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Description
Model Garden serves as a catalog for AI and machine-learning models within the Google Cloud AI platform. It assists teams in discovering, assessing, tailoring, and implementing Google models alongside selected offerings from partners and open-source creators. Each model page offers tailored guidance and deployment alternatives; based on the specific model, users may have the opportunity to test, use a managed API, adjust settings, or deploy the model to a cloud endpoint. Developers can navigate through the Google Cloud console, utilizing supported APIs, SDKs, and command-line tools for their tasks. This resource is specifically designed for developers, data scientists, and machine-learning teams focused on creating generative AI and various applications powered by models. The availability of models, choices for deployment, and related usage costs can differ based on the model and its configuration. Therefore, users should review the specifics of each model to ensure optimal implementation.
Description
MLflow is an open-source suite designed to oversee the machine learning lifecycle, encompassing aspects such as experimentation, reproducibility, deployment, and a centralized model registry. The platform features four main components that facilitate various tasks: tracking and querying experiments encompassing code, data, configurations, and outcomes; packaging data science code to ensure reproducibility across multiple platforms; deploying machine learning models across various serving environments; and storing, annotating, discovering, and managing models in a unified repository. Among these, the MLflow Tracking component provides both an API and a user interface for logging essential aspects like parameters, code versions, metrics, and output files generated during the execution of machine learning tasks, enabling later visualization of results. It allows for logging and querying experiments through several interfaces, including Python, REST, R API, and Java API. Furthermore, an MLflow Project is a structured format for organizing data science code, ensuring it can be reused and reproduced easily, with a focus on established conventions. Additionally, the Projects component comes equipped with an API and command-line tools specifically designed for executing these projects effectively. Overall, MLflow streamlines the management of machine learning workflows, making it easier for teams to collaborate and iterate on their models.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
Google Cloud Platform
Yes
Amazon SageMaker
No
Aporia
No
Azure Machine Learning
No
Azure Marketplace
No
Comet LLM
No
Dagster
No
Flyte
No
Gemini Enterprise Agent Platform
Yes
Google Cloud CLI
Yes
Integrations
Google Cloud Platform
Yes
Amazon SageMaker
Yes
Aporia
Yes
Azure Machine Learning
Yes
Azure Marketplace
Yes
Comet LLM
Yes
Dagster
Yes
Flyte
Yes
Gemini Enterprise Agent Platform
No
Google Cloud CLI
No
Pricing Details
Costs depend on the selected model and its usage, plus any managed endpoint or compute resources used; applicable model and infrastructure rates vary by configuration.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
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
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
No
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)
No
In Person
No
Vendor Details
Company Name
Founded
1998
Country
United States
Website
cloud.google.com/model-garden
Vendor Details
Company Name
MLflow
Founded
2018
Country
United States
Website
mlflow.org
Product Features
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
