This topic describes the documentation updates for new features and updates of Machine Learning Platform for AI (PAI) in 2021.
July 2021
Date | Feature | Type | Description | Reference |
2021.07.29 | Automatic speech recognition (ASR) models | New feature | The Chinese and English speech vectorization models are added to Model Hub. | N/A |
2021.07.06 | Elastic Algorithm Service (EAS) SDKs | New feature | Official EAS SDKs are provided to call the services that are deployed based on models. EAS SDKs reduce the time that is required to define the call logic and improve the call stability. PAI provides EAS SDKs for Python, Java, and Golang. |
June 2021
Date | Feature | Type | Description | Reference |
2021.06.27 | Plug-ins for the AI industry | User experience optimization | The topics about how to use the computer vision model training plug-in and the general-purpose model training plug-in are updated based on the procedures in the PAI console. | N/A |
2021.06.24 | Model deployment by using custom images | New feature | In most cases, environmental dependencies are complex during business development. If you use a processor to deploy a model as a service, you must package shared libraries to the processor. You cannot install the dependency to a path of the system by running the yum install command. This method is less flexible. EAS provides a feature that allows you to use a custom image to deploy a model as a service. |
May 2021
Date | Feature | Type | Description | Reference |
2021.05.27 | Authorization in EAS | User experience optimization | The sample code that displays the content of a RAM policy is updated. |
April 2021
Date | Feature | Type | Description | Reference |
2021.04.25 | Dataset management | New feature | This module centralizes the management of PAI-related datasets, algorithms, models, and images. | |
2021.04.19 | Product models | New feature | The product recognition model is added to Model Hub. | Product recognition model |
2021.04.07 | Built-in processors | New feature | Built-in processors for TensorFlow 1.15 and PyTorch 1.6 are added. |
March 2021
Date | Feature | Type | Description | Reference |
2021.03.04 | Offline prediction in end-to-end text recognition | New feature | EasyVision of PAI allows you to perform model training and prediction in end-to-end text recognition. You can use EasyVision to perform distributed training and prediction on multiple servers. This topic describes how to use EasyVision to perform offline prediction in end-to-end text recognition based on existing training models. | |
2021.03.04 | Labeling templates | New feature | This topic describes the labeling templates for text, videos, and images, and the scenarios and data structure of each labeling template. |
February 2021
Date | Feature | Type | Description | Reference |
2021.02.26 | Learning path | New feature | This topic describes the learning path of PAI. | |
2021.02.25 | Components for binary classification | Optimization | This topic describes the input parameters and PAI commands of components for binary classification and an example of how to use the components. |
January 2021
Date | Feature | Type | Description | Reference |
2021.01.26 | Intelligent video processing models | New feature | The models for general video classification and video highlights generation are added. This topic describes the input and output formats of the models and provides test examples. | N/A |
2021.01.20 | Distributed deep learning framework Whale | New feature | Whale is a flexible, easy-to-use, efficient, and centralized distributed training framework. It provides simple and easy-to-use API operations for data parallelism, model parallelism, pipeline parallelism, operator splitting, and hybrid parallelism, and combines multiple parallelism strategies. Whale is developed based on TensorFlow and is fully compatible with TensorFlow API. You need only to add a few lines of code that describe distributed parallelism strategies to an existing TensorFlow model to perform distributed and hybrid parallel training. | None |
2021.01.11 | The development environment of Data Science Workshop (DSW) | Optimization | This topic describes how to work with the development environment of DSW, including how to use user interfaces, run preset cases, and manage third-party libraries. | None |
2021.01.11 | Create a DSW instance | Optimization | You must create DSW instances before you use DSW to build Notebook models. This topic describes how to create a DSW instance. |