Hands-on serving models using KFserving // Theofilos Papapanagiotou // Data Science Architect at Prosus // MLOps Meetup #40 episode artwork

EPISODE · Oct 30, 2020 · 57 MIN

Hands-on serving models using KFserving // Theofilos Papapanagiotou // Data Science Architect at Prosus // MLOps Meetup #40

from MLOps.community · host Demetrios

MLOps community meetup #40! Last Wednesday, we talked to Theofilos Papapanagiotou, Data Science Architect at Prosus, about Hands-on Serving Models Using KFserving.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠// Abstract:We looked at some popular model formats like the SavedModel of Tensorflow, the Model Archiver of PyTorch, pickle&ONNX, to understand how the weights of the NN are saved there, the graph, and the signature concepts.We discussed the relevant resources of the deployment stack of Istio (the Ingress gateway, the sidecar, and the virtual service) and Knative (the service and revisions), as well as Kubeflow and KFServing. Then we got into the design details of KFServing, its custom resources, the controller and webhooks, the logging, and configuration.We spent a large part in the monitoring stack, the metrics of the servable (memory footprint, latency, number of requests), as well as the model metrics like the graph, init/restore latencies, the optimizations, and the runtime metrics, which end up in Prometheus. We looked at the inference payload and prediction logging to observe drifts and trigger the retraining of the pipeline.Finally, a few words about the awesome community and the roadmap of the project on multi-model serving and inference routing graph.// Bio:Theo is a recovering Unix Engineer with 20 years of work experience in Telcos, on internet services, video delivery, and cybersecurity. He is also a university student for life; BSc in CS 1999, MSc in Data Coms 2008, and MSc in AI 2017.Nowadays, he calls himself an ML Engineer, as he expresses his passion for System Engineering and Machine Learning.His analytical thinking is driven by curiosity and a hacker spirit. He has skills that span a variety of different areas: Statistics, Programming, Databases, Distributed Systems, and Visualization.----------- Connect With Us ✌️-------------  Join our Slack community:  https://go.mlops.community/slackFollow us on Twitter:  @mlopscommunitySign up for the next meetup:  https://go.mlops.community/registerConnect with Demetrios on LinkedIn:  https://www.linkedin.com/in/dpbrinkm/Connect with Theofilos on LinkedIn:  https://linkedin.com/in/theofpa

Episode metadata supplied by the publisher feed · Published Oct 30, 2020

Embed this episode

NOW PLAYING

Hands-on serving models using KFserving // Theofilos Papapanagiotou // Data Science Architect at Prosus // MLOps Meetup #40

0:00 57:45

No transcript for this episode yet

We transcribe on demand. Request one and we'll notify you when it's ready — usually under 10 minutes.

No similar episodes found.

No similar podcasts found.

Frequently Asked Questions

How long is this episode of MLOps.community?

This episode is 57 minutes long.

When was this MLOps.community episode published?

This episode was published on October 30, 2020.

Can I download this MLOps.community episode?

Yes. Use the download control on the episode player to save the publisher-provided media file.
URL copied to clipboard!