Machine Learning at Amazon
| Autor: | Ralf Herbrich |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
030109 nutrition & dietetics Active learning (machine learning) business.industry Computer science Algorithmic learning theory Stability (learning theory) Online machine learning Multi-task learning Machine learning computer.software_genre Robot learning 03 medical and health sciences Computational learning theory Instance-based learning Artificial intelligence business computer |
| Zdroj: | WSDM |
| DOI: | 10.1145/3018661.3022764 |
| Popis: | In this talk I will give an introduction into the field of machine learning and discuss why it is a crucial technology for Amazon. Machine learning is the science of automatically extracting patterns from data in order to make automated predictions of future data. One way to differentiate machine learning tasks is by the following two factors: (1) How much noise is contained in the data? and (2) How far into the future is the prediction task? The former presents a limit to the learnability of task --- regardless which learning algorithm is used --- whereas the latter has a crucial implication on the representation of the predictions: while most tasks in search and advertising typically only forecast minutes into the future, tasks in e-commerce can require predictions up to a year into the future. The further the forecast horizon, the more important it is to take account of uncertainty in both the learning algorithm and the representation of the predictions. I will discuss which learning frameworks are best suited for the various scenarios, that is, short-term predictions with little noise vs. long-term predictions with lots of noise, and present some ideas to combine representation learning with probabilistic methods. In the second half of the talk, I will give an overview of the applications of machine learning at Amazon ranging from demand forecasting, machine translation to automation of computer vision tasks and robotics. I will also discuss the importance of tools for data scientist and share learnings on bringing machine learning algorithms into products. |
| Databáze: | OpenAIRE |
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