Primary Authors: Nishaanthini Gnanavel and Genevieve Richards Effective product quality control is of utmost importance in the manufacturing industry. The presence of defective components can have adverse effects on various aspects, including escalating production costs, compromising product quality, diminishing product longevity, and leading to substantial resource wastage. However, by promptly identifying defects and implementing suitable […]

Operationalizing models is critical for companies to get a return on their machine learning investments, but deployment is only one part of that operationalization process. With H2O.ai’s latest Snowflake Integration Application, authorized Snowflake users can easily deploy models, significantly reducing deployment timelines and enabling a level of self-service that creates faster time to value. H2O.ai […]

H2O.ai now provides data scientists and machine learning (ML) engineers even more powerful features that give greater control, governance, and scalability within their machine learning workflow – all available on our H2O AI Cloud. Now, H2O MLOps enables you to: Deploy model explanations in production Explainability is core to understanding ML model behavior and to deepen […]

Note: this is a community blog post by Shamil Dilshan Prematunga. It was first published on Medium. When we step into the AI application world it is not one easy step. It has a series of tasks that are combined. To convert an idea to the workable stage we must fulfill the requirements in each […]

For years machine learning (ML) researchers have focused on building outstanding models and figuring out how to squeeze every last drop of performance from them. But many have realized that creating top-performing models doesn’t necessarily equate to having them deliver business value. Often the best models can be very complex and costly to run in […]