Machine Learning Meetup @ Google

[Important update: we have closed the registration, and submitted the registration list to Google for badge printing. If you registered on AICamp website as instructed below, you are all set. Otherwise, they won't allow access. ]

We will host next in-person ML monthly meetup (January), in collaboration with Google Developers.

[Important RSVP instruction]

• Attendees are required to register at the event website (We will have your correct name for printing badge and check in. NO walk-ins, NO access without badge)
• Attendees are required to be fully vaccinated and bring vaccination proof.
• Contact us to submit topics and/or sponsor the meetup on venue/food/drink/swags/prizes. https://forms.gle/JkMt91CZRtoJBSFUA
• Community on Slack for events chat, speakers office hour and sharing learning resources, job openings and projects collaboration. join slack

Agenda:

• 10:00am~10:20am: Checkin, Snacks and Networking
• 10:20am~10:30am: Welcome/community update/Sponsor intro
• 10:30am~12:00pm: Tech talks
• 12:00pm~12:30pm: Open discussion, Lucky draw and closing

Tech Talk 1: End-to-end Computer Vision models using TensorFlow
Speaker: Nitin Tiwari, Google GDE and Software Engineer @Larsen & Toubro Infotech
Abstract: In this session, we will discuss building end-to-end object detection models and deploying them on mobile devices using TensorFlow Lite. You will learn:

• Dataset preparation and labeling
• Training a custom object detection model
• Deploying the model on an Android application

Tech Talk 2: Practitioners guide to MLOps - A framework for continuous delivery and automation of machine learning
Speaker: Girish Patil, Cloud Engineer, AI @Google
Abstract: This session outlines an MLOps concepts and framework that defines core processes and technical capabilities in MLOps. This will help mature MLOps practices for building and operationalizing ML systems and can help teams improve collaboration, improve the reliability and scalability of ML systems, and shorten development cycle times. These benefits in turn drive innovation and help gain overall business value from investments in ML. This session is intended for technology leaders and enterprise architects who want to understand MLOps. It’s also for teams who want details about what MLOps looks like in practice. The session assumes that readers are familiar with basic machine learning concepts and with development and deployment practices such as CI/CD.

The session will be two parts. The first part, an overview of the MLOps lifecycle, is for all readers. It introduces MLOps processes and capabilities and why they’re important for successful adoption of ML-based systems. The second part is a deep dive on the MLOps processes and capabilities. This part is to understand the concrete details of tasks like running a continuous training pipeline, deploying a model, and monitoring predictive performance of an ML model.

Tech Talk 3: Machine Learning from Few Examples
Speaker: Shweta Bhatt, Google GDE and Applied Scientist @Jupiter
Abstract: Traditional ML systems are highly dependent on large amounts of labelled data for the purpose of model training and their maintenance. However, in practice there is a significant cost associated with data collection, annotation and validation and not all businesses can afford that. Few-shot learning approaches can help address this problem and this session will cover an overview of the same.