MLOps strategies, reducing time-to-scale ML Models, and more!
Scaling AI continues to be a significant challenge, a Gartner survey revealed that only 54% of AI projects make it from pilot to production. This can be attributed to the impediments that technology and business leaders face in moving ML models to production. Read our whitepaper to learn how businesses can employ a robust framework for MLOps that seamlessly allows the integration of AI solutions with existing live applications.
Build, train and deploy ML models at scale.
ML model productionizing refers to hosting, scaling, and running an ML Model on top of relevant datasets. ML models in production also need to be resilient and flexible for future changes and feedback. However, ML modeling is hard to translate into active business gains. A plethora of engineering, data, and business concerns become bottlenecks while handling live data and putting ML models into production. Read our bog on the top 5 challenges that organizations face In order to understand the common pitfalls of productionizing ML models.
Learn the best practices to deploy ML Models
Sigmoid built a unique AI deployment environment for a leading CPG brand that allowed multiple business teams to run ML models and scale them across various departments and geographies. It ensured faster deployment of ML models and reduced the cost of running models by 2x.
Ensure faster deployment of ML models
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