Course Outline
In this course, you’ll dive into the fundamentals of AI inference—how machine learning models generate real-world results. We’ll explore the two main types of inference:
- Cloud-based inference with platforms like Together AI, Groq, and Cohere, designed for scalability and high-speed deployments
- Local inference with tools like Ollama, ideal for private, offline, and resource-efficient applications
By the end of this course, you'll know how to evaluate trade-offs and choose the best inference method for your AI projects based on latency, cost, privacy, and scalability.
Learning Outcomes
- Understand the fundamentals of AI inference
- Learn how to deploy cloud-based inference for scalable applications
- Explore local inference for privacy-focused, offline AI solutions
- Compare performance, cost, and security factors for different inference types
Who Is This Course For?
This course is designed for AI developers, data engineers, and businesses looking to optimize AI model deployment. Whether you’re working on real-time AI applications, privacy-sensitive solutions, or cost-efficient deployments, this course will help you make the right technical choices.
Why Enroll?
Understanding AI inference is crucial for deploying AI efficiently. By enrolling in this course, you’ll gain hands-on insights into cloud vs. local inference, helping you select the right approach based on scalability, privacy, and performance requirements.
Pre-requisites
- Basic knowledge of machine learning models
- Familiarity with AI APIs and deployment workflows
- Interest in optimizing AI performance and costs
Let’s Get Started!
Ready to build and deploy AI models with confidence? Enroll now and master AI inference strategies!
