Did you miss CNCF’s Kubernetes AI Day at KubeCon + CloudNativeCon Europe 2021 back in May? If you did, you may not have noticed that all the talks from the event have been uploaded to YouTube. In part two of this two part blog series, we’ll give you an executive summary of the last batch of the day’s talks.
The SAME Project: A Cloud native Approach to Reproducible Machine Learning
In this talk, David Aronchick of Microsoft presented the SAME project which aims to address the challenges of reproducibility when it comes to machine learning.
- A recap of key AI and machine learning definitions
- An exploration of why models take so long to deploy or fail in production
- The case for cloud native machine learning (Composability, Portability, Scalability)
- A overview of the SAME Project (Self Assembling Machine Learning Environments)
- Live demo!
Stand Up for Ethical AI! How to Detect and Mitigate AI Bias
In this talk Andrew Butler and Animesh Singh of IBM spoke on the topic of how to bring trust and transparency to the world of AI.
- An overview of IBM’s vision for Trusted AI
- An exploration of the issues of bias, explainability and adversarial robustness in AI
- An overview of Kubeflow components
- How to integrate Trusted AI with Kubeflow Pipelines
- Live demo!
- How to integrate Trusted AI with KFServing
Kubernetes to server a fraud detection model
In this presentation Gabriela Souza de Melo of Legiti talked about her experiences on how they use machine learning to help identify credit card fraud.
- An overview of how credit card fraud works, specifically minimizing chargebacks while maximizing approval rates
- How Legiti uses machine learning to detect fraud
- Data ingestion and real time data processing
- Training custom models per customer
- An example using an “Order Evaluation” API
- How autoscaling, continuous delivery and automated rollbacks work
MLOPs design patterns with Kubeflow Pipelines
In this talk Amy Unruh from Google presented on the topic of best practices and design patterns for MLOps and how to operationalize those patterns with Kubeflow Pipelines.
- Why “productionizing” models on your laptop is hard.
- An overview of the MLOps patterns and practices that can help in the journey of taking models to production.
- An overview of Kubeflow
- How to operationalize models with Kubeflow Pipelines
- Live demos!
Missed part 1 of this blog series? Click here to get the recap of the talks presented at Kubernetes AI Day EU 2021.
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