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From engineer to machine learning architect: ML system design, distributed training, recommender systems, MLOps, and production Generative AI at scale.
8 modules · 48 lessons
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.
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.
Complete this course to earn a verified Ucanly certificate you can add to your profile, share on LinkedIn, and showcase to employers as proof of the skills you've built.
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.