01:46

Manage the ML Lifecycle with Seldon

16:12

Seldon Team Spotlight: Kaitlin Hourihane

03:15

Intro to Seldon Core (v2)

27:55

Seldon Team Spotlight: Alex Rakowski

01:00

Seldon's Team Growth and International Expansion post Series B

01:20

What Series B means for Seldon's Product Innovations

06:14

Seldon's $20m Series B Fundraising Announcement

57:27

Building a Data Centric, End to End MLOps Workflow with Seldon and Snorkel AI

01:38

Introducing Seldon

02:34

The Seldon Partner Program

01:29

Explain your ML Model's Decisions with Seldon

02:07

Monitor your ML Pipelines with Seldon

01:59

Deploy Models at Scale with Seldon

05:27

Intro to Seldon Core (v1)

02:00

Intro to MLServer

03:46

Join the Seldon team

56:33

Practical AI Ethics; An MLOps Perspective

01:40

Intro to Seldon

03:16

Seldon Deploy and KFServing: Serverless Deployment of Machine Learning Models

36:55

Drift Detection: An Introduction with Seldon

57:34

Seldon Deploy 1.0 Launch Event

02:33

How does Seldon enable you to be more efficient when deploying ML models?

05:54

Seldon Deploy Product Overview - CTO Clive Cox

01:43

Why is flexibility essential when deploying ML models?

02:16

Highlights from the Seldon Deploy Launch

01:15:14

AI, Data & Ethics with Prof. Joanna Bryson

31:31

TensorFlow London: MLOps and ML MetaData - Why ML projects need both by Hannes Hapke, SAP Concur

34:17

TensorFlow London: TensorFlow 2.x - Applied Machine Learning at Scale by Paige Bailey, Google

01:20:36

Tech Etchics London: What Role Venture Capital Plays in the Ethics of Tech

57:23

Tech Ethics London: Do we need to rethink data for public good with Reema Patel

23:04

TensorFlow London: TensorFlow Hub - Models, Models, and Models

34:50

TensorFlow London: CD4ML and the challenges of testing and quality in ML systems

57:47

Seldon Alibi Presentation: Community Call 30th April 2020

55:21

Tech Ethics London: Responsible Business and Climate Change by Mauro Cozzi, CEO of Emitwise

01:02:24

Tech Ethics London: Future of Healthcare by Andy Wilkins, co-founder of vision4health

36:19

TensorFlow London: TensorFlow.js - Machine Learning in JavaScript by Jason Mayes, Google

28:01

TensorFlow London: Text classification with transformers in TensorFlow 2 by David Mráz

36:13

TensorFlow London: Build your own agent with TensorFlow 2.0. for StarCraft II

30:38

TensorFlow London: Computer vision in football with TensorFlow

31:25

TensorFlow London: Deep learning for classification of Attention Deficit Hyperactive Disorder

32:26

TensorFlow London: The IPU, TensorFlow and everything in between

29:25

TensorFlow London: Training to Explainability via GitOps with Kubeflow by Ryan Dawson

30:30

TensoRflow London: Introduction to Convolutional Neural Networks with Tensorflow by David Tyler

04:33

Seldon Deploy: Machine Learning Model Production on Kubernetes

29:31

TensorFlow London: Tensorflow for Medical Imaging by Ladislav Urban

17:54

TensorFlow London Meetup: Overview of TensorFlow.js by Nikos Katsikanis

17:41

TensorFlow London: TensorFlow Extended (TFX) by Christos Aniftos, ML specialist at Google Cloud

12:15

TensorFlow London: Going packaging free with ML on Google Cloud Platform by Alexandra Abbas

26:16

TensorFlow London Meetup at Google: Lightning talk by Alex Housley

09:12

XAI Seldon expert - Interpretable AI and explanations

28:52

TensorFlow London: Introduction to Gaussian processes using TensorFlow based library GPflow

33:32

TensorFlow London: TensorFlow 2.0 by Gema Parreno Piqueras, Data scientist at BBVA

01:54

ML deployment is the main barrier to business value

03:51

Seldon Team @Rise London

26:41

TensorFlow London: Cutting edge generative models. Applications and implications by Pierre Richemond

33:38

Tensorflow London: Building Artificial General Intelligence by Peter Morgan

10:22

Seldon Update

32:24

How to manage data science development from an organizational perspective by Nic Young, Bibblio

36:35

TensorFlow London: Tensorflow and Graph Recommender Networks by Yaz Santissi, GDG Cloud

16:51

Scaling ML operations and Gartner insight