What is the main challenge of training foundation models?
   Managing computational cost, data quality, and ethical considerations at scale.

10. How are foundation models deployed efficiently?
    Using techniques like quantization, sharding, caching, and serving via scalable inference APIs.