There are different generations of tools when it comes to automated machine learning. Legacy tools such as AWS Sagemaker, Google AutoML etc have existed for quite some time now and the key goal of these tools was to help machine learning engineers and people that are technical themselves.

But, now there are even better tools. Nirman Dave, CEO at Obviously AI claimed that their models are better than those of AWS Sagemaker or Google AutoML. 

Based in San Francisco, Obviously AI specialises in building no-code AI models for businesses. Dave founded the startup with a mission to transform every company into an AI company, boasting of having developed the most rapid and accurate no-code AI tool to date. 

“So what happens is that the customer in just a few minutes gets access to these machine learning models that they can build or customise for themselves,” explained Dave.

However, on the other hand, legacy tools that have been around for nearly 13-15 years now and are very slow at building models. “What’s really special about what we do is we build the models in less than a minute. So we’re the fastest tool to build AI models today” Dave added.

Nonetheless, Dave also acknowledges that his company is not the only one building similar tools. But even though there are other companies that have built no-code AI tools, their approach is very different. 

“We mostly focus on tabular data, supervised learning. We have now kind of branched into unsupervised learning as well with our custom Large Language Models (LLMs) offering.”

But Levity AI, for example, another company building no-code AI tools, focuses mostly on image, video or audio type of data. 

Demand

Founded in 2021, so far Obviously AI has boarded 52 customers. Even though most of them are in the US, the startup  has customers in India, Japan, and also South Africa.

“Currently, week over week we’re growing about 15% in terms of customer acquisition,” he said.

One of the big customers the startup has in India is a large consumer bank. Dave revealed that they are building a loan repayment model for this bank. Normally, the underwriting process for the loans takes a significant amount of time when done manually.

“They wanted to build a model that can quickly process the loan, predict default probability and give it out to the underwriters to make a decision.” Dave said.

Manufacturers of AI models

The startup is engaged with mostly mid-market businesses that don’t have a data science team or cannot scale one and also enterprise businesses that might have a backlogged data science team.

“Essentially, we are manufacturers of AI models,” Dave said. The process begins with the customer bringing a collection of data to our platform and specifying their desired prediction or AI model based on that data. 

“And we help with everything from data cleaning, to model selection to hyper parameter tuning, to model deployment and management, all of that process is done automatically by the system,” Dave added.

The user interface is also designed for people that are not heavily technical. Further, another thing that Obviously AI provides, and which they excel in, is dedicated data science support.

“So essentially, we work with a lot of data scientists. These are individual practitioners, individuals that run their own consulting firms or folks that are just starting out, and we provide them with connections to these customers that we have,” Dave said.

Impact on jobs

On the contrary, there is a growing concern that no-code tools could have a negative impact on jobs, particularly those that involve coding and software development. As these tools become more sophisticated and accessible, they may reduce the need for traditional coding skills in certain roles, leading to a shift in the job market. 

However, Dave believes Obviously AI’s no-code tool is not replacing data scientists, instead it is accelerating the data science process. 

“The reason companies like Hewlett-Packard use us is not because they want to get rid of their data science team. It’s actually that they want to accelerate the data science team. 

“The goal here is to help them move significantly faster. The data science team is going to take two months to get from raw data to insights and analytics and predictions. Now it’s going to take them a week of a couple of days.”

 Besides, a data scientist’s job is not solely to build AI models; but to build a strategy. 

“So I don’t think data scientists will be replaced. I think no-code tools will only help them focus more on the strategy which is the most exciting part of the job,” Dave concluded.