Vasudev Maduri
Staff Data Engineer at Admiral Group | GDE on Cloud
London, United Kingdom
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Vasudev is a Google Developer Expert (GDE) in Google Cloud and seasoned professional serving as a Staff Data Engineer at Admiral Group. With a wealth of experience in the realms of Google Cloud Platform (GCP), big data, and data architecture, Vasudev has consistently demonstrated his prowess in implementing industry-leading products recognized by Gartner.
His journey in the world of technology has encompassed stints at renowned organizations like Hitachi and Ford, where he honed his expertise in data engineering and analysis. Vasudev is a practitioner and an active mentor, deeply committed to fostering talent and contributing to open-source technologies.
As a thought leader in his field, Vasudev has shared his insights and knowledge with a global audience through over 30 impactful talks. Vasudev continues to inspire and innovate, driving transformative change in the world of data and technology.
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Rapid Data Science / ML Development With TPUs & Edge TPUs
Google's Tensor Processing Units (TPUs) are revolutionizing the way data scientists work. Week-long training times are a thing of the past and many models can now be trained in minutes in a notebook. Agility and fast iterations are bringing neural networks into regular software development cycles and many developers are ramping up on machine learning. This session will introduce TPUs, Edge TPUs then dive deep into their microarchitecture secrets. It will also show you how to use them in your day-to-day projects to iterate faster. In fact, we will not just demo but train most of the models presented in this session on stage in real time, on TPUs & Edge TPUs.
Serverless Containers with Knative and Cloud Run
When you build a serverless app, you either tie yourself to a cloud provider, or you end up building your own serverless stack. Knative provides a better choice. Open-source Knative extends Kubernetes to provide a set of middleware components for container-based serverless apps that can run anywhere. In this talk, we’ll explore Knative components (serving, eventing, build) and also take a look at its managed cousin Cloud Run on Google Cloud.
Turn Python Scripts into Beautiful ML Tools
We've all seen poor tooling slow down data science and machine learning projects. In fact, most projects develop their own unique ecosystem of bug-ridden and unmaintainable internal tools to analyze data, often through a patchwork of Jupyter Notebooks and Flask apps.
Along with rapid velocity of data ingestion, the need for speedier decision making will simultaneously rise. Today’s process of MLOps will become redundant as data scientists will start searching for faster ways to create production grade deployments with insightful user interfaces that communicate the power of the algorithms without getting stuck in long MLOps journeys.
In this workshop, we would be discussing about a new python library called “Streamlit” which helps data scientists rapidly create production-grade visualizations with backend integration and quickly share their results with stakeholders to generate powerful insights. Here we would utilize Streamlit in creating a web-based tool which runs on top of an optimization code, reducing our algorithm development to front end deployment lead time from “2-3 weeks” to “2-3 days”.
At the end of the workshop you will have (1) a beautiful demo , and (2) a new weapon to tackle tooling problems in your own projects.
DevOps for Machine Learning: Deploying ML Models at Scale on GCP
If you are one of the Cool developer doing Style Transfer, Visual Translation or lurking at arxiv-sanity for what is hot, but wondering how would you take the model beyond Jupyter notebooks?
"It is my impression that the world of deep learning research is starting to plateau. What's booming: deploying DL to real-world problems."
-François Chollet
I trod the same path when I started as a founding ML Engineer, over the past two years I have learned that solid engineering is essential for building ML Application at web scale. Productionizing ML model is the last mile journey, the most dreaded and less talked about topic, knowing the right toolchain to automate your build pipeline is essential for APIfiying your ML Models.
Typical ML pipeline is accompanied by a big data infrastructure to de-normalize and preprocess the application data to prepare training data, then a microservice to expose the trained model artifact on a runtime component as a service.
In this session, we will explore the GCP DevOps toolchain to build, train, test, deploy and monitor an ML Model. The focus will be on the toolchain and how to automate the entire process from model development to deployment on Google Cloud Platform.
Devfest Ireland 2023 Sessionize Event
DevFest London 2023 Sessionize Event
DevOpsDays Tel Aviv 2022 Sessionize Event
Devfest London 2022 Sessionize Event
DevFest Dutse 2022 Sessionize Event
Global AI Developer Days - Toronto - (In Person) Sessionize Event
DevFest Chennai 2022 Sessionize Event
GDG DevFest UK & Ireland Sessionize Event
Great North DevFest Sessionize Event
GDG DevFest UK & Ireland 2020 Sessionize Event
GDG DevFest London 2019 Sessionize Event
DevFest Veneto 2019 Sessionize Event
GDG DevFest Pescara 2019 Sessionize Event
GDG DevFest Hyderabad 2019 Sessionize Event
GDG DevFest Hyderabad 2018 Sessionize Event
Vasudev Maduri
Staff Data Engineer at Admiral Group | GDE on Cloud
London, United Kingdom
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