Please note: this event is scheduled for Brisbane time (GMT + 10).
Register for the event on the GDG Cloud Brisbane page: https://www.meetup.com/GDG-Cloud-Brisbane/events/273762062/
1/ Kaz Sato (Google Developer Advocate)
"Productionizing ML with ML Ops and Cloud AI"
The hardest part of ML adoption in enterprises is Productinization. As we see in recent discussions around ML Ops, there is a big gap between Data Scientists' PoC code and production ML development and operation with the Ops team. Such as, preparing a manageable ML dev environment, building a scalable ML serving infrastructure, setting up a ML pipeline for continuous training, and automated validation of data and model. In this session, we will learn how to leverage various Google's ML/AI offerings such as TensorFlow Extension (TFX), TensorFlow Enterprise, Cloud AI Platform Notebooks, Training, Prediction, and Pipelines for productionizing your ML service with the ML Ops best practices.
2/ Sharmistha Chatterjee (Senior Manager Data Sciences) & Charmi Chokshi (Machine Learning Engineer & Shipments)
"Fairness in Machine Learning"
Sharmistha is currently working as a Senior Manager of Data Sciences at Publicis Sapient. She is responsible for driving digital transformations of clients using AI/ML across a range of industries including travel and hospitality, supply chain, IoT, Media among others. She has proven experience in both doing AI research and implementing scalable AI solutions for enterprises.
Charmi enjoys playing with data and predicting possibilities! From giving tech sessions at the local meetups to delivering tech-talks at global stages, she loves helping developers & students who look forward to growing their careers in the field of Artificial Intelligence.
Most recently, She has been recognised as a Google Developers Expert in Machine Learning due to her community contributions and technical expertise in the field.
3/ Bon van Luijt (SeMI Technologies CEO & CO-Founder)
"Semantic Search on GCP with the Weaviate vector search engine."
It is estimated that 80% of all data is unstructured and where traditional search engines allow you to quickly and efficiently search for keywords, Weaviate indexes data based on its semantic meaning thanks to its build-in NLP machine learning model.
Weaviate is an open-source vector search engine that you install it directly from the Google Cloud Marketplace console. Through its graph-based API interface (RESTful and GraphQL), you can easily integrate Weaviate into existing applications or use it as the core infrastructure for your solution.
During this presentation, we will discuss how Weaviate works, what use cases you can solve with it, and we will live-build a document search engine and perform some of the out-of-the-box classification features.