Operationalizing Analytics

image22

Lost Opportunity

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:

Models Never Make It Out of the Sandbox

  • Leading researchers agree that only 15% of data science projects are actually deployed into production

Decay

  • Those fortunate 15% that do make it into production will decay or spoil over time if they are not consistently refined and tuned

Working With Outdated Tools

  • Familiarity with technologies makes doing the data science work fast, but it also keeps the analytics team from utilizing the latest tools available

image23

Bringing Two Worlds Together

Where Analytics Meets Operations

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:


Development

  • Conduct data wrangling & feature engineering
  • Build data pipelines for model inputs via templates
  • Create & test models in development environment(s)
  • Model business process configuration (scheduling, reports & approvals)
  • “Check-in” models & dependencies to trigger automation


Automation

  • Run unit tests for data pipelines  
  • Run automated model validation & performance tests  
  • Execute champion / challenger model evaluation  
  • Create model / business reports for approval process  
  • Store model metadata, artifacts, & performance metrics


Consumption (Production)

  • Deploy data pipelines & real-time queues for new models  
  • Deploy model artifacts to execution environment (scoring engine, web service, etc.)   
  • Integrate models into business process flows  
  • Apply A/B or gradual deployment strategies for new models artifacts  
  • Log outputs for continuous monitoring & improvement 

Value Proposition

Significant benefits of Analytic Ops:

  • Reduce time to market
  • Improve quality of product
  • Reduce maintenance overhead
  • Ensure auditability and regulatory compliance


Analytic Ops Benefits multiple departments:

  • Business end users receive a better product faster
  • Data systems owners enjoy improved governance & clearer processes
  • Data Science internal teams experience optimization and automation of processes along with flexibility to explore, model, ideate, etc.
  • IT Support saves money from reduced operational overhead


We stand ready to make Analytic Ops a reality for you with our toolkits, processes, best practices and accelerators.

Schedule a Demo

Key Features Of Our Accelerators

Technology Agnostic

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.

Automated Orchestration

Our tool also automates the analytics productionization process, including: training, validation, performance testing, A/B testing, approvals and deployment.

Self Learning

Our accelerator provides a methodology to regularly and automatically update the models based on new incremental data. 

Scalable Machine & Deep Learning

Our solution integrates with horizontally scalable clusters to allow for true deep learning on the historical data.

Intuitive GUI

Our solution contains a simple interface for allowing citizen data scientists to modify, train, validate, and deploy models to production.