14 May 2021
Don’t miss out! Join us at our upcoming event: KubeCon + CloudNativeCon North America 2021 in Los Angeles, CA from October 12-15. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
Embrace DevOps Practices to ML Pipeline Development - Tommy Li & Yihong Wang, IBM
DevOps practices are broadly adopted to facilitate the software development, testing, delivery and deployment cycles. It also smooths the transition of a software application from development to production. Kubeflow is a machine learning (ML) toolkit on top of Kubernetes. Kubeflow Pipelines with Tekton is an end-to-end workflow orchestration tool which manages the pipelines execution on Tekton backend. Enabling DevOps practices to complete the integration of end-to-end workflow is part of our journey while developing kfp-tekton. I will share the experience about consolidating ML end-to-end scenarios, Build/Deployment status dashboard, Slack channel notification into the DevOps process alongside the Continuous Integration (CI) and Continuous Deployment (CD) on target Kubernetes cluster. I will also cover the tools and services to overcome the challenges we faced.
Embrace DevOps Practices to ML Pipeline Development - Tommy Li & Yihong Wang, IBM
DevOps practices are broadly adopted to facilitate the software development, testing, delivery and deployment cycles. It also smooths the transition of a software application from development to production. Kubeflow is a machine learning (ML) toolkit on top of Kubernetes. Kubeflow Pipelines with Tekton is an end-to-end workflow orchestration tool which manages the pipelines execution on Tekton backend. Enabling DevOps practices to complete the integration of end-to-end workflow is part of our journey while developing kfp-tekton. I will share the experience about consolidating ML end-to-end scenarios, Build/Deployment status dashboard, Slack channel notification into the DevOps process alongside the Continuous Integration (CI) and Continuous Deployment (CD) on target Kubernetes cluster. I will also cover the tools and services to overcome the challenges we faced.
- 2 participants
- 22 minutes
14 May 2021
Don’t miss out! Join us at our upcoming event: KubeCon + CloudNativeCon North America 2021 in Los Angeles, CA from October 12-15. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
Fair Scheduling for Deep Learning Workloads in Kubernetes - Yodar Shafrir, Run:AI
In this talk we will deep dive into pod scheduling in Kubernetes. We'll discuss the importance of fair scheduling in use cases where Data Scientists share a cluster with a limited number of GPUs and examine the requirements for a fair scheduling solution. We'll look at the architecture and components of the Kubernetes scheduling and discuss how we can achieve fairness. We will also share some of the building blocks we used when building our own fair scheduler.
Fair Scheduling for Deep Learning Workloads in Kubernetes - Yodar Shafrir, Run:AI
In this talk we will deep dive into pod scheduling in Kubernetes. We'll discuss the importance of fair scheduling in use cases where Data Scientists share a cluster with a limited number of GPUs and examine the requirements for a fair scheduling solution. We'll look at the architecture and components of the Kubernetes scheduling and discuss how we can achieve fairness. We will also share some of the building blocks we used when building our own fair scheduler.
- 1 participant
- 10 minutes
14 May 2021
Don’t miss out! Join us at our upcoming event: KubeCon + CloudNativeCon North America 2021 in Los Angeles, CA from October 12-15. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
Kubernetes to serve a fraud detection model - Gabriela Souza de Melo, Legiti
In this talk, Gabriela will present how Legiti has used a Kubernetes cluster for serving a real-time fraud detection machine learning application. This application is the most crucial API in Legiti, being the main point of contact with their customers, and being reached in a critical moment by their customers: in the middle of the transaction processing flow, and while the end users are waiting to know if their transaction has been accepted and their order placed. By leveraging Kubernetes' scaling capabilities, Legiti has ensured a reliable server, capable of handling their customers' transaction peaks without degradation of model performance, response quality or latency. Kubernetes also makes it easy to have auto deployments that have no impact on production reliability, with easy ways to set up automated rollbacks based on CI/CD end to end tests results. Using Kubernetes' Python client and pod management and configurations available by Kubernetes, Legiti was able to incorporate business logic that requires for the applications to get restarted with simple Python scripts.
Pearson Henri, Co-founder & CTO of Legiti, will join for the live Q+A session following this talk!
