Oxylabs Launches ML-Powered Adaptive Parser for E-commerce as a New Member Joins the AI & ML Advisory Board

VILNIUS, LITHUANIA / ACCESSWIRE / September 3, 2020 / Oxylabs, a proxy and data gathering service provider, has released an adaptive parsing feature in beta phase for their Next-Gen Residential Proxies solution. Adaptive parsing allows for effortless parsing of high-quality data from e-commerce websites using integrated machine learning (ML) algorithms. These strides towards innovation have been accompanied by the growing number of members on the Oxylabs’ AI & ML advisory board, which is set to support the company’s efforts to spearhead the technological advancements in the proxy service sphere.

The ML-powered adaptive parser will aid every business engaged in web data gathering, as there will be no need to take care of complexities which are integral to building and maintaining in-house custom parsers. The Next-Gen Residential Proxy feature takes care of the custom parser upkeep, which involves making constant updates every time a target web-page layout changes.

“As a leading proxy service provider in the market, Oxylabs strives to create an environment where forward-thinking businesses can focus on data analysis rather than the data gathering process. That is why we place our innovation efforts to build a well rounded solution, now available for testing, which can ease the web gathering operations and increase the success rates,” – said Julius Cerniauskas, CEO at Oxylabs.

The Next-Gen Residential Proxies, a platform upon which the adaptive parser operates, has shaken the market in the early August of 2020 by integrating AI (Artificial Intelligence) and ML into the residential proxy platform for the very first time. Newly introduced features have the ability to mimic organic user-movements better than ever, eradicating the threat of detection by anti-bot solutions. Also, an integrated auto-retry system allows users to reach the highest data collection success rates by rerunning failed requests multiple times until the data retrieval is successful.

Oxylabs is delighted to announce that Gautam Kedia, a Machine Learning systems expert, will be joining the Oxylabs AI & ML advisory board. Gautam has over a decade of experience leading teams in the fields of applied Machine Learning at Microsoft, Stripe & Lyft.

Gautam Kedia will now be working alongside four other advisory board members to support Oxylabs as it expands its influence in the data industry. All members of the advisory board are the industry-leading data science, machine learning and AI experts, whose professional and educational backgrounds stem from highly reputable organisations such as NASA, Massachusetts Institute of Technology (MIT) and UCL.

“I’m thrilled to join Oxylabs to help advance the proxy and scraping service with ML technologies. I look forward to learning from and working with the best minds in the field,” – said Gautam Kedia.

The advisory board is made up of:

  • Gautam Kedia, Applied Science Lead for Microsoft, ML Lead for Lyft
  • Pujaa Rajan, Deep Learning Engineer at Node.io, USA Ambassador at Women In AI and Google Developer ML Expert
  • Adi Andrei, Lead, Mentor, Senior Data Scientist, NASA, Unilever, British Gas
  • Jonas Kubilius, Artificial Intelligence researcher
  • Muhammad Ali, PhD Researcher, Artificial Intelligence at UCL

About Oxylabs

Oxylabs is the leading global provider of premium proxies and data scraping solutions for large-scale web data extraction. The company’s mission is clear: To give every business – whether big or small – the right to access big data. With unmatched hands-on experience in web data harvesting, Oxylabs is in trusted partnerships with dozens of Fortune 500 companies and global businesses, helping them unearth hidden gems of business intelligence data through state-of-the-art products and technological expertise. For more information, please visit: https://oxylabs.io/

Media contact: Vytautas Kirjazovas
Company: Oxylabs
Website: www.oxylabs.io
Phone: +37060054118
Email: press@oxylabs.io

SOURCE: Oxylabs

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