Tensorflow London 13: Barbara Fusinska 'Hassle Free, Scalable, Machine Learning with Kubeflow'

•Download as PPTX, PDF•

1 like•9,167 views

Speaker: Barbara Fusinska, Machine Learning Strategic Cloud Engineer at Google Title: Hassle Free, Scalable, Machine Learning with Kubeflow Abstract: Kubeflow uses Kubernetes strengths to build a toolkit for data scientists where they can create, train and publish the models in a hassle-free and scalable way. The goal is to run machine learning workflow without a need to think about the infrastructure. In this talk, Barbara will discuss the capabilities of Kubeflow from the data scientist perspective. The presentation will introduce how you can use the platform to build the models and deploy it adjusting the computation environment. Bio: Barbara is a Machine Learning Strategic Cloud Engineer at Google with strong software development background. While working with a variety of different companies, she gained experience in building diverse software systems. This experience brought her focus to the Data Science and Big Data field. She believes in the importance of the data and metrics when growing a successful business. Alongside collaborating around data architectures, Barbara still enjoys programming activities. Currently speaking at conferences in-between working in London. She tweets at @BasiaFusinska and you can follow her blog. Thanks to all TensorFlow London meetup organisers and supporters: Seldon.io Altoros Rewired Google Developers Rise London

Technology

Hassle free, scalable
Machine Learning with
Kubeflow
Barbara Fusinska
@BasiaFusinska

About me
Machine Learning
Strategic Cloud Engineer
@BasiaFusinska
barbarafusinska.com
barbara@fusinska.com
https://katacoda.com/kubeflow/

Agenda
• What is Machine Learning, Deep
Learning and AI?
• Introduction to Kubeflow
• Data Scientist perspective

Machine Learning
Deep Learning
Artificial Intelligence

What has
changed?
• Big Data
• The Cloud
• GPU
• Training methods
• Tools
• Computer Vision, NLP

Kubernetes
• Container orchestration
• Automatic deployment
• Distributed computing
• Open-source

Kubeflow
• Composable, Portable, Scalable
ML Stack for Kubernetes
• Easy, repeatable deployments
• Components:
• JupyterHub
• TensorFlow Job Operator and
Controller (Custom Resources,
TFJob)
• TensorFlow Model Serving

Machine Learning process
Computation
Feedback

Data Scientist perspective
Code
Algorithm choosing
Hyperparameter tuning
Adjusting the architecture
Building the model
Running production code
Hardware exploit
Model serving
Model consumption

Kubeflow for Machine Learning
Code
Jupyter Notebooks
TFJobs
TFJobs
Jupyter Notebooks
Model Serving

Kubeflow for
Machine
Learning
Demo
https://katacoda.com/kubeflow/

Keep in touch
BarbaraFusinska.com
Barbara@Fusinska.com
@BasiaFusinska
https://katacoda.com/kubeflow/

This talk was recorded in London on Oct 30, 2018 and can be viewed here: https://youtu.be/tNK3Fc02jj0 Arno Candel is the Chief Technology Officer at H2O.ai. He is the main committer of H2O-3 and Driverless AI and has been designing and implementing high-performance machine-learning algorithms since 2012. Previously, he spent a decade in supercomputing at ETH and SLAC and collaborated with CERN on next-generation particle accelerators. Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He was named “2014 Big Data All-Star” by Fortune Magazine and featured by ETH GLOBE in 2015. Follow him on Twitter: @ArnoCandel.

Next Generation Cloud Computing With Google - RightScale Compute 2013

RightScale

Speaker: Martin Gannholm - Lead Engineer, Google Google Cloud Platform provides everything you need to build, run, and scale social, mobile, and online applications. Already, tens of thousands of popular applications like Khan Academy, Angry Birds, SnapChat, and Pulse are benefiting from the power of running on top of Google infrastructure. Come join Google as we go deep on how to best leverage our technology with RightScale to build your next masterpiece.

The Evolution of your Kubernetes Cluster

Kublr

From a skunk-works project to running the entire enterprise While developers see and realize the benefits of Kubernetes, how it improves efficiencies, saves time, and enables focus on the unique business requirements of each project; InfoSec, infrastructure, and software operations teams still face challenges when managing a new set of tools and technologies, and integrating them into an existing enterprise infrastructure. In this meetup, Chris, CTO at Tigera, and Oleg, CTO at Kublr, discussed the evolution of your Kubernetes cluster - from a skunk-works project to running the entire enterprise.

The combination of StackPointCloud with NetApp creates NetApp Kubernetes Service, the industry’s first complete Kubernetes platform for multi-cloud deployments and a complete cloud-based stack for Azure, Google Cloud, AWS, and NetApp HCI. Further, Trident is a fully supported open source project maintained by NetApp, designed from the ground up to help meet the sophisticated persistence demands of containerized applications.

Creating a Kafka Topic. Super easy? | Andrew Stevenson and Marios Andreopoulo...

HostedbyConfluent

Distributed architecture in a cloud native microservices ecosystem

Zhenzhong Xu

[Lakmal] Automate Microservice to API

Lakmal Warusawithana

The recent constraints on businesses have pushed organizations to accelerate their plans for moving operations to the digital world—often shrinking timelines from years to months. Microservice architecture (MSA) is critical to accomplish fast innovation and the APIs exposed from microservices should be secured, managed, observed and monetized. All these steps require significant time. Kubernetes is designed for automation. The Operator pattern captures how you can write code and extend the Kubernetes cluster to automate a task going beyond its out-of-the-box capabilities. In this session, Lakmal will demonstrate and share his experience of how to automate microservice to API by introducing a Kubernetes Operator that works together with an API Management system while enhancing the developer experience.

Microservice Plumbing - Glynn Bird - Codemotion Rome 2017

Codemotion

When HPC meet ML/DL: Manage HPC Data Center with Kubernetes

Yong Feng

Tectonic Summit 2016: Multi-Cluster Kubernetes: Planning for Unknowns

CoreOS

Managing Your Cloud Spend With PlanForCloud - RightScale Compute 2013

RightScale

Manage thousands of k8s applications with minimal efforts using kube carrier

LibbySchulze

Cncf event driven autoscaling with keda

JurajHantk

AI & Machine Learning Pipelines with Knative

Animesh Singh

Event-driven Applications with Kafka, Micronaut, and AWS Lambda | Dave Klein,...

HostedbyConfluent

One of the great things about running applications in the cloud is that you only pay for the resources that you use. But that also makes it more important than ever for our applications to be resource-efficient. This becomes even more critical when we use serverless functions. Micronaut is an application framework that provides dependency injection, developer productivity features, and excellent support for Apache Kafka. By performing dependency injection, AOP, and other productivity-enhancing magic at compile time, Micronaut allows us to build smaller, more efficient microservices