Kubernetes to serve a fraud detection model - Gabriela Souza de Melo, Legiti
In this talk, Gabriela will present how Legiti has used a Kubernetes cluster for serving a real-time fraud detection machine learning application. This application is the most crucial API in Legiti, being the main point of contact with their customers, and being reached in a critical moment by their customers: in the middle of the transaction processing flow, and while the end users are waiting to know if their transaction has been accepted and their order placed. By leveraging Kubernetes' scaling capabilities, Legiti has ensured a reliable server, capable of handling their customers' transaction peaks without degradation of model performance, response quality or latency. Kubernetes also makes it easy to have auto deployments that have no impact on production reliability, with easy ways to set up automated rollbacks based on CI/CD end to end tests results. Using Kubernetes' Python client and pod management and configurations available by Kubernetes, Legiti was able to incorporate business logic that requires for the applications to get restarted with simple Python scripts.
Pearson Henri, Co-founder & CTO of Legiti, will join for the live Q+A session following this talk!
- 1 participant
- 18 minutes
14 May 2021
Don’t miss out! Join us at our upcoming event: KubeCon + CloudNativeCon North America 2021 in Los Angeles, CA from October 12-15. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
ML Ops design patterns with Kubeflow Pipelines - Amy Unruh, Google
When moving your ML workflows from notebook exploration to production, many new problems can arise. We'll talk about some of the reasons this transition can be difficult; discuss patterns that can address these problems; then show how Kubeflow Pipelines (KFP) can be used to support and implement these patterns, and demo KFP in action.
ML Ops design patterns with Kubeflow Pipelines - Amy Unruh, Google
When moving your ML workflows from notebook exploration to production, many new problems can arise. We'll talk about some of the reasons this transition can be difficult; discuss patterns that can address these problems; then show how Kubeflow Pipelines (KFP) can be used to support and implement these patterns, and demo KFP in action.
- 1 participant
- 20 minutes
14 May 2021
Don’t miss out! Join us at our upcoming event: KubeCon + CloudNativeCon North America 2021 in Los Angeles, CA from October 12-15. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
Scaling ML pipelines with KALE — the Kubeflow Automated Pipeline Engine - Salman Iqbal, Learnk8s
One of the most common hurdles with developing AI and deep learning models is to design data pipelines that can operate at scale and in real-time. Data scientists and engineers are often expected to learn, develop and maintain the infrastructure for their experiments. What's the best setup and what's involved in getting models being production-ready? Where do you start? In this talk, you will learn about KALE — the Kubeflow Automated Pipeline Engine. With KALE you can finally link the work done by data scientists in Jupyter Notebooks to a production-grade pipeline that trains the models at scale and serves them in real-time.
Scaling ML pipelines with KALE — the Kubeflow Automated Pipeline Engine - Salman Iqbal, Learnk8s
One of the most common hurdles with developing AI and deep learning models is to design data pipelines that can operate at scale and in real-time. Data scientists and engineers are often expected to learn, develop and maintain the infrastructure for their experiments. What's the best setup and what's involved in getting models being production-ready? Where do you start? In this talk, you will learn about KALE — the Kubeflow Automated Pipeline Engine. With KALE you can finally link the work done by data scientists in Jupyter Notebooks to a production-grade pipeline that trains the models at scale and serves them in real-time.
- 1 participant
- 25 minutes
14 May 2021
Don’t miss out! Join us at our upcoming event: KubeCon + CloudNativeCon North America 2021 in Los Angeles, CA from October 12-15. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
Stand up for ethical AI! How to detect and mitigate AI bias using Kubeflow - Andrew Butler & Animesh Singh, IBM
The application of AI algorithms in domains such as criminal justice, credit scoring, and hiring holds great promise. At the same time, it raises legitimate concerns about algorithmic fairness. There’s a growing demand for fairness, accountability, and transparency in ML systems. It is important to note that training data isn’t the only source of possible bias and adversarial contamination. It can also be introduced through inappropriate data handling or model selection, or even incorrect algorithm design. To address this, this talk will examine how to build a pipeline that’s open, transparent, secure, fair, and that fully integrates into the AI lifecycle by leveraging Kubeflow Pipelines. Then the correct method to deploy machine learning models in production and collect payloads to enable inbuilt bias checkers using KFServing will be detailed.
