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AdvancedArtificial Intelligence8 weeks
Certificate IncludedCertificate EligibleUcanly certificate

Artificial Intelligence (AI): Advanced

From engineer to AI architect: transformer internals, distributed training, fine tuning and alignment, advanced RAG, multi agent systems, and LLMOps at scale.

Duration
8 weeks
Level
Advanced
Content
48 lessons
8 modules · 17 h 12 min
Certificate
Included
On completion
Access
Fee by email
Or take the free aptitude test

What you'll learn

Understand transformer internals, attention mathematics, positional encoding, and modern architectures like Mixture of Experts
Train models at scale using distributed training, mixed precision, and DeepSpeed
Customize models with full fine tuning, LoRA, QLoRA, and RLHF and DPO based alignment
Architect production RAG systems including graph RAG, multi hop retrieval, and grounding evaluation
Design multi agent architectures with planning, memory, and safety using LangGraph
Optimize inference with vLLM, batching, quantization, and cost aware model selection
Evaluate AI systems rigorously with benchmarks, LLM as judge methods, and bias and fairness analysis
Meet AI governance, privacy, and regulatory compliance requirements
Architect and launch a scalable, production grade AI platform with a customized model and multi agent layer

Who this course is for

  • Intermediate level graduates ready to architect production grade AI systems
  • Working AI and ML engineers preparing for senior engineer or AI architect roles
  • Engineers who want deep, hands on experience with distributed training and advanced RAG
  • Anyone targeting senior level AI system design interviews

Skills you'll gain

Advanced Artificial IntelligenceAI ArchitectTransformersDistributed TrainingFine TuningLoRAQLoRARLHFDPOAdvanced RAGGraph RAGMulti Agent SystemsLangGraphLLMOpsvLLMQuantizationAI SafetyAI GovernanceSenior AI Engineer

This course includes

Live mentor led Ucanly sessions plus self paced recorded content
Ucanly mock AI system design interviews and senior level portfolio review
Research and resume audit with placement guidance
Production style projects in every module
Lifetime access to course materials and future updates
Advanced Certificate of Completion from Ucanly

Course content

8 modules · 48 lessons · 17 h 12 min

Deep learning at depth with modern architectures, training at scale and distributed systems, model customization and alignment, advanced RAG and knowledge systems, agentic AI and multi agent architecture, LLMOps and inference optimization, evaluation and governance, and a capstone production AI platform.

Requirements

  • Completion of the Intermediate level, or hands on experience training deep learning models and shipping AI features with PyTorch, transformers, and RAG
  • Comfort with Python, PyTorch, and basic cloud or deployment concepts
  • A computer or cloud environment capable of running GPU accelerated training

About this course

This is the final stage of the Ucanly Artificial Intelligence track, where engineers become AI architects. Built for developers with hands on experience training deep learning models and shipping AI features with PyTorch, transformers, and RAG, this program goes deep into transformer internals, distributed training across GPUs, full fine tuning, LoRA and QLoRA, RLHF and preference alignment, and advanced RAG architectures including graph RAG and multi hop retrieval. You will design multi agent systems with LangGraph, optimize inference with vLLM and quantization, and apply real LLMOps discipline covering evaluation, guardrails, bias, and governance. You will graduate having architected and launched a scalable, production grade AI platform with a customized model, a multi agent layer, full evaluation and observability, and an automated CI/CD pipeline.

Earn a Ucanly certificate

Complete this course to earn a Ucanly certificate with its own page and QR code. Save it as a PDF, share its link, or add it to the certifications on your LinkedIn profile.

Certificate IncludedCertificate EligibleUcanly certificate

Frequently asked questions

It is recommended, but hands on experience training deep learning models and shipping AI features with PyTorch, transformers, and RAG is sufficient to start.

Yes, you will run a distributed training job across multiple GPUs using techniques like mixed precision and DeepSpeed.

Full fine tuning, LoRA and QLoRA, instruction tuning and dataset engineering, and RLHF and DPO based preference alignment.

A scalable, production grade AI platform with a customized model, a multi agent layer, full evaluation and observability, and an automated CI/CD pipeline, defended in a live architecture review with Ucanly mentors.