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ease
features
design
support

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Description

Amazon SageMaker equips users with an extensive suite of tools and libraries essential for developing machine learning models, emphasizing an iterative approach to experimenting with various algorithms and assessing their performance to identify the optimal solution for specific needs. Within SageMaker, you can select from a diverse range of algorithms, including more than 15 that are specifically designed and enhanced for the platform, as well as access over 150 pre-existing models from well-known model repositories with just a few clicks. Additionally, SageMaker includes a wide array of model-building resources, such as Amazon SageMaker Studio Notebooks and RStudio, which allow you to execute machine learning models on a smaller scale to evaluate outcomes and generate performance reports, facilitating the creation of high-quality prototypes. The integration of Amazon SageMaker Studio Notebooks accelerates the model development process and fosters collaboration among team members. These notebooks offer one-click access to Jupyter environments, enabling you to begin working almost immediately, and they also feature functionality for easy sharing of your work with others. Furthermore, the platform's overall design encourages continuous improvement and innovation in machine learning projects.

Description

Manage and optimize models throughout the entire ML lifecycle. This includes experiment tracking, monitoring production models, and more. The platform was designed to meet the demands of large enterprise teams that deploy ML at scale. It supports any deployment strategy, whether it is private cloud, hybrid, or on-premise servers. Add two lines of code into your notebook or script to start tracking your experiments. It works with any machine-learning library and for any task. To understand differences in model performance, you can easily compare code, hyperparameters and metrics. Monitor your models from training to production. You can get alerts when something is wrong and debug your model to fix it. You can increase productivity, collaboration, visibility, and visibility among data scientists, data science groups, and even business stakeholders.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon SageMaker
Amazon Web Services (AWS)
PyTorch
Python
TensorFlow
Apache Spark
Axolotl
Clone Protocol
CogniSync
Flask
GitHub
Google Cloud Platform
IBM Cloud
Microsoft Azure
New Relic
Plotly Dash
R Markdown
Seldon
Ultralytics
lemwarm

Integrations

Amazon SageMaker
Amazon Web Services (AWS)
PyTorch
Python
TensorFlow
Apache Spark
Axolotl
Clone Protocol
CogniSync
Flask
GitHub
Google Cloud Platform
IBM Cloud
Microsoft Azure
New Relic
Plotly Dash
R Markdown
Seldon
Ultralytics
lemwarm

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

$179 per user per month
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/sagemaker/build/

Vendor Details

Company Name

Comet

Founded

2017

Country

United States

Website

www.comet.com

Product Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Product Features

Data Science

Access Control
Advanced Modeling
Audit Logs
Data Discovery
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Alternatives

Alternatives

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