Stand up for ethical AI! How to detect and mitigate AI bias using Kubeflow - Andrew Butler & Animesh Singh, IBM
The application of AI algorithms in domains such as criminal justice, credit scoring, and hiring holds great promise. At the same time, it raises legitimate concerns about algorithmic fairness. There’s a growing demand for fairness, accountability, and transparency in ML systems. It is important to note that training data isn’t the only source of possible bias and adversarial contamination. It can also be introduced through inappropriate data handling or model selection, or even incorrect algorithm design. To address this, this talk will examine how to build a pipeline that’s open, transparent, secure, fair, and that fully integrates into the AI lifecycle by leveraging Kubeflow Pipelines. Then the correct method to deploy machine learning models in production and collect payloads to enable inbuilt bias checkers using KFServing will be detailed.
- 2 participants
- 30 minutes
14 May 2021
Don’t miss out! Join us at our upcoming event: KubeCon + CloudNativeCon North America 2021 in Los Angeles, CA from October 12-15. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
Taming the Beast: Managing the day 2 operational complexity of Kubeflow - Mofi Rahman & Paul van Eck, IBM
Kubeflow is a machine learning toolkit for Kubernetes where users can develop, deploy, and manage ML workflows in a scalable and portable manner. Kubeflow is composed of many components such as notebooks, service meshes, and pipelines, coupled with their many potential configurations and dependencies. All these moving parts can be daunting for an end user and make Kubeflow impossible to manage and maintain even for an experienced teams. To help alleviate these woes, this session will provide users some insight into how they can navigate this confusing landscape. Tips regarding deployment and configuration, as well as lessons learned from both managing Kubeflow deployments and contributing to upstream Kubeflow, will be provided to help users tame the platform’s operational complexity.
Taming the Beast: Managing the day 2 operational complexity of Kubeflow - Mofi Rahman & Paul van Eck, IBM
Kubeflow is a machine learning toolkit for Kubernetes where users can develop, deploy, and manage ML workflows in a scalable and portable manner. Kubeflow is composed of many components such as notebooks, service meshes, and pipelines, coupled with their many potential configurations and dependencies. All these moving parts can be daunting for an end user and make Kubeflow impossible to manage and maintain even for an experienced teams. To help alleviate these woes, this session will provide users some insight into how they can navigate this confusing landscape. Tips regarding deployment and configuration, as well as lessons learned from both managing Kubeflow deployments and contributing to upstream Kubeflow, will be provided to help users tame the platform’s operational complexity.
- 2 participants
- 25 minutes
14 May 2021
Don’t miss out! Join us at our upcoming event: KubeCon + CloudNativeCon North America 2021 in Los Angeles, CA from October 12-15. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
The SAME Project: A Cloud Native Approach to Reproducible Machine Learning - David Aronchick, Microsoft
We live in a time of both feast and famine in machine learning. Large organizations are publishing state-of-the-art models at an ever-increasing rate but the average data scientist face daunting challenges to reproduce the results themselves. Even in the best cases, where a newly forked code runs without syntax errors (often not the case), this only solves a part of the problem as the pipelines used to run the models are often completely excluded. The Self-Assembling Machine Learning Environment (SAME) project is a new Kubernetes and Kubeflow project and community around a common goal: creating tooling that allows for quick ramp-up, seamless collaboration and efficient scaling. This talk will discuss our initial public release, done in collaboration with data scientists from across the spectrum, where we are going next and how people can use our learnings in their own practices.
The SAME Project: A Cloud Native Approach to Reproducible Machine Learning - David Aronchick, Microsoft
We live in a time of both feast and famine in machine learning. Large organizations are publishing state-of-the-art models at an ever-increasing rate but the average data scientist face daunting challenges to reproduce the results themselves. Even in the best cases, where a newly forked code runs without syntax errors (often not the case), this only solves a part of the problem as the pipelines used to run the models are often completely excluded. The Self-Assembling Machine Learning Environment (SAME) project is a new Kubernetes and Kubeflow project and community around a common goal: creating tooling that allows for quick ramp-up, seamless collaboration and efficient scaling. This talk will discuss our initial public release, done in collaboration with data scientists from across the spectrum, where we are going next and how people can use our learnings in their own practices.
- 1 participant
- 28 minutes
