Build with Generative AI on Alibaba Cloud

Accelerate innovation in generative AI with Alibaba Cloud products and solutions

Overview

Generative AI is seeing greater use across a range of industries. Applications and services are being rebuilt and reconfigured with generative AI technologies such as large language models (LLMs). Alibaba Cloud's comprehensive range of cloud products and solutions, in conjunction with leading AI capabilities, can support you to accelerate innovation, create new and intelligent customer experiences, and generate opportunities for business success.

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Disclaimer

Note that the videos as displayed in this page are solely for the purpose of demonstrating product functions, all information shown in the videos was generated by GPT models and is not verified by Alibaba Cloud. Alibaba Cloud makes no warranties, expressed or implied, as to the authenticity, accuracy and completeness of all such information.

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Deploying Llama 2 Models on PAI-EAS

Result Demonstration

About Llama 2:

Llama is an open-source LLM. Compared to its predecessor, Llama 1, Llama 2 has more training data and a longer context length, making it more versatile and accurate in generating natural language at scale. Llama 2 has different versions with varying scales of parameters, including 7 billion, 13 billion, and 70 billion.

Deployment:

  • PAI-EAS (Elastic Algorithm Service) is a component of Machine Learning Platform for AI (PAI) that allows users to deploy and manage AI applications easily. It includes the PAI-Blade inference accelerator, a hardware accelerator engineered to improve the performance of LLMs with an inference speed up to six times faster than traditional methods. This means you can process more data, generate faster responses, and enhance the performance of your language-based applications. You can run Llama 2 on PAI-EAS in WebUI with just a few clicks and the sample code provided in the blog, or use API with Python Wrapper code in the blog to set up Llama 2 service. You can also find guidance on how to mount a custom model on Llama 2 with PAI-EAS in the blog.

Product

Platform for AI (PAI)

This one-stop platform provides whole-process AI engineering capabilities based on machine learning algorithms.

Blog

Running Llama2 Models on Alibaba Cloud's PAI with Ease

This blog introduces the easy deployment of Llama 2 models on Alibaba Cloud's PAI-EAS platform, which offers significant speed boosts and cost savings for users through PAI Blade.

Video Tutorial

Running Stable Diffusion Models on PAI

Result Demonstration

About Stable Diffusion:

Stable Diffusion is an open-source and popular cross-modal generation model that leverages advanced deep learning algorithms and techniques to generate visually compelling images based on text descriptions. Iteratively updating the generated image progressively enhances the final result's quality, coherence, and fidelity.

Deployment:

  • You can use PAI-DSW (Data Science Workshop) to fine-tune the Stable Diffusion models. Utilize PAI-DLC (Deep Learning Container) to quickly set up and manage a streamlined deep learning environment with ready-to-use containers of popular deep learning frameworks. Finally, deploy the models and create text-to-image generation service with a few clicks in PAI-EAS.

Product

Platform for AI (PAI)

This one-stop platform provides whole-process AI engineering capabilities based on machine learning algorithms.

Blog

Unleashing the Creative Potential with GenAI-Diffusion Graphic Generation Solution

This article explores how you can create your own text-to-image service based on Stable Diffusion models without high costs or extensive time commitments.

Documentation

Deploy Stable Diffusion for AI image generation with EAS in a few clicks

This topic describes how to PAI-EAS to deploy Stable Diffusion Web UI as a web application. You can use the deployed application for model inference.

Generative AI on Alibaba Cloud

Accelerate innovation with generative AI to create new business success

Using AnalyticDB for PostgresSQL for Vector Query and Analysis

Result Demonstration

Your Challenges:

Large volumes of complex unstructured data pose a challenge to traditional vector databases regarding real-time performance analytics, high costs, and system compatibility alongside the need for business scalability being a major concern.

Our Solution:

  • AnalyticDB for PostgreSQL is a cloud-native data warehouse with massive parallel processing (MPP) capabilities. The vector database feature can help you manage large-scale unstructured data such as documents, emails, image and web content and is ideal for modern data services that manage and process large volumes of structured and unstructured data, provide real-time analysis, and adapt to changing business needs. Enterprises with domain-specific requirements can leverage AnalyticDB for PostgreSQL to produce accurate results on topics based on domain-specific or specialized data. In this example, you can easily build a chatbot that combines LLM’s learning and comprehension capabilities with the core capabilities of AnalyticDB for PostgreSQL. This includes built-in vector data retrieval and full-text retrieval, providing professional and timely answers with the knowledge of your enterprise.

Product

AnalyticDB for PostgreSQL

An online MPP (Massively Parallel Processing) data warehousing service based on the open source Greenplum Database

Blog

Building an Enterprise-Specific Chatbot with AnalyticDB for PostgreSQL and Generative AI

This blog introduces the principles and processes of building an enterprise-specific Chatbot based on LLM and a vector database.

Video Tutorial

Building a Conversational Search Service with OpenSearch Vector Search Edition

Result Demonstration

Your Challenges:

Industries such as e-commerce and online social platforms contain large volumes of unstructured data that are constantly increasing and changing, and the customer inquiries are usually complicated and require real-time response. Search services based on LLMs cannot fulfill customer needs for accuracy and timeliness, and demand high hardware costs.

Our Solution:

  • OpenSearch provides a high-performance search service that supports billion-level 128-dimensional documents (as vector data), or thousand-level QPS with millisecond-level query response. It integrates different retrieval algorithms for vector data and supports complex search requirements such as hybrid search, filtering by expression, and filtering while searching. OpenSearch also provides data compression features and fine index structure design to lower storage and memory requirements, saving hardware costs at scale. You can combine OpenSearch with LLMs through a service tool, to build an intelligent, dedicated, and real-time search service for your business. This will improve service quality and timeliness, without requiring heavy investment in hardware.

Product

OpenSearch

A one-stop development platform for intelligent search services with a fully open text vector search engine and semantic understanding for queries

Blog

Building a Conversational Search Service with OpenSearch and LLMs

This article introduces how to build a conversational search service based on an open-source vector search engine and LLMs.

Generative AI on Alibaba Cloud

Accelerate innovation with generative AI to create new business success

Setting Up a Virtual Clothes Try-On Service Based on Stable Diffusion Models

Result Demonstration

Your Challenges:

Image-based virtual try-on consists of generating an image of a target model wearing a given item of clothing, which offers a more convenient and personalized user experience. However maintaining a natural and vivid effect consistently is difficult to guarantee - particularly at scale.

Our Solution:

  • LoRA (Low-Rank Adaptive Relational Attention) is a widely used algorithm for AI image generation which allows users to fine-tune generative models by adding a small number of parameters, and using a small number of data sets. Platform for AI (PAI) allows you to easily and quickly fine-tune AI generated imagery based on LoRA and Stable Diffusion models, providing a wide range of generation space for models, poses, and backgrounds while reducing the number of trainable parameters and GPU memory requirements. This opens up new possibilities for a more personalized and intelligent customer experience for the fashion retail industry.

Product

Platform for AI (PAI)

This one-stop platform provides whole-process AI engineering capabilities based on machine learning algorithms.

Blog

Quickly Set Up a Virtual Clothes Try-On Services with PAI

This article introduces how to quickly build a virtual clothes try-on services based on the Stable Diffusion models.

Video Tutorial

Simplifying Logistics Management with EasyDispatch for Field Service Management Solution

Result Demonstration

Your Challenges:

Managing a logistics business requires constant attention across various parallel tasks such as driver management, route planning, and delivery monitoring, etc. Performance factors such as route efficiency and delivery timeliness directly affect customer satisfaction.

Our Solution:

  • EasyDispatch is an AI-powered logistics management platform that enables businesses to optimize their logistics operations through real-time monitoring, intelligent dispatching, and predictive maintenance capabilities. The platform uses advanced algorithms to optimize delivery routes, minimize transportation costs, and improve overall efficiency. You can combine Alibaba Cloud AnalyticDB and a LLM to build a chat-style plugin for EasyDispatch to create and manage delivery tasks without the need for manual operations. This plugin can provide personalized and accurate responses to customer inquiries, and optimize delivery routes automatically to improve efficiency.

Solution

EasyDispatch for Field Service Management

This AI-based solution improves field service dispatch capabilities and efficiency, helping FSPs fulfill service scheduling requirements in real-time.

Blog

Simplifying Your Delivery Process in EasyDispatch with Generative AI

This blog explores how integrating Generative AI and vector database retrieval can benefit a delivery business and provides use cases for vector database retrieval.

Video Tutorial

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