Machine learning challenges at LinkedIn: Spark, TensorFlow, and beyond
From people you may know (PYMK) to economic graph research, machine learning is the oxygen that powers how LinkedIn serves its 630M+ members. Zhe Zhang provides you with an architectural overview of LinkedIns typical machine learning pipelines complemented with key types of ML use cases.
| Talk Title | Machine learning challenges at LinkedIn: Spark, TensorFlow, and beyond |
| Speakers | Zhe Zhang (LinkedIn) |
| Conference | O’Reilly Artificial Intelligence Conference |
| Conf Tag | Put AI to Work |
| Location | London, United Kingdom |
| Date | October 15-17, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | Talk Video |
From people you may know (PYMK) to economic graph research, machine learning is the oxygen that powers how LinkedIn serves its 630M+ members. Zhe Zhang provides you with an architectural overview of LinkedIn’s typical machine learning pipelines complemented with key types of ML use cases. He explores the changes and challenges brought in by the emergence of deep learning techniques, including hardware (GPU, networking), data, tooling, and language (Python and C++ versus Java and Scala). You’ll be introduced to the ongoing work of establishing a unified ML infrastructure based on Spark and TensorFlow, which offers high performance and efficiency together with ease of use.
