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Unclaimed ProfileThe AI Engineer Production Track moves beyond simple chatbot prototypes and dives deep into the high-stakes world of enterprise-grade AI deployment. This comprehensive program is meticulously designed to bridge the gap between local development and production-ready infrastructure. You will master the complexities of deploying Large Language Models and sophisticated Agentic systems across leading cloud providers such as AWS, GCP, Azure, and Vercel. The curriculum places a heavy emphasis on MLOps, ensuring that your AI architectures remain scalable, observable, and secure in a real-world environment. Participants will gain hands-on experience with modern industry standards, including Amazon Bedrock, SageMaker, and advanced multi-agent orchestration. By integrating automation via Terraform and GitHub Actions, this course empowers developers to manage end-to-end lifecycles—from development and testing to continuous delivery. Whether you are aiming to implement robust RAG pipelines or complex Agentic loops with guardrails, this track provides the technical foundation needed to build AI solutions that businesses can trust, monitor, and scale under heavy production loads.
About the creator
Ligency
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Ligency is a specialized team of AI engineers and educators dedicated to bridging the gap between theoretical machine learning concepts and practical, production-grade applications. With a focus on the rapidly evolving landscape of Large Language Models (LLMs), agents, and automation, Ligency has established itself as a premier technical resource for students aiming to transition from casual AI users to professional AI builders. Their pedagogical approach is deeply rooted in the philosophy of learning by doing, prioritizing hands-on implementation over passive observation. By demystifying complex technologies such as RAG (Retrieval-Augmented Generation), QLoRA, and agentic workflows, they provide a structured pathway for…Show more
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Program Overview
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Learning format
Subcategory
Price
Price may change · updated within 1–2 weeks
Course language
What You'll Learn
- Deploy SaaS LLM apps to production on Vercel, AWS, Azure, and GCP, using Clerk
- Design cloud architectures with Lambda, S3, CloudFront, SQS, Route 53, App Runner and API Gateway
- Integrate with Amazon Bedrock and SageMaker, and build with GPT-5, Claude 4, OSS, AWS Nova and HuggingFace
- Rollout to Dev, Test and Prod automatically with Terraform and ship continuously via GitHub Actions
- Deliver enterprise-grade AI solutions that are scalable, secure, monitored, explainable, observable, and controlled with guardrails.
- Create Multi-Agent systems and Agentic Loops with Amazon Bedrock AgentCore and Stands Agents
Best For
- Software engineers transitioning into AI/ML roles.
- Cloud architects looking to specialize in LLM deployment pipelines.
- Developers who have built LLM prototypes but struggle with scaling.
- MLOps engineers focused on automating the model lifecycle.
Not For
- Absolute beginners with no programming experience.
- Individuals seeking basic prompt engineering tutorials.
- Learners strictly interested in theoretical AI research without infrastructure requirements.
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