InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Massively Scaling MySQL Using Vitess
Sugu Sougoumarane gives an overview of the salient features of Vitess, and at the end, covers some advanced features with a demo.
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Monitoring AI with AI
Iskandar Sitdikov discusses a solution, tooling and architecture that allows an ML engineer to be involved in delivery phase and take ownership over deployment and monitoring of ML pipelines.
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Migrating ML from Research to Production
Conrado Silva Miranda shares his experience leveraging research to production settings, presenting the major issues faced by developers and how to establish stable production for research.
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Unintended Consequences of AI — Panel Discussion
The panelists discuss some of the unexpected and unintended consequences AI might have.
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PCF Data Collection for TBM
Chris Busch and Raj Sivaraj overview the foundation data collection needs of Mastercard, and how data is collected and provided back to product teams with critical expense data for show-back.
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Building a Voice Assistant for Enterprise
Manju Vijayakumar talks about Einstein Assistant - an AI Voice assistant for enterprises that enables users to "Talk to Salesforce".
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Reasoning about Uncertainty at Scale
Max Livingston presents a case study of using Bayesian modelling and inference to directly model behavior of aircraft arrivals and departures, focusing on the uncertainty in those predictions.
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Machines Can Learn - a Practical Take on Machine Intelligence Using Spring Cloud Data Flow and TensorFlow
Christian Tzolov showcases how building a complex use-case, such as real-time image recognition or object detection, can be simplified with the help of the Spring Ecosystem and TensorFlow.
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Algorithms behind Modern Storage Systems
Alex Petrov talks about modern storage system approaches, discussing storage internals, evaluation techniques to choose a database best suitable for a certain data.
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Zero to Production in Five Months @ ThirdLove
Megan Cartwright discusses how ThirdLove built their first machine learning recommendation algorithm that predicts bra size and style.
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Designing Automated Pipelines for Unseen Custom Data
Kevin Moore discusses some challenges in designing automated machine learning pipelines that can deal with custom user data that it has never seen before, as well as some of Salesforce’s solutions.
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Goldilocks and Artificial Intelligence
Rob Keefer discusses some of the positive and negative impacts of AI on human performance, offering a framework for determining the right amount of AI to mix into a system that will help users.
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