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Model Simulation

Why Model Management?

01

Efficient Deployment

HexagonML streamlines the deployment of AI models into production environments, ensuring compatibility and efficiency. This is crucial for minimizing downtime and ensuring that AI solutions are operational and reliable.

02

Scalability

HexagonML equips businesses with the tools and processes to scale AI models as their data loads and user demands increase. This scalability is essential for organizations experiencing growth and evolving requirements.

03

Continuous Improvement

AI models can become less accurate over time without proper maintenance. HexagonML emphasizes the continuous training and updating of models, ensuring that they remain relevant and deliver high-quality results as new data becomes available.

Meet Hexagon ML

OUR STORY

We are in a digital era where we have all data and storage needed to process information.  However we are limited with time. Today, every industry's goal should be to improve their competitive  capability and maximize information from data. We believe every company has the potential to achieve this. Our platforms enable and engage company's employees to participate and improve upon business predictive goals, whether its predicting or forecasting the sales for next year or churn rate. 

Data Analyst

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Model management is a critical practice in the realm of data science and artificial intelligence. It involves the systematic organization, deployment, monitoring, and maintenance of machine learning and AI models throughout their lifecycle. Effective model management ensures that these models perform optimally, remain up-to-date, and align with evolving business needs. It encompasses various aspects, from version control and scalability to compliance and collaboration between data scientists and IT operations teams. Ultimately, model management is instrumental in harnessing the full potential of AI technologies to drive data-driven decision-making and innovation in organizations across diverse industries.

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  • Identify when to re-train the models

  • Meta insights combining multiple models

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Transparency

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Trustbility

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Explainability

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Governance

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