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Asset Maintenance Prediction (AMP)

Solutions Gallery > Asset Maintenance Prediction (AMP)

Kogentix AMP: The foundation for practical AI fueled by big data

Building modern AI applications is far too difficult, leaving the promise of AI unfulfilled for most organizations. Fortunately, that’s about to change. Kogentix AMP makes it possible to build, manage, monitor, and deploy AI applications leveraging cost effective, scalable open source software. Business leaders and data scientists can focus on using their data for superior results, not scripting and managing a wide variety of ever changing tools. For data to have an impact, the entire data science process must be automated and operationalized. Kogentix AMP delivers.

Streamline decision management

Create workflows easily with a drag and drop interface for different types of models using R, Python, H2O, Spark Mllib or custom algorithms. With a scalable and intuitive process from registering, validating and retraining models, the platform enables you to trace models performance across algorithms and workflows. The net result – your insights are more actionable and more impactful.

Manage workflow effectiveness in production

It’s a reality that data changes and workflows must adapt or lose effectiveness. Kogentix AMP monitors the effectiveness of your workflows in production, and will alert you or even take action automatically to optimize the workflow with better algorithms.

Iterate freely with the performance of in-memory technology

Running AI applications in-memory on a horizontally scalable platform eliminates I/O bottlenecks from intermediate systems, enables workflow optimization on the largest dataset possible, and most importantly, enables rapid iteration to constantly improve results.

Key highlights

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About Kogentix
We founded Kogentix with one premise in mind. We knew that open source big data technologies (particularly the ever-evolving Hadoop stack) would play a critical role in transforming enterprises. That’s when we sat down and looked at the gaps in the industry. We filtered them down to three main issues.

 

 

 

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