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

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

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.

She’s a Hazard to Herself She’s a Hazard Hi there, I’m Mallory, and I’d like to invite you into our world with “She’s a Hazard to Herself!” Join us as we navigate life with Multiple Sclerosis from the seat of my power wheelchair. Discover stories of resilience, family, and the community we’ve built around chronic illness. Whether you’re impacted by MS or want to learn from our journey, there’s something here for you. So why wait? Subscribe to “She’s a Hazard to Herself” on your favorite podcast app and be part of our journey today. Let’s lift each other up, one episode at a time! Tips, News and Stories for Older Adults Esther C Kane CAPS, C.D.S. "Tips, News, and Stories for Older Adults" delivers weekly insights tailored for seniors. We bring you summaries of curated news, practical advice, and inspiring stories that matter to the 55+ community. From health and finance to technology and lifestyle, our content keeps you informed and engaged. Sourced from trusted outlets, each episode offers valuable information for navigating your golden years. Join us as we explore aging with positivity, wisdom, and engaging stories. Your perfect companion for staying active, learning, and embracing life's later chapters. Prayer Time Heir Waves Prayer Time A podcast especially for our Prayer Time community NEWMORROW SESSIONS - A PodCast Series on the Future of Hospitality Mario C. Bauer, Florian Schneider, Axel Weber & Dr. Tillman Bardt The Newmorrow PodCast is more than a podcast — it's a platform for open dialog on the future of our business, a platform for those building what doesn’t exist yet. Here, we share and embrace our passion for the hospitality industry, but we won’t romanticize the journey. We ask the tough questions, confront uncomfortable truths, and prepare for a future that resists easy answers. We believe that the tougher and wilder times become, the more openly, honestly and humanely people need to talk to each other and act together. We believe, openness, togetherness, and truthfulness should also be cornerstones of a professional community to develop our utopian idea of „open source“. This is a space where visionaries don’t just imagine the future — they wrestle with the paradoxes that shape it: success vs. happiness, data vs. instinct, stability vs. reinvention. Join leaders, entrepreneurs, and thinkers as they share not what made them — but what’s actively shaping them, now and next. So tune in

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.

What is this episode about?

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:...

Can I download this MLOps.community episode?

Yes, you can download this episode by clicking the download button on the episode player, or subscribe to the podcast in your preferred podcast app for automatic downloads.
URL copied to clipboard!