What resources should we use?

Industry 4.0 AI has blurred the lines between cloud platforms, frameworks, libraries, languages, and models. Transformers are new, and the range and number of ecosystems are mind-blowing. Google Cloud provides ready-to-use transformer models.

OpenAI has deployed a “transformer” API that requires practically no programming. Hugging Face provides a cloud library service, and the list is endless.

This lesson will go through a high-level analysis of some of the transformer ecosystems we will be implementing throughout this course.

Our choice of resources to implement transformers for NLP is critical. It is a question of survival in a project. Imagine a real-life interview or presentation. Imagine you are talking to your future employer, your employer, your team, or a customer.

You begin your presentation with an excellent PowerPoint with Hugging Face, for example. You might get an adverse reaction from a manager who may say, “I’m sorry, but we use Google Trax here for this type of project, not Hugging Face. Can you implement Google Trax, please?” If you don’t, it’s game over for you.

The same problem could have arisen by specializing in Google Trax. But, instead, you might get the reaction of a manager who wants to use OpenAI’s GPT-3 engines with an API and no development. If you specialize in OpenAI’s GPT-3 engines with APIs and no development, you might face a project manager or customer who prefers Hugging Face’s AutoML APIs. The worst thing that could happen to you is that a manager accepts your solution, but in the end, it does not work at all for the NLP tasks of that project.

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