Machine Learning Frameworks in Industrial Applications: Challenges and Prospects

Machine learning is widely applied in image recognition, multimedia creation, natural language processing, and search advertising recommendation, showing broad use and strong performance.

Facing increasingly complex application scenarios, machine learning frameworks help save effort writing large amounts of low‑level code, avoid deployment and environment adaptation troubles, and allow focus on business scenarios and model design, gaining wider industry acceptance.

The most popular deep learning frameworks worldwide include TensorFlow, PyTorch, Caffe, Theano, MXNet, Torch, PaddlePaddle, etc.; these open‑source frameworks perform excellently and each has its own advantages and characteristics. As deep learning technology becomes more widespread and frameworks mature, the barrier to entry continues to lower.

However, for fast‑growing products like Xiaohongshu, which feature dual‑column layouts and mainly distribute user‑generated content, higher demands are placed on system timeliness and sparse content expression, and data processing challenges keep growing. Therefore, developing a machine learning framework tailored to its own business needs is often a better choice.

At the same time, several common issues arise in the practical application of machine learning frameworks:

What preparations does a developer need to master a deep learning framework?

Can deep learning frameworks “cure all” problems? Are they applicable to all application scenario requirements?

How can CPU and GPU hardware capabilities be fully utilized to improve machine learning performance?

What are the distinctive features and innovations of domestic deep learning frameworks compared with mainstream frameworks from Europe and the United States?

On October 19, 2022 at 19:00, Xiaohongshu technology REDtech invited the founder of OneFlow, Yuan Jinhui, and the head of Xiaohongshu Intelligent Distribution Department, Rui Ge, to deliver a new episode of the 【REDtech 来了】technical live broadcast, focusing on the challenges and prospects of machine learning frameworks in industrial applications.

Speaker bios: Yuan Jinhui obtained a PhD in Computer Science from Tsinghua University in 2008 (excellent dissertation award), conducted postdoctoral research in computational neuroscience at Tsinghua (2008‑2011), was a senior researcher at Microsoft Research Asia, founded Beijing OneFlow Technology in 2017 to build a next‑generation deep learning framework, and currently serves as architect of the Tianchu Open Source Open Platform at Jiangsu Laboratory and a member of the Large Model Technology Committee at Beijing Institute for General Artificial Intelligence. Rui Ge is the head of Xiaohongshu Intelligent Distribution Department, who built an online learning training framework supporting ultra‑large‑scale parameters, supports personalized learning for search‑ad‑recommendation models, and has previously worked at Baidu Fengchao on large‑scale distributed training algorithms, applying models such as trillion‑feature LR, GBDT, and large‑scale sparse discrete DNN in industry, and developed a semi‑supervised learning algorithm for search‑ad relevance that significantly improved ad relevance.

Live broadcast details: Date – October 19, 2022 (Wednesday) 19:00‑21:00; Platform – follow the Xiaohongshu Technology REDtech video account, reserve the live stream (also simulcast on Douyin and B站 by searching Xiaohongshu Technology REDtech); scan the QR code to join the discussion group for real‑time links and reminders; highlights and raffle activities will be posted in the WeChat group, with a chance to be selected by speakers for Q&A.

Additionally, Xiaohongshu’s autumn recruitment is in progress, with openings for front‑end engineers, iOS engineers, machine learning algorithm engineers, Java engineers, test development engineers, data engine engineers, and more.