ADaSci Academy

    ADaSci Certified LLMOps Engineer

    The ADaSci Certified LLMOps Engineer (CLE) program is a 30-hour, self-paced certification designed to equip professionals with the skills to build, deploy, and operate reliable, secure, and scalable large language model…

    3,449+ enrolled
    30 hours
    Intermediate
    ADaSci Certified LLMOps Engineer
    01 — Overview

    About this course

    The ADaSci Certified LLMOps Engineer (CLE) program is a 30-hour, self-paced certification designed to equip professionals with the skills to build, deploy, and operate reliable, secure, and scalable large language model systems across cloud and enterprise environments.


    Covering the full LLM lifecycle, from prompt engineering, fine-tuning, and RAG pipelines to CI/CD automation, cloud and on-premises deployment, observability, and governance, the curriculum blends strong operational foundations with hands-on labs and real-world case studies. Participants gain practical experience using tools and leading cloud AI platforms to manage LLMs in production-grade settings.


    This globally recognised certification validates your ability to ensure LLM reliability, performance, security, and compliance at scale, making you a critical enabler of enterprise AI adoption. Ideal for LLMOps, MLOps, DevOps, cloud engineers, and AI platform teams, it accelerates careers at the intersection of AI engineering, infrastructure, and responsible AI operations.


    With lifetime validity, structured learning pathways, and recognition by the world’s premier AI body, this certification positions you as a trusted leader in operationalising large language models for real-world, mission-critical applications.

    02 — Outcomes

    What you'll learn

    Design, version, and operate LLM systems from experimentation and prompt engineering to deployment, monitoring, and continuous improvement in production.
    Build automated pipelines for testing prompts, validating models and data, containerizing services, and deploying LLM applications.
    Deploy and manage LLM workloads across Kubernetes, serverless platforms, and local GPU infrastructure while optimizing for cost, latency, and reliability.
    Implement logging, tracing, drift detection, and human-in-the-loop feedback systems to measure performance, ensure quality, and maintain long-term model health.
    Apply enterprise-grade security controls, compliance frameworks, access management, and bias mitigation techniques to operate LLMs safely and transparently at scale.
    03 — Curriculum

    Course content

    7 sections · 62 lectures

    04 — Prerequisites

    Requirements

    • This certification course develops the ability to design, deploy, and operate large language model systems across data, infrastructure, and production environments. It builds expertise at the intersection of AI reliability, automation, and enterprise-scale LLM lifecycle management.
    05 — Audience

    Who should take this course

    • Intermediate to Advanced

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    Duration30h
    LevelIntermediate
    CertificateYes

    $249.00