Data Science Model Deployment

Data Science Model Deployment Challenges

  • Corporate investment data science and AI teams is rising, yet they still struggle to realize value. According to Forrester Consulting, only 22% of enterprise companies are currently seeing a significant return from data science expenditures.
  • Teams have a myriad of tools to create models, but deployed model (a unit of code and configuration) must be seamlessly integrated into a production environment
  • Teams need the capabilities to iterate on models currently in production in order to improve their performance.
  • Teams need to monitor and test the detailed performance of production models and compare the performance of new candidate models without risk to IT security or SLAs.
  • A model is not an island. Models are part of complex pipelines of data integration, monitoring, and batch or real time interactions.
  • Data scientists must use their favorite build models, often through experimentation and iteration. They leverage their favorite statistical languages, such as Python or R, as well as frameworks for developing machine learning and deep learning models, such as Spark, TensorFlow, Caffe, and Theano.

Environment Management and Our Open Source 'Analytic Container'

Data Science Deployment Model Analytic Container

With the help of our open source Analytic Container Specification and Docker, we create an ideal data science environment, equipped with whatever tools - either open-source or proprietary - your data scientists prefer.

Your data scientists can then create as many development, test, and ad hoc environments as they need from that base Analytic Container.

We store and inject the code and configuration in that Analytic Container, run the model, and extract operation test results.

Resource Management and our DataOps Platform

Data Science Deployment Model Resource Management

IT teams are also commonly tasked with managing the resources needed by data scientists to run models and build analyses.

Providing your IT team with a platform that supports multi-tenancy can provide optimal resource utilization and the ability to segregate test data and processing from production workloads, if desired.

Sandboxing before production is a safe and efficient way to move a data scientists new model into production. After validating on live data, operations can quickly move new models into production while sharing the results in a 'Kitchen'.

Model Management in our DataOps Platform

Data Science Deployment Model Model Management

DataOps Platform: The various Machine Learning models, management, people (and their tools) are tied together cohesively using a technical environment called a DataOps Platform. The DataOps Platform includes support for:
Execution of the data pipeline (orchestration) to keep the model up-to-date
Deployment of new models
Testing and monitoring of model quality
Management of development and production environments
Source-code version control
Model operations reporting and dashboards

Our Solution

Data Science Deployment Model Speed ideas to production.

Our DataKitchen DataOps platform enables the quick iterations and deployment new predive models into production while monitoring model quality. We move data science model pipelines into production, automates orchestration, and guarantee machine learning performance.

We provide simple, single platform where data scientists, data engineers, IT Operations, and business analysts come together to automate and optimize machine learning across the enterprise:

Orchestrate from data, to ML model, to customer value. We have an integrate data and model pipeline with integrated quality checks to make sure bad results does not slip into your customers' hands or systems. Our platform also manage the code produced by your tools.

Deploy ideas to production - Our platform will help you get your new model into production quickly by simplifying the process of branching & merging code, creating and managing work environments, automating deployment, parameterizing the process, and promoting re-use.

Automate and monitor quality - while orchestrating data to customer value or deploying ideas to production our platform provides a framework for automated tests so you can work with confidence.

model deploy

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