From the course: AI Pricing and ROI: A Technical Breakdown

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Introduction to AI as a platform

Introduction to AI as a platform

From the course: AI Pricing and ROI: A Technical Breakdown

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Introduction to AI as a platform

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- [Instructor] For many use cases, there isn't always an API for a task, so sometimes we need to build the models ourselves. In this case, it's smart to focus on the model and let a platform provide the structure that we need. In this video, we'll focus on the same parameters we used in the previous video for AI APIs, including time to market, reliability, cost, latency, multi-tenancy, and customizability. Platforms provide us with standardized methods to deploy and train models, allowing us to get to market faster for each subsequent model. Some of these frameworks include Databricks, SageMaker, Azure ML, Vertex AI, and many more. Since these platforms specialize in these products, they're typically more reliable with hundreds of engineers working on them. Cost-wise these platforms may be expensive, but many charge in the same elastic way as regular cloud services. As an example here, many of these platforms have…

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