How fast your data science team can develop, test and automate analytic models is a major factor in how fast you can innovate and respond to the market.
Three major issues result in lost opportunity around data science and analytics:
Analytic Ops provides a framework that enables Data Scientists to use tools and libraries of their choosing to develop and train models.
The models then follow an automated journey to production, leveraging best practices from software engineering & DevOps, with governance and metadata management that scales to an enterprise grade level.
3-Part Approach To Delivery:
Significant benefits of Analytic Ops:
Analytic Ops Benefits multiple departments:
We stand ready to make Analytic Ops a reality for you with our toolkits, processes, best practices and accelerators.
Our Analytic Ops Accelerator supports multiple training & execution platforms, including: Hadoop, GPU, Microsoft Analytics, KNIME, Yellowfin, etc. as well as multiple languages including R, Python, SAS, TensorFlow, Caffe, Theano, etc.
Our tool also automates the analytics productionization process, including: training, validation, performance testing, A/B testing, approvals and deployment.
Our accelerator provides a methodology to regularly and automatically update the models based on new incremental data.
Our solution integrates with horizontally scalable clusters to allow for true deep learning on the historical data.
Our solution contains a simple interface for allowing citizen data scientists to modify, train, validate, and deploy models to production.