- Data quality is one of the most crucial aspects of success for ML practitioners, and respondents rated it the most challenging to overcome.
- ML teams can overcome obstacles related to data curation and annotation quality by collaborating closely with annotation partners, thus speeding up model deployment.
The latest report by Scale AI uncovers what’s working and what’s not working with Artificial Intelligence (AI) implementation and the best practices for Machine Learning (ML) teams to move from testing to real-world deployment.
To understand where AI innovation is being stifled, where breakdowns occur, and what strategies are aiding businesses in achieving success, the report examines every stage of the ML lifecycle, from data collection and annotation to model creation, deployment, and monitoring.
The report’s objective is to continue to shed light on the realities of what it takes to realise AI’s full business potential and to assist organisations and ML practitioners in overcoming current obstacles, learning about and putting best practices into practice, and ultimately using AI to their advantage.
Data quality is one of the most crucial aspects of success for ML practitioners, and respondents rated it to be the hardest hurdle to overcome. More than one-third (37%) of the participants in this survey claimed they lacked the variety of data necessary to enhance model performance.
They claim that they lack not just diversity but quality as well – only nine per cent of respondents said their training data is free from noise, bias, and gaps.
No matter the industry or stage of AI development, most teams struggle with data diversity and quality issues.
According to Scale’s statistics, ML teams can overcome obstacles related to data curation and annotation quality by collaborating closely with annotation partners, thus speeding up model deployment.
ML teams that are not associated with annotation partners take longer than three months to receive annotated data.
Scale AI performed an online poll with American adults between March 31 and April 12, 2022. For the report, more than 1,300 ML professionals were polled, including those from Meta, Amazon, Spotify, and other companies.