Which stage resonates with you the most? Share your thoughts! 👇 #AI #MachineLearning #GenerativeAI #IBMTechnology.

This framework might just be what you need to find the winning combination of models and use cases.

Remember, a multi-model approach can be beneficial. Different use cases may require different models for optimal performance.

Finally, be mindful of deployment. Will you use a public cloud or go on-premise? Each option comes with its own trade-offs in terms of security and control.

Stage 5: Choose the model that delivers the most value. It’s all about aligning performance with your specific needs and context.

Stage 4: Test the shortlisted models against your identified use case. Run some trials to see how they perform in real-world scenarios.

Stage 3: Evaluate each model's size, performance, costs, risks, and deployment methods. This step is crucial to understanding the landscape of your choices.

Stage 2: Compile a shortlist of available models. Are there foundation models your organization is already using? This will help narrow down your options.

Stage 1: Clearly articulate your use case. What do you want your AI to do? This is the foundation upon which everything else will be built.

Instead, let’s explore a straightforward AI model selection framework with six essential stages. Ready? Let’s break it down!.

But here’s the kicker: bigger isn’t always better. 📊 While massive models may seem appealing due to their parameter counts, they come with hefty costs—compute, complexity, and variability.

If you’re diving into generative AI, choosing the right foundation model isn’t easy. With countless options available, the stakes are high—pick wrong, and you may face biases or hallucinations.

🧵 The most critical guide to selecting the right AI foundation model for your use case:

What are your thoughts on the impact of Large Language Models? 🤔 Drop your comments below! And if you found this thread insightful, retweet and follow for more! #AI #MachineLearning #BusinessInnovation.

They can also assist in software development by generating and reviewing code. And this is just the beginning! 🚀 As LLMs continue to evolve, we’ll uncover even more innovative uses.

Now, let’s talk about business applications! LLMs can create intelligent chatbots for customer service, generate content for articles, emails, and even YouTube scripts! 📈.

During training, LLMs learn to predict the next word in a sentence, adjusting their parameters to improve accuracy. Think: "The sky is. "—with enough iterations, they can confidently conclude: "blue!" 🌈.

How do they work? 🤖 It's all about three components: data, architecture, and training. The transformer architecture enables LLMs to understand word context within sentences, making predictions more accurate.

LLMs are not just huge in size; they're also complex in nature. The most advanced models, like GPT-3, are pre-trained on 45 terabytes of data and utilize 175 billion parameters! 🌐 That's a lot of information to process!.

We're talking about models that can be tens of gigabytes in size and trained on petabytes of text data! 📚 To put that in perspective, 1 GB can store about 178 million words—imagine the scale we're dealing with!.