Talks
Upcoming
Past
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This hands-on experience will provide deep insights into how generative AI is transforming IT operations, automation, and infrastructure management. Learn how to build, deploy, and scale AI models on Google Cloud, and walk away with actionable AI use cases tailored to your organization.
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5 Tricks for a GCP data and AI billing treat. A few common opportunities from hundreds of cost opt sessions with customers
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Every year there are a lot of exciting new announcements and updates at Google Cloud Next. There are four days worth of keynotes, talks, customer demos and interactive sessions to go through. For that reason, our cloud architects and engineers will be sifting through all the presentations and talks from this year's Google Cloud Next and picking the top 10 most important highlights that you need to be aware of. They dig into the details and look at how these announcements may affect you
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Get an inside look at the recently annoucned Vertex AI service on Google Cloud Platfom, in partnership with GCP we'll cover the concepts and services you need to know about to build, test and deploy ML workloads.
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Preparation of data for analysis is an essential part of any analytics system. Expediting this process shortens the time between event and insight. Securing, loading, cataloging and processing data are often considered challenging processes to implement. The Data Lake architecture addresses some of these challenges and limits handoffs between teams working with data. In this talk we'll cover the tools you can use on AWS to build a data lake and derive insight from your data fast!
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Get started with ML on GCP with BigQuery ML - a 101 lesson on ML on BigQuery. If you are looking for ways to get started with ML but are unsure how, look no further. In this webinar, we will demonstrate how you can get started in hours not months.
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Preparation of data for analysis is an essential part of any analytics system. Expediting this process shortens the time between event and insight. Securing, loading, cataloging and processing data are often considered challenging processes to implement. In this talk we'll learn how AWS Lake formation provides a set of tools and templates which make it simpler for teams to perform these tasks in an automated way. We'll work with a novel dataset stored in a variety of AWS data sources. Using a data ingest pipeline, we'll catalog and transfer it to a data lake in a format ready for analysis & consumption, ensuring the appropriate security policies are enforced to protect it. We'll build a few dashboards in Quicksight to gain some intuition about the data and finally we'll apply some machine learning to enhance our insights at scale. Key takeaways, AWS Lake Formation simplifies & automates challenging data wrangling tasks allowing you to quickly build a secure data lake, letting you focus on adding business value.
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The hype around Machine Learning often focuses on the latest and greatest models that have been built. But once you’ve built a great model how do you get it to production? How do you serve predictions to your clients at scale? How do you re-train the model with new data automatically? How do you monitor model performance over time? These are just a few of the challenges of running ML in the wild. In this talk, we will setup Cloud AI Platform Pipelines on GCP using kubeflow as an Orchestrator. We’ll use TensorFlow Extended to show how you can go from a notebook prototype to a production model in simple repeatable steps. We’ll cover some pitfalls and best practices engineers can leverage too so your next ML project gets to production fast! (and stays there!)