AI Strategy Data Quality Machine Learning 
AI News
SEPTEMBER 27, 2024
What we are seeing in the Data world in general is continued investment in data and analytics software. Analysts estimate that the spend on Data and Analytics software last year was in the $100 billion plus range. Second, is data quality and accessibility, the quality of the data is critical.
IBM Journey to AI blog
DECEMBER 20, 2023
By giving machines the growing capacity to learn, reason and make decisions, AI is impacting nearly every industry, from manufacturing to hospitality, healthcare and academia. Without an AI strategy, organizations risk missing out on the benefits AI can offer. What is an AI strategy?
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AWS Machine Learning Blog
FEBRUARY 27, 2024
In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. Workflow B corresponds to model quality drift checks.
IBM Journey to AI blog
SEPTEMBER 3, 2024
.” For example, synthetic data represents a promising way to address the data crisis. This data is created algorithmically to mimic the characteristics of real-world data and can serve as an alternative or supplement to it. In this context, data quality often outweighs quantity.
Unite.AI
OCTOBER 4, 2023
Hence, it is vital to rapidly minimize issues present in Generative AI technologies. Several key strategies can be implemented to reduce bias in AI models. Some of these are: Ensure Data Quality: Ingesting complete, accurate, and clean data into an AI model can help reduce bias and produce more accurate results.
ODSC - Open Data Science
FEBRUARY 28, 2023
Cloudera For Cloudera, it’s all about machine learning optimization. Their CDP machine learning allows teams to collaborate across the full data life cycle with scalable computing resources, tools, and more.
Unite.AI
MARCH 14, 2024
The demand for high-quality training data is intensifying , with 66% of respondents anticipating an increase in their training data needs over the next two to five years. This underscores the critical role of data in training more sophisticated and accurate AI models.
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