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AdvancedMachine Learning8 weeks
Certificate IncludedCertificate EligibleUcanly certificate

Machine Learning: Advanced

From engineer to machine learning architect: ML system design, distributed training, recommender systems, MLOps, and production Generative AI at scale.

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

What you'll learn

Architect production ML systems, addressing offline and online consistency, latency, and safe degradation
Process data at scale with PySpark and build reusable feature platforms with feature stores
Understand transformer internals and run distributed training with data and model parallelism
Build specialized ML systems: large scale recommenders, learning to rank, and reinforcement learning
Run full MLOps with model registries, versioning, inference optimization, and drift detection
Fine tune models with LoRA and QLoRA, and build production RAG architectures
Run LLMOps in production: rigorous evaluation, inference optimization with vLLM, cost engineering, and guardrails
Architect and launch a scalable, production grade ML platform with a feature store, monitoring, and full CI/CD

Who this course is for

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

Skills you'll gain

Advanced Machine LearningML ArchitectML System DesignDistributed TrainingDeepSpeedRayFeature StoreMLOpsMLflowModel MonitoringDrift DetectionRecommender SystemsLearning to RankReinforcement LearningFine TuningLoRAQLoRAProduction RAGLLMOpsvLLMQuantizationONNXSenior Machine Learning Engineer

This course includes

Live mentor led Ucanly sessions plus self paced recorded content
Ucanly mock ML system design interviews and senior level portfolio review
Resume and profile 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

Machine learning system design, data engineering and feature platforms, training at scale and advanced deep learning, specialized ML systems including recommenders and RL, MLOps serving and reliability, advanced Generative AI and LLM engineering, LLMOps and cost engineering, and a capstone production ML platform.

Requirements

  • Completion of the Intermediate level, or hands on experience training, tuning, and deploying machine learning and deep learning models on real data
  • 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 Machine Learning track, where engineers become ML architects. Built for practitioners with hands on experience training, tuning, and deploying machine learning and deep learning models on real data, this program goes deep into ML system design, large scale data engineering with PySpark and Airflow, distributed training with DeepSpeed, and specialized systems like recommenders, ranking, and reinforcement learning. You will run full MLOps with feature stores, model registries, and drift detection, and move into advanced Generative AI and LLM engineering: fine tuning with LoRA and QLoRA, production RAG, and full LLMOps covering evaluation, guardrails, inference optimization with vLLM, and cost engineering. You will graduate having architected and launched a scalable, production grade machine learning platform with automated pipelines, a feature store, a customized model, monitoring and drift detection, an AI powered layer, and full CI/CD.

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, tuning, and deploying machine learning and deep learning models on real data is sufficient to start.

Yes, you will build a production recommender system covering retrieval, ranking architecture, and reinforcement learning and bandit based online learning.

Fine tuning with LoRA and QLoRA, production RAG architectures, distillation, and full LLMOps including evaluation, vLLM based inference optimization, and cost engineering.

A scalable, production grade machine learning platform with automated pipelines, a feature store, a customized model, monitoring and drift detection, an AI powered layer, and full CI/CD, defended in a live architecture review with Ucanly mentors.