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Description

Baidu AI Cloud Machine Learning (BML) serves as a comprehensive platform for enterprises and AI developers, facilitating seamless data pre-processing, model training, evaluation, and deployment services. This all-in-one AI development and deployment system empowers users to efficiently manage every aspect of their projects. With BML, tasks such as data preparation, model training, and service deployment can be executed in a streamlined manner. The platform boasts a high-performance cluster training environment, an extensive array of algorithm frameworks, and numerous model examples, along with user-friendly prediction service tools. This setup enables users to concentrate on refining their models and algorithms to achieve superior prediction outcomes. Additionally, the interactive programming environment supports data processing and code debugging, making it easier for users to iterate on their work. Furthermore, the CPU instance allows for the installation of third-party software libraries and customization of the environment, providing users with the flexibility they need to tailor their machine learning projects. Overall, BML stands out as a valuable resource for anyone looking to enhance their AI development experience.

Description

Distributed AI represents a computing approach that eliminates the necessity of transferring large data sets, enabling data analysis directly at its origin. Developed by IBM Research, the Distributed AI APIs consist of a suite of RESTful web services equipped with data and AI algorithms tailored for AI applications in hybrid cloud, edge, and distributed computing scenarios. Each API within the Distributed AI framework tackles the unique challenges associated with deploying AI technologies in such environments. Notably, these APIs do not concentrate on fundamental aspects of establishing and implementing AI workflows, such as model training or serving. Instead, developers can utilize their preferred open-source libraries like TensorFlow or PyTorch for these tasks. Afterward, you can encapsulate your application, which includes the entire AI pipeline, into containers for deployment at various distributed sites. Additionally, leveraging container orchestration tools like Kubernetes or OpenShift can greatly enhance the automation of the deployment process, ensuring efficiency and scalability in managing distributed AI applications. This innovative approach ultimately streamlines the integration of AI into diverse infrastructures, fostering smarter solutions.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

JupyterLab Yes 
Kubernetes No 
PyTorch No 
Red Hat OpenShift No 
TensorFlow No 

Integrations

JupyterLab No 
Kubernetes Yes 
PyTorch Yes 
Red Hat OpenShift Yes 
TensorFlow Yes 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial Yes 
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 Yes 
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 Yes 
Live Training (Online) No 
In Person Yes 

Vendor Details

Company Name

Baidu

Founded

2000

Country

China

Website

intl.cloud.baidu.com/product/bml.html

Vendor Details

Company Name

IBM

Country

United States

Website

developer.ibm.com/apis/catalog/edgeai--distributed-ai-apis/Introduction/

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 

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