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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

KitOps serves as a robust system for packaging, versioning, and sharing AI/ML projects, leveraging open standards to seamlessly integrate with existing AI/ML, development, and DevOps tools, while also being compatible with your enterprise container registry. It has become the go-to choice for platform engineering teams in the AI/ML domain seeking a secure method for packaging and managing their assets. With KitOps, you can create a comprehensive ModelKit for your AI/ML projects, encapsulating all elements necessary for local reproduction or production deployment. Additionally, the ability to selectively unpack a ModelKit allows team members to optimize their workflow by only accessing the components pertinent to their specific tasks, thereby conserving both time and storage resources. Given that ModelKits are immutable, can be signed, and reside within your established container registry, they provide organizations with an efficient means of tracking, controlling, and auditing their projects, ensuring a streamlined workflow. This innovative approach not only enhances collaborative efforts but also fosters consistency and reliability across AI/ML initiatives.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

No images available

Integrations

AWS Lambda Yes 
Amazon EC2 Yes 
Amazon SageMaker Yes 
Amazon Web Services (AWS) Yes 
Apache Spark Yes 
Azure Container Registry Yes 
Azure Functions Yes 
Docker Yes 
Google Compute Engine Yes 
Grafana Cloud Yes 
H2O.ai Yes 
Heroku Yes 
Keras Yes 
Knative Yes 
Kubernetes Yes 
NVIDIA DRIVE Yes 
Prometheus Yes 
Swagger Yes 
TensorFlow Yes 
ZenML Yes 

Integrations

AWS Lambda No 
Amazon EC2 No 
Amazon SageMaker No 
Amazon Web Services (AWS) No 
Apache Spark No 
Azure Container Registry No 
Azure Functions No 
Docker No 
Google Compute Engine No 
Grafana Cloud No 
H2O.ai No 
Heroku No 
Keras No 
Knative No 
Kubernetes No 
NVIDIA DRIVE No 
Prometheus No 
Swagger No 
TensorFlow No 
ZenML No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version Yes 

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

Customer Support

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

Customer Support

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

Types of Training

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

Types of Training

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

Vendor Details

Company Name

BentoML

Country

United States

Website

www.bentoml.com

Vendor Details

Company Name

KitOps

Founded

2024

Country

Canada

Website

kitops.ml

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 

Product Features

DevOps

Approval Workflow No 
Dashboard No 
KPIs No 
Policy Management No 
Portfolio Management No 
Prioritization No 
Release Management No 
Timeline Management No 
Troubleshooting Reports No 

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 

Alternatives

Alternatives