In-house vs. Off-the-shelf AI Models: The Era of Generative AI

The exciting acquisition of MosaicML by Databricks, as detailed in the recently published article, underscores a fundamental shift in the AI landscape.

Businesses are increasingly recognising the importance of not just having data, but having well-structured, AI-ready data that can be seamlessly integrated into large language models (LLMs).  

Generative AI is transforming industries, powering innovations, and driving growth.

But, the heart of AI’s potential lies in the data that feeds it. Companies need vast volumes of high-quality, relevant data that is both meticulously organised and readily accessible. 

And as this article stresses, many are facing pressure to prepare their data for this new wave of AI models.  

While it’s possible to use off-the-shelf models trained on generic internet data, there’s a concern that this approach often falls short. Models like these can be filled with extraneous information that can skew results and expose companies to privacy and security concerns. 

As the Databricks-MosaicML partnership illustrates, in-house language models built on proprietary data can significantly alleviate these challenges. However, it’s essential to note that moving to an entirely in-house model is not the only solution available. 

Take Google’s Vertex AI and Gen app builder as prime examples. They provide a comprehensive suite of AI tools and pre-built models in their model garden. 

This approach offers a unique value proposition by bridging the gap between generic and custom models.  

These tools understand the need for secure, private, and foundational models. And they allow organisations to start testing and implementing generative AI using their own data as the foundation. There’s also resources to build, deploy, and scale models efficiently – saving time and resources while ensuring data privacy and security. 

But there’s more at stake here than privacy and security.

When businesses can leverage their own data in custom AI models or customise pre-built models like those offered by Google Vertex AI, they unlock the ability to generate more accurate, relevant, and valuable insights.

In a world where data has become a key driver of competitive advantage, this potential is too significant to ignore. Indeed, the Databricks-MosaicML partnership is a powerful testament to the value of data readiness and in-house language models. 

Yet, solutions like Google Vertex AI and Gen app builder show that there is more than one way to harness the power of data in the age of AI. Offering flexibility to those who may not yet be ready to fully embrace in-house models. 

Whether you’re contemplating an in-house AI model or looking to leverage tools like Google’s Vertex AI, it’s clear that having your data structured and ready for integration into AI models is an absolute necessity.  

But here’s the real question: Is your business ready to seize the opportunities these technological advances offer? 

Do you have the infrastructure in place to capitalise on your data and build powerful, secure, and private LLMs? 

If the answer is anything less than a confident ‘yes’, it’s time for action. 

At Teraflow, we’re experts in data, machine learning and AI infrastructures. We help companies prepare for the future, structuring and organising data for seamless integration into large language models. 

Don’t let your data go to waste – harness its full potential with Teraflow.  The future of AI is here. And the time to act is now. 

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