Machine Learning

Recommendation System: Matching Algorithms and Architecture

In this article, Alibaba technical expert Aohai introduces the matching module in a recommender system and discusses the architecture.

Recommender System: Online Service Orchestration and Architecture

In this article, Alibaba technical expert Aohai introduces the online service orchestration and architecture for a recommender system.

Recommender System: Ranking Algorithms and Training Architectures

In this article, Alibaba technical expert Aohai introduces the ranking algorithms and training architectures of a recommender system, specifically.

Basic Concepts and Architecture of a Recommender System

In this article, Alibaba technical expert Aohai introduces the basic concepts and architecture of an enterprise-level recommender system.

Building a PAI-based Recommender System within 10 Minutes

In this article, Alibaba technical expert Aohai introduces how to establish a simple recommender system based on Machine Learning Platform for AI (PAI) within 10 minutes.

How TensorFlow on Flink Works: Flink Advanced Tutorials

This article explains how to use a single engine to implement the entire machine learning process through TensorFlow on Flink.

Job Scheduler DAG 2.0: Building a More Dynamic and Flexible Distributed Computing Ecosystem

This article describes how the Alibaba Job Scheduler team upgraded the core scheduling and distributed execution system over the past two years to build DAG 2.

Introduction to EMR DataScience

In this article, AI expert Aohai provides an overview of the DataScience node of E-MapReduce and its components.

Ham vs Spam: Sorting Spam Email Using Alibaba Cloud's Platform for AI (PAI)

Here, we show how Alibaba Cloud's PAI DSW (Data Science Workshop) can be used to build a model for sorting spam and non-spam email.

Gesture Recognition: The Right Way to AI Interaction

Baitao, Yueyi, Jingzhou, Hongcheng, Yingzhi, and Kante introduce research about the interactive ability of gesture recognition by the Tmall Genie M laboratory.

Application of Deep Reinforcement Learning in Time Series Data Compression - ICDE 2020 Paper

This article provides an in depth overview of the research, results, and exploration conducted on data compression using deep reinforcement learning by Alibaba.

Applying Machine Learning to Big Data Processing

This blog discusses some applications of machine learning in big data including data processing, census data analyzing, and the pipelines machine learning.

Evaluating the Future of AI - Impacts, Merits, and Demerits

This article discusses various aspects of AI and its future implications. Also, it discusses how AI will make a positive impact in various industries like healthcare and robotics.

Chatbots - Technology and the Future

This article discusses chatbots: what they are, how they work, and future possibilities. It also outlines how organizations can leverage the Alibaba Cloud Intelligent Service Robot.

What Is the Future Like for CIOs?

This article discusses the evolving role of the CIOs and how the future CIOs should be responsible for business functions that fall out of the CIO purview traditionally.

Can Databases Be Autonomous? DAS Helps You Move Into the Future

DAS is the industry's first cloud service that provides database autonomy. Learn more about the six core autonomy features and the four core innovations.

Where Does AI Stand Today?

In this blog, Jin Rong, VP of Research at Alibaba DAMO Academy, discusses the current practices, innovations, and future avenues of exploration in AI.

Spark-TFRecord: Toward Full Support of TFRecord in Spark

In this post, we will introduce Spark-TFRecord, a new solution to enable support for native TensorFlow data format in Spark.

What's New with Mars - Alibaba's Distributed Scientific Computing Engine

Comprehensive information, detailing the latest Mars releases and plans for upcoming releases.

Use Mars with RAPIDS to Accelerate Data Science on GPUs in Parallel Mode

The author explains the commonalities and differences of code within common Python tools, Mars, and RAPIDS, and how it can pave the way for the future of data science.