In September I traveled to the small town of Sitges outside of Barcelona to learn more about the future of technology. Full Stack Fest is a yearly, single track conference touching on a broad range of tech topics (frontend, backend, testing, new languages, etc). I was there to absorb some of the knowledge and inspiration from the curated list of speakers. In this post I will go over my favorite talks.
Since July 2019 I have been an intern at Meltwater in Budapest, working in the Foundation team that is focused on developer productivity. It has been a truly valuable experience to solve challenging real-life problems, that have an impact on the everyday lives of our developers.
In this blogpost, I will share my experience as an intern at Meltwater, and discuss the details of the project that I have been working on.
In late August 2019 Meltwater had the pleasure of hosting Zalando in our Berlin office for a knowledge sharing session about applying product management for internal platforms that improve software delivery performance.
In this post we will explain how this meeting came to be, provide a sneak peak into the topics we covered, and what upcoming iterations of this exchange could look like.
For our fairhair.ai platform we enrich over 450 million documents such as news articles and social posts per day, with a dependency tree of more than 20 NLP syntactic and semantic enrichment tasks. We ingest these documents as a continuous stream of data and guarantee delivery of enriched documents within 5 minutes of ingestion.
This technical feat required tight collaboration between two specialised teams: data science and platform engineering. Enabling both teams to efficiently work together around a common workflow execution engine was another problem we needed to solve. Hopefully that description fully piqued your interest because our solution (Benthos) is totally boring.
Meltwater has been providing sentiment analysis powered by machine-learning for more than 10 years. In 2009 we deployed our first models for English and German. Today, we support in-house models for 16 languages.
In this blog post we discuss how we use deep learning and feedback loops to deliver sentiment analysis at scale to more than 30 thousand customers.