Adapdix, a specialist in Edge Automation software with its AI-powered automation technology, announced a subsidised program to provide its customers with access to qualified data scientists in order to help overcome the worldwide shortage of skilled staff required for the implementation of Artificial Intelligence (AI) and Machine Learning (ML).
Adapdix is utilising its existing group of skilled employees and advanced AI/ML edge modeling software to meet the robust customer demand for AI/ML projects. This much-needed resource is then made available to Adapdix customers at a subsidised rate of $5 (€4.25) per hour. No long-term commitment is required, meaning that anyone involved in an active proof of concept, pilot, or production deployment can benefit from the program.
“To help meet customer demand despite these skill shortages, we at Adapdix have launched a new program. This taps into the talent of Adapdix employees and an ecosystem of service partners that we’ve evaluated, trained, and certified to provide value,” says Anthony Hill, CEO at Adapdix.
“This program involves only skilled, well-qualified people that work together with Adapdix employees, which ensures work quality remains high. By offering subsidised EdgeOps experts, we intend to help companies accelerate their digital transformation and raise their implementation of AI/ML to the next level.”
According to IDC, it is estimated that from 2020 to 2027, demand for data scientists will grow at a rate of 26.9% CAGR. QuantHub has been tracking the ongoing shortage and has found that in 2020 there was a shortfall of 250,000 data scientists, with 67% of companies taking steps to expand their data science teams.
Adapdix recently announced EdgeOps DataMesh, the first product of its next-generation AI-powered automation software platform EdgeOps. Adapdix software is used by many customers throughout industry, including two of the top five semiconductor manufacturers, to improve their manufacturing performance.
EdgeOps DataMesh overcomes the typical hurdles of real-time operational data management by performing data ingestion, pre-processing and edge inferencing in millisecond timeframes, thus enabling real-time analysis to improve operational efficiency and reduce downtime of high-value assets.
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