🆂🅻🅾🆃 #1: Get to Know Firebase and its ML Kit - Rachel Saunders, Google
Firebase enables app developers to build faster and easier with serverless products (like authentication and real-time databases). The Firebase platform can also help with app quality (like Crashlytics) and growing your app (like sliced audience feature distribution and messaging). Both prototype and production apps can take advantage of our toolkits for developing and growing your app. Come learn about how Firebase can help you with your hobby, startup, or even enterprise-level apps!
We'll also take a bit of deep dive into ML Kit - Firebase's mobile SDK that offers you Google's machine learning in a powerful yet easy-to-use package. There's no need to have deep knowledge of neural networks or model optimization to get started. On the other hand, if you are an experienced ML developer, ML Kit provides convenient APIs that help you use your custom TensorFlow Lite models in your mobile apps.
🆂🅻🅾🆃 #2: MLKit + Firebase Predictions - Aditya Gautam, Google
ML Kit allows you to harness the power of machine learning in your apps without needing to be an expert. Leverage powerful, but simple-to-use on-device and cloud-based APIs for Vision and Natural Language Processing, or train and/or deploy your own models. Understand some big additions to ML Kit and how to use these to enable smarter, richer experiences to your users. In the second half, we would learn about how we can do smart segmentation of users using Firebase Predictions and target the selected groups of people for revenue optimization, user retention and other customization.
🆂🅻🅾🆃 #3: BigQuery ML: Practical use cases using the built in machine learning algorithm in BigQuery - Marcus Stade
Creating machine learning models directly in the Cloud Data Warehouse? Big Query, part of the Google Cloud Platform, offers a integrated solution called Big Query ML with the possibility to create machine learning models and make prediction based on data stored in the database. The build-in machine learning solution offers a wide variety of machine learning models types, e.g linear regression, logistic regression and k-means clustering. In this presentation we will create machine learning models and make predictions based on business data.
Marcus has a track record as digital analytics consultant. Before joining the digital analytics community, he was working in the field of Web-Development and Online-Marketing. He organizes the Digital Analytics Meetup in Munich and Nuremberg and publishes articles covering Digital Analytics topics on magazines and his blog https://www.marcusstade.de.
We are looking for speakers and sponsors!
As always, we are super happy to have you with a lightning talk on your small or big successes, open source projects or just something amazing you would like to share.