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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Deploy your machine learning model in the cloud within minutes using a consolidated packaging format that supports both online and offline operations across various platforms. Experience a performance boost with throughput that is 100 times greater than traditional flask-based model servers, achieved through our innovative micro-batching technique. Provide exceptional prediction services that align seamlessly with DevOps practices and integrate effortlessly with widely-used infrastructure tools. The unified deployment format ensures high-performance model serving while incorporating best practices for DevOps. This service utilizes the BERT model, which has been trained with the TensorFlow framework to effectively gauge the sentiment of movie reviews. Our BentoML workflow eliminates the need for DevOps expertise, automating everything from prediction service registration to deployment and endpoint monitoring, all set up effortlessly for your team. This creates a robust environment for managing substantial ML workloads in production. Ensure that all models, deployments, and updates are easily accessible and maintain control over access through SSO, RBAC, client authentication, and detailed auditing logs, thereby enhancing both security and transparency within your operations. With these features, your machine learning deployment process becomes more efficient and manageable than ever before.

Description

A closed-loop universal multivariable optimizer is designed to enhance both the performance and quality of Model Predictive Control (MPC) systems. This optimizer utilizes data from Excel files sourced from Dynamic Matrix Control (DMC) by Aspen Tech, Robust Model Predictive Control Technology (RMPCT) from Honeywell, or Predict Pro from Emerson to develop and refine accurate models for various multivariable-controller variable (MV-CV) pairs. This innovative optimization technology eliminates the need for step tests typically required by Aspen Tech and Honeywell, operating entirely within the time domain while remaining user-friendly, compact, and efficient. Given that Model Predictive Controls (MPC) can encompass tens or even hundreds of dynamic models, the possibility of incorrect models is a significant concern. The presence of inaccurate dynamic models in MPCs leads to bias, which is identified as model prediction error, manifesting as discrepancies between predicted signals and actual measurements from sensors. COLUMBO serves as a powerful tool to enhance the accuracy of Model Predictive Control (MPC) models, effectively utilizing either open-loop or fully closed-loop data to ensure optimal performance. By addressing the potential for errors in dynamic models, COLUMBO aims to significantly improve overall control system effectiveness.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon EC2 Yes 
Amazon SageMaker Yes 
Amazon Web Services (AWS) Yes 
Apache Airflow Yes 
Apache Spark Yes 
Aspen DMC3 No 
Azure Container Registry Yes 
Google Cloud Run Yes 
Google Compute Engine Yes 
Grafana Cloud Yes 
H2O.ai Yes 
Keras Yes 
Knative Yes 
Kubernetes Yes 
Microsoft Excel No 
Prometheus Yes 
PyTorch Yes 
Swagger Yes 
TensorFlow Yes 
ZenML Yes 

Integrations

Amazon EC2 No 
Amazon SageMaker No 
Amazon Web Services (AWS) No 
Apache Airflow No 
Apache Spark No 
Aspen DMC3 Yes 
Azure Container Registry No 
Google Cloud Run No 
Google Compute Engine No 
Grafana Cloud No 
H2O.ai No 
Keras No 
Knative No 
Kubernetes No 
Microsoft Excel Yes 
Prometheus No 
PyTorch No 
Swagger No 
TensorFlow No 
ZenML No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

BentoML

Country

United States

Website

www.bentoml.com

Vendor Details

Company Name

PiControl Solutions

Country

United States

Website

www.picontrolsolutions.com/products/columbo/

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

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