iMerit Technology
Advancing AI Starts with Strategic Machine Learning Data Operations

Radha Basu, Founder and CEO

Machine learning is the backbone of many AI applications, but the real ground truth of AI relies on leveraging the right AI data solutions and high-quality data. A recent iMerit survey reveals that nearly 93 percent of data professionals indicated that high-quality annotated data is essential to the long-term success of machine learning. “As companies reach production and begin to scale, they must navigate new data challenges,” said iMerit founder and CEO Radha Basu.

“This requires refinement of existing AI models and inherently realizes the value of using high-quality data throughout the machine learning data operations feedback cycle.”

Radha’s perspective rings true, aligning with the thoughts of world-renowned data scientists Andrew Ng and DJ Patil, who advocate for a more data-centric approach to AI, moving away from model-centric practices. The bottom line is, companies using high quality, precise data in machine learning data operations dictates the best outcomes for their AI applications.

iMerit has a ringside seat into this new frontier of solving data problems or edge cases for large enterprises across autonomous mobility, healthcare AI, geospatial technology, social media, and many more.

Solving Edge Cases: The Path to Accelerating AI

Companies leveraging AI data solutions founded on the premise of utilizing human intelligence and technology to manage their ML data ops will gain the competitive edge in the last mile of AI production. A recent iMerit survey reveals that nearly 96 percent of data professionals believe that leveraging human intelligence and insights is critical to solving edge cases and advancing AI. Through its work, iMerit sees this again and again, particularly across its top autonomous mobility clients.

Taking an autonomous vehicle fleet to production with safety as a top priority is no small task. Once in production, autonomous vehicles are faced with unique on-road scenarios or edge cases that are tricky for the ML to navigate.For example, the ML may struggle to distinguish between a person on the road from a store-front window reflection of a person on the road, further requiring a human to teach it how to make the right decision.

As companies reach production and begin to scale, they must navigate new data challenges. This requires refinement of existing AI models and inherently realizes the value of using high-quality data throughout the machine learning data operations feedback cycle

As an end-to-end AI data solutions company, iMerit combines technology, and experts in the loop, to fully support enterprises’ machine learning data operations strategy that ultimately powers their AI applications.

Three Key Elements to AI Data Solutions: People, Processes and Technology

Companies in the proof-of-concept stage require large volumes of data to prove their ML model. But, as companies reach production, the volumes of data and data precision take a whole new level of effort. AI-forward companies may be best suited to lean AI data solution providers like iMerit to leverage the three key elements people, processes, and technology required to successfully deploy AI in the marketplace.

People are needed to advance AI:

AI cannot achieve human-like intelligence without leveraging human intelligence to learn, and in many cases, domain experts are needed to train the AI. Talent, expertise, and experience have a major impact on the quality and precision of data needed to solve complex problems iMerit employs highly skilled data annotation experts, including linguistic Ph.D. professionals, medical doctors, and more, to help companies obtain the high-quality structured data required to accelerate their AI.

Using the right processes across ML data ops is key

Technology is only as good as one’s ability to use it properly, which is why enterprises building AI applications must leverage the right processes across their ML data ops.Leaning on AI data solutions providers like iMerit gives companies access to domain experts that can guide every phase of a company’s ML data ops process including requirements definition, workflow engineering, technology and tool selection, talent identification, execution, evaluation and refinement, and analytics.

The adoption of AI data solutions and technology infrastructure like iMerit Data Studio is a critical path to creating the high quality data needed to bring AI to market at scale. iMerit Data Studio delivers companies high quality data at scale, enables them to rapidly scale data annotation teams, leverages experts in the loop across a variety of industries, uses the right annotation tools across the ecosystem, and gives flexibility in data formats and access to quality metrics to analyze results along the way.

iMerit’s Founder and CEO, Radha Basu, Shares Her Journey into the AI and ML industry.

In 2012, Radha Basu founded iMerit on the belief that profitable technology companies can be built on goals that balance financial success while effectuating a positive societal impact. Today, iMerit is a leading AI data solutions company delivering high-quality data that powers machine learning and artificial intelligence applications for Fortune 500 companies. Radha led iMerit through its first two funding rounds, raising 23.5 million dollars to date from investors, and continues propelling the company to new revenue heights.

iMerit’s founding tenets set it apart in the AI and ML industry, and its humanistic approach toward advancing AI while promoting a better quality of life for people everywhere is quite innovative and inspirational. “iMerit employs more than 5,500 highly skilled employees who are trained to help solve the pain points many AI-forward companies experience in their machine learning data operations,” says Radha. “iMerit pairs people, processes, and technologies to help its clients bring their AI applications to market faster and at scale.”

But, Radha’s story as a software engineer and IT leader began long before iMerit. Radha was among the first IT influencers to propel Bangalore, India, with the moniker ‘The Silicon Valley of India.’ As a senior leader at Hewlett Packard Labs in Bangalore during the late 80s, Radha was responsible for setting up HP’s operations and its first-ever software center in India. Alongside companies like Texas Instruments and Infosys, she helped pioneer new technologies in the country. Radha eventually moved to the U.S. to continue her 20-year career at HP, where she was responsible for overseeing and growing its Electronic Business Software Division to 1.2-billion dollars.

In 1999, Radha became the Chairman and CEO of SupportSoft, where she led the company through initial and secondary public offerings in 2000 and 2003, and built it into a worldwide market leader in support automation software. In 2006, Radha joined her husband, Dipak Basu, to start the Anudip Foundation, a non-profit organization that trains young people in the world of technology and places them in data related jobs