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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.
Description
ONNX provides a standardized collection of operators that serve as the foundational elements for machine learning and deep learning models, along with a unified file format that allows AI developers to implement models across a range of frameworks, tools, runtimes, and compilers. You can create in your desired framework without being concerned about the implications for inference later on. With ONNX, you have the flexibility to integrate your chosen inference engine seamlessly with your preferred framework. Additionally, ONNX simplifies the process of leveraging hardware optimizations to enhance performance. By utilizing ONNX-compatible runtimes and libraries, you can achieve maximum efficiency across various hardware platforms. Moreover, our vibrant community flourishes within an open governance model that promotes transparency and inclusivity, inviting you to participate and make meaningful contributions. Engaging with this community not only helps you grow but also advances the collective knowledge and resources available to all.
API Access
Has API
API Access
Has API
Integrations
Flyte
Apolo
Aporia
Azure Marketplace
Comet LLM
Dagster
Databricks
Determined AI
Docker
Higson
Integrations
Flyte
Apolo
Aporia
Azure Marketplace
Comet LLM
Dagster
Databricks
Determined AI
Docker
Higson
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
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
MLflow
Founded
2018
Country
United States
Website
mlflow.org
Vendor Details
Company Name
ONNX
Website
onnx.ai/
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization