Partner at 1D.works, Lithuania
Sarunas Simaitis started his career as a market research analyst and transitioned to artificial intelligence engineer which resulted in practical knowledge of a wide array of data techniques. While working with market research, he gained experience how to work with small amounts of data, simple metrics, extract and deliver insight to the clients. Moving into a data researcher tough how to work with large amounts of real-time user data. In his latest endeavour as an AI engineer, he is making the algorithms do the insight extraction and decisions in production.
Session: How to avoid pitfalls when deploying ML in production
A few things to keep in mind when deploying artificial intelligence in production
Deploying artificial intelligence-based solutions requires not only a change in tool stack but also a change in the business mindset. Just because machine learning software works in a specific case it does not necessarily mean that the result is meaningful. This talk is meant to elaborate on what tactical steps to take to make artificial intelligence work in real life.
Learning outcomes:
- See how we solved a real life problem with neural networks.
- Learn why and how to make the system transparent.
- Learn how to maintain an artificial intelligence system.
- Learn how to structure an artificial intelligence project.
Additional information and programme of International Conference on Robotics, Automation & Artificial intelligence Systems – here
