Building with AI is easy. Shipping is hard
Summary
DiamantAI has launched "Prompt to Production," a new comprehensive course designed to guide professionals from initial AI prototypes to robust production systems. This program, comprising 16 lectures each paired with a hands-on lab, focuses on developing reliable, efficient, and modular AI software, mirroring practices of top production teams. Participants will learn a repeatable methodology and deploy a live product, all within approximately 15 focused hours at their own pace, supported by an in-terminal AI coach. The course is offering ten free early access spots this month in exchange for feedback, and individuals joining the waiting list will secure a special founding price before the public launch.
Key takeaway
For AI Engineers or MLOps professionals struggling to transition AI prototypes into reliable production systems, consider exploring DiamantAI's "Prompt to Production" course. It provides a structured methodology, hands-on labs, and an AI coach to help you master building efficient, modular, and deployable AI software. Joining the waiting list now secures a founding price, offering a cost-effective way to acquire critical production-grade AI development skills.
Key insights
Building AI prototypes is easy; achieving reliable, efficient, and modular production systems requires a specific methodology.
Principles
- Software should be reliable, efficient, and modular.
- A structured methodology is key for production AI.
- Hands-on application reinforces learning.
Method
The course proposes a method involving structured prompting, progressing through 16 lectures and hands-on labs, guided by an AI coach, to build and deploy a production system.
In practice
- Develop AI software with production-grade reliability.
- Implement efficient and modular AI system designs.
- Deploy a real, live AI product.
Topics
- AI Production Systems
- MLOps
- AI Software Development
- Prompt Engineering
- AI Training Courses
- DiamantAI
Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by 💎DiamantAI.