Real-Time Sentiment Analysis | The Big Dev Theory | S1 | Ep.3

In this episode of The Big Dev Theory on Twitch, we are joined by Olena Kutsenko Developer Advocate at Aiven.

Ever wondered how to enrich your real-time streaming data with machine learning-driven analysis? Whether it is event or anomaly detection, automated alerts and notifications, network or sentiment analysis, you can leverage the power of machine learning algorithms to gain valuable insights and make informed decisions. By integrating machine learning into your real-time streaming data pipeline, you open up a world of possibilities for all kinds of data analysis. In this session, we'll show you an example of using a model run by SageMaker and usage of SageMaker Jupyter Notebooks to enrich streaming data from an Apache Kafka topic.

Each episode, we chat with AWS partners and bring experts with specialized knowledge in various areas of technology to provide informative and engaging live streams that help developers stay up-to-date with the latest trends and tools.

Stuart Clark, Senior Developer Advocate @ AWS

Shannon Brazil, Incident Responder, CIRT @AWS

The Big Dev Theory is a live stream broadcast every week on the AWS Twitch channel. Our live streams are designed to help developers learn about the advantages of our partner technologies and AWS, these events provide developers with the opportunity to learn from some of the top minds in the industry and connect with other developers who are working on similar projects. A key part of its mission to help developers build and innovate with confidence.