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Pros and Cons of Citizen Data Scientists
Organizations in almost every sector are actively working to see how they can leverage AI and ML to accelerate their business and achieve greater outcomes. In some circles, citizen data scientists have been widely promoted as being the solution to help organizations accelerate their ML/AI journey.
But as the saying goes, caveat emptor. While there are some benefits to having citizen data scientists, they are no silver bullet – and they certainly aren’t a replacement for true data scientists.
Datatron 3.0 Product Release – Enterprise Feature Enhancements
Streamlined features that improve operational workflows, enforce enterprise-grade security, and simplify troubleshooting.
Datatron 3.0 Product Release – Simplified Kubernetes Management
Eliminate the complexities of Kubernetes management and deploy new virtual private cloud environments in just a few clicks.
Datatron 3.0 Product Release – JupyterHub Integration
Datatron continues to lead the way with simplifying data scientist workflows and delivering value from AI/ML with the new JupyterHub integration as part of the “Datatron 3.0” product release.
Success Story: Global Bank Monitors 1,000’s of Models On Datatron
A top global bank was looking for an AI Governance platform and discovered so much more. With Datatron, executives can now easily monitor the “Health” of thousands of models, data scientists decreased the time required to identify issues with models and uncover the root cause by 65%, and each BU decreased their audit reporting time by 65%.
Success Story: Domino’s 10x Model Deployment Velocity
Domino’s was looking for an AI Governance platform and discovered so much more. With Datatron, Domino’s accelerated model deployment 10x, and achieved 80% more risk-free model deployments, all while giving executives a global view of models and helping them to understand the KPI metrics achieved to increase ROI.
5 Reasons Your AI/ML Models are Stuck in the Lab
AI/ML Executive need more ROI from AI/ML? Data Scientist want to get more models into production? ML DevOps Engineer/IT want an easier way to manage multiple models. Learn how enterprises with mature AI/ML programs overcome obstacles to operationalize more models with greater ease and less manpower.
Life Cycle of Machine Learning Models
Production-grade machine-learning models require strong deployment framework in order to reduce the time it takes to iterate a model faster, deploy new features quickly, and train on incoming data faster.