Machine Learning and Cloud Computing: Survey of Distributed and SaaS Solutions ∗ Daniel Pop Institute e-Austria Timi¸soara Bd. Vasile P^arvan No. 4, 300223 Timi¸soara,Rom^ania E-mail:
[email protected] Abstract pages (text mining, Web mining), spatial data, mul- timedia data, relational data (molecules, social net- Applying popular machine learning algorithms to works). Analytics tools allow end-users to harvest the large amounts of data raised new challenges for the meaningful patterns buried in large volumes of struc- ML practitioners. Traditional ML libraries does not tured and unstructured data. Analyzing big datasets support well processing of huge datasets, so that new gives users the power to identify new revenue sources, approaches were needed. Parallelization using mod- develop loyal and profitable customer relationships, ern parallel computing frameworks, such as MapRe- and run your overall organization more efficiently and duce, CUDA, or Dryad gained in popularity and accep- cost effectively. tance, resulting in new ML libraries developed on top Research in knowledge discovery and machine learn- of these frameworks. We will briefly introduce the most ing combines classical questions of computer science prominent industrial and academic outcomes, such as (efficient algorithms, software systems, databases) with Apache MahoutTM, GraphLab or Jubatus. elements from artificial intelligence and statistics up to We will investigate how cloud computing paradigm user oriented issues (visualization, interactive mining). impacted the field of ML. First direction is of popu- Although for more than two decades, parallel lar statistics tools and libraries (R system, Python) de- database products, such as Teradata, Oracle or Netezza ployed in the cloud.