What do you like best about the product?

IBM watsonx.ai stands out for its ability to streamline model development and deployment at scale. As a developer, I appreciate how it blends flexibility with structure, offering a wide range of model types from classical ML to large language models. The UI is clean, and the integration with existing cloud and security frameworks is straightforward, which helps in speeding up experimentation cycles without compromising governance.

What do you dislike about the product?

While the platform is powerful, it can feel overwhelming at first, especially when setting up more customized workflows. Additionally, pricing could be more transparent for users who are still exploring options before committing at an enterprise level. Improved onboarding tutorials for developers new to IBM's ecosystem would be a welcome addition.

What problems is the product solving and how is that benefiting you?

IBM watsonx.ai helps tackle the challenge of scaling AI models securely and efficiently in an enterprise environment. For me, it solves two major pain points: the difficulty of managing multiple models across projects and the need for better visibility into model performance over time. By centralizing development, deployment, and monitoring in one platform, it saves significant effort that would otherwise go into stitching different tools together. It also benefits my workflow by making model governance and compliance easier to manage without slowing down innovation.