InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
-
From POC to Production in Minimal Time - Avoiding Pain in ML Projects
Janet Bastiman describes how turning an AI proof of concept into a production ready, deployable system can be a world of pain, especially if different parts of the puzzle are done by different teams.
/presentations/poc-ml/en/mediumimage/Janbig-1585962776885.JPG)
-
Real-Time Data Streaming with Azure Stream Analytics
Alexander Slotte introduces Azure Stream Analytics, its ecosystem, and real world examples streaming Twitter feeds as well as sensor data from Raspberry Pi.
/presentations/azure-stream-analytics-raspberry-pi/en/mediumimage/Alexbig-1585779348893.JPG)
-
What Does It Mean to Be a Data Scientist? Definitions and Lessons Learned from the Trenches
Brian Korzynski discusses what Data Science and Big Data are, focusing on the data preparation that needs to take place, and making a distinction between ML issues and programming.
/presentations/data-science-big-data-programming/en/mediumimage/Bribig-1585532223774.JPG)
-
Big Data Legal Issues. GDPR and Contracts
Anton Tarasiuk discusses the legal issues that can be encountered when dealing with Big Data, GDPR and contracts.
/presentations/law-big-data-gdpr/en/mediumimage/Antbig-1585331671197.JPG)
-
Big Data Information Architecture for AI
Toby Woolfe discusses case studies (Watson Willow, the L’Oreal factory and Iplexia demo) to show a factory line manager talking to Watson.
/presentations/watson-case-study/en/mediumimage/Tobbig-1584923822433.JPG)
-
Monitoring and Tracing @Netflix Streaming Data Infrastructure
Allen Wang talks about the design and implementation details of the dev/ops tools used by Netflix and highlights the critical roles they play in operating their data infrastructure.
/presentations/netflix-streaming-data-infrastructure/en/mediumimage/Albig-1584722318863.JPG)
-
From Spark to Elasticsearch and Back - Learning Large-Scale Models for Content Recommendation
Sonya Liberman shares an algorithmic architecture that enables running complex models under difficult scale constraints and shortens the cycle between research and production.
/presentations/content-recommendation-model/en/mediumimage/Sonbig-1584280027769.JPG)
-
ML/AI Panel
The panelists discuss what makes ML different from other types of applications and why it requires special tooling.
/presentations/panel-ml-tooling/en/mediumimage/Chrisbig-1583859512876.jpg)
-
Swift for Tensorflow
Paige Bailey demonstrates how Swift for TensorFlow can make advanced machine learning research easier and faster.
/presentations/swift-tensorflow/en/mediumimage/pabig-1583757949880.JPG)
-
Computational Propaganda - How Algorithms Influence our Decisions
Pawel Rzeszucinski discusses the Cambridge Analytica case, Brexit and the use of bots for influencing people’s decisions.
/presentations/social-networks-algorithms/en/mediumimage/Pawbig-1583506356712.JPG)
-
How AI Is Transforming Test Automation
Daniel Gold reviews concepts in AI and ML, how it is related to testing and what is the vision for AI in testing, how they used AI to process the DOM, and the technical challenges encountered.
/presentations/ai-test-automation/en/mediumimage/Danbig-1583505655562.JPG)
-
Spring Data JPA from 0-100 in 60 Minutes
Jens Schauder looks into how Spring Data JPA works and how it integrates with Spring by means of Spring Data JPA.
/presentations/spring-data-jpa/en/mediumimage/Jenbig-1583368251415.JPG)