Sample Enterprise Private Cloud Workflow:
Rapidly Deployed Microservices
for optimized ad analytics algorithms

Incorporating sophisticated algorithms into highly automated computations within a cloud-based API framework, an internet analytics company develops a set of self-adaptive microservices for maximizing the effectiveness of ad buys, giving clients real-time ad optimization with no installation or configuration.

An Algorithm Optimization Workflow
Powered by Enterprise Private Cloud

1

Create Prototypes

Analysts work in desktop notebooks to design algorithms for optimization of advertising goals across various platforms, including inline documentation and examples.

Enterprise Private Cloud essentials for this step:

Hybrid desktop/cloud interface: Connect your Wolfram desktop interface to Enterprise Private Cloud to combine the familiarity of Mathematica with the flexible deployment and computation of the Wolfram Cloud.

Literate programming, available anywhere: Code, document and deploy in an intuitive, unified environment, streamlining implementation—familiar to both analysts and developers, and cloud accessible for immediate collaboration.


2

Implement and Test

Developers integrate algorithms into an Enterprise Private Cloud testing environment, performing read-only analysis with historical data and consulting with researchers to make the appropriate adjustments.

Enterprise Private Cloud essentials for this step:

Rapid development with world-class algorithms: Quickly design and implement optimizations using high-level machine learning and statistical capabilities in a streamlined development environment.

Instant high-powered APIs: Develop sophisticated optimization systems using advanced computation, and deploy them to cloud APIs for easy programmatic access.


3

Deploy to Production

New algorithms are added to the production API by changing database endpoints, providing clients with immediate access to the latest optimization services with no front end updates required.

Enterprise Private Cloud essentials for this step:

Centralized resource management: Seamlessly transition between testing and production with URL endpoint management, providing rapid service updates with minimal end-user action and immediate reversibility in case of problems.

High-performance computing in a scalable architecture: Combine built-in parallel programming capabilities with automated load balancing and process management for efficient, reliable computations at any scale.


4

Analyze and Iterate

Production algorithms automatically adjust to trends minute by minute, with researchers analyzing usage via web dashboards to make additional improvements and to innovate new algorithms in rapid succession.

Enterprise Private Cloud essentials for this step:

Automatic updating and maintenance: Use adaptive algorithms and scheduled background tasks to improve services frequently and without interruption, providing consistent results in volatile ad markets.

Integrated logging and tracking infrastructure: Automatically collect data on resource usage and algorithm performance, enabling meta analysis of services for additional quality control and performance tracking.


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