
Why the Biggest Obstacle to Machine Learning Initiatives is Data
The success of industrial artificial intelligence rests on reliable data.
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CIO Applications Europe | Monday, April 03, 2023

Quality data is at the heart of the success of enterprise artificial intelligence (AI).
FREMONT, CA:The success of industrial artificial intelligence rests on reliable data. Because of this, it continues to be the key obstacle for businesses looking to integrate machine learning (ML) into their processes and applications.
According to Appen's most recent State of AI Report, the industry has made remarkable strides in assisting businesses in overcoming the challenges associated with locating and preparing their data. However, a great deal still needs to be done at all levels, including organisational structure and corporate policy.
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The Costs of Data:Four steps can be identified within the enterprise AI life cycle: Data sourcing, data preparation, model testing and deployment, and model evaluation.
Tasks like developing and testing various ML models have been sped up and automated thanks to developments in computing and machine learning tools. Numerous models of various sizes and architectures can be trained and tested concurrently using cloud computing systems. However, more training data will be needed when machine learning models multiply and expand in size.
Regrettably, gathering training data and annotating still involve a lot of human labour and are frequently application-specific. Teams don't have the necessary processes in place to easily and efficiently collect the data they need or lack sufficient data for a certain use case, new machine learning algorithms that demand bigger volumes of data.
The chief product officer of Appen stated to VentureBeat that high-quality training data is essential for correct model performance, and huge, inclusive datasets are expensive. Investing is necessary since excellent AI data can boost the likelihood that your project will move from the pilot stage to production.
Although ML teams can begin with prelabeled datasets, they will eventually need to gather and classify their unique data to grow their efforts. Labelling can be very expensive and labour-intensive, depending on the application.
Businesses have abundant data, yet they cannot address quality problems. The quality of ML models is decreased by biased, incorrectly labelled, inconsistent, or insufficient data, which hurts the return on investment of AI endeavours. Model predictions will be wrong if they train ML models with bad data. Teams must have a combination of high-quality datasets, synthetic data, and human-in-the-loop evaluation in their training kit to guarantee that their AI performs well in real-world scenarios.
The Gap Between Data Scientists and Business Leaders:According to Appen, business leaders are far less likely than technical personnel to view data preparation and sourcing as the primary hurdles of their AI initiatives. Technologists and business leaders still have different perspectives on the biggest obstacles to incorporating data for the AI lifecycle.
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