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What is BentoML?
Effortlessly launch your machine learning model in any cloud setting in just a few minutes. Our standardized packaging format facilitates smooth online and offline service across a multitude of platforms. Experience a remarkable increase in throughput—up to 100 times greater than conventional flask-based servers—thanks to our cutting-edge micro-batching technique. Deliver outstanding prediction services that are in harmony with DevOps methodologies and can be easily integrated with widely used infrastructure tools. The deployment process is streamlined with a consistent format that guarantees high-performance model serving while adhering to the best practices of DevOps. This service leverages the BERT model, trained with TensorFlow, to assess and predict sentiments in movie reviews. Enjoy the advantages of an efficient BentoML workflow that does not require DevOps intervention and automates everything from the registration of prediction services to deployment and endpoint monitoring, all effortlessly configured for your team. This framework lays a strong groundwork for managing extensive machine learning workloads in a production environment. Ensure clarity across your team's models, deployments, and changes while controlling access with features like single sign-on (SSO), role-based access control (RBAC), client authentication, and comprehensive audit logs. With this all-encompassing system in place, you can optimize the management of your machine learning models, leading to more efficient and effective operations that can adapt to the ever-evolving landscape of technology.
What is Amazon SageMaker Edge?
The SageMaker Edge Agent is designed to gather both data and metadata according to your specified parameters, which supports the retraining of existing models with real-world data or the creation of entirely new models. The information collected can also be used for various analytical purposes, such as evaluating model drift. There are three different deployment options to choose from. One option is GGv2, which is about 100MB and offers a fully integrated solution within AWS IoT. For those using devices with constrained capabilities, we provide a more compact deployment option built into SageMaker Edge. Additionally, we support clients who wish to utilize alternative deployment methods by permitting the integration of third-party solutions into our workflow. Moreover, Amazon SageMaker Edge Manager includes a dashboard that presents insights into the performance of models deployed throughout your network, allowing for a visual overview of fleet health and identifying any underperforming models. This extensive monitoring feature empowers users to make educated decisions regarding the management and upkeep of their models, ensuring optimal performance across all deployments. In essence, the combination of these tools enhances the overall effectiveness and reliability of model management strategies.
Integrations Supported
Amazon SageMaker
Amazon Web Services (AWS)
Amazon SageMaker Unified Studio
Apache Spark
Azure Container Registry
Azure Functions
Docker
Google Cloud Run
Google Compute Engine
Grafana
Integrations Supported
Amazon SageMaker
Amazon Web Services (AWS)
Amazon SageMaker Unified Studio
Apache Spark
Azure Container Registry
Azure Functions
Docker
Google Cloud Run
Google Compute Engine
Grafana
API Availability
Has API
API Availability
Has API
Pricing Information
Free
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
BentoML
Company Location
United States
Company Website
www.bentoml.com
Company Facts
Organization Name
Amazon
Date Founded
2006
Company Location
United States
Company Website
aws.amazon.com/sagemaker/edge/
Categories and Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
Categories and Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
