MindsDB drives AI for open source machine learning 

The democratisation of technology paves the way for its evolution, or so say the more upbeat and outspoken evangelists in this space.

We might respectfully suggest that this statement is more true for AI than just about any other field. 

You know what they say i.e. a rising tide lifts all boats. The more hands on deck, the easier it is to craft and refine tools and applications, the faster we progress as collective users of AI. When one team has a breakthrough, all teams eventually benefit.  

MindsDB insists that it is all about making those breakthroughs easier to come by.  

When the company was founded, the overarching aim of MindsDB was to speed up the process of building AI-powered applications. Almost six years, 13,000 GitHub Stars, and more than 70 technology data integrations later, MindsDB is becoming a developer platform of potential interest for some.

“Over the past 20 years, we’ve seen important revolutions in the world of SaaS, from the rise of web applications, to the growth of mobile apps. Now, it’s time for the next revolution in the world of technology: the reinvention of all the systems we use on a daily basis, like email, calendars, CRMs, ERPs, CMSs, telehealth, office documents etc. , all with AI capacities at their core,” said Jorge Torres, co-founder and CEO of MindsDB. 

Torres says that with MindsDB, developers can create the next wave of AI-centered applications.

Shared ML models

MindsDB recently initiated integrations with Hugging Face and OpenAI, both of which aim to share machine learning models and datasets for the betterment of the technology as a whole. These integrations will allow anyone to bring Natural Language Processing (NLP) and generative AI models into their database with a few lines of SQL. 

The company claims that this technology provides a little-to-no-code solution to help companies leverage cutting AI capabilities for their own specific needs.

According to co-founder Torres and team, “With Hugging Face, for instance, developers will be able to access pre-trained NLP models and apply them to their own data for use cases such as advanced text classification, sentiment analysis, emotion detection, translation etc.”

With the OpenAI integration, developers can use and customise generative AI models including GPT-3, Codex and DALL·E, to derive greater meaning and context from their data.  

“The key here is the nurturing of an open source culture around the adoption and innovation of AI. Open source is a form of shared learning that propels technology to new heights – if one company finds a new use case or a solution to a problem, the learning from that solution can be shared and widely benefited from,” noted the company, in a press statement.  

MindsDB claims to be enabling developers to create the next wave of AI-centered applications. But there is a realisation here that fast-tracking those AI use cases will require a broader application of machine learning. 

“Until recently, ML was the preserve of companies with established in-house expertise and endless budgets to match. These organisations could afford to write carefully customised ML algorithms line-by-line for specific purposes. Only around a quarter of businesses say that creating an ML model took them less than a month, whereas almost half say it would take them up to a whole year. This is creating a bottleneck, where countless businesses see the value in ML but few are able to truly capitalize on it,” said Torres and team.

However, the next chapter of ML may be different. 

Instead of requiring users to fully understand the detail behind how models are trained and utilised for obtaining predictions or having a team of coding experts on-hand to tailor and tweak algorithms to suit a particular use case, smaller teams and even those without coding skills will be able to deploy and utilize ML to their advantage. 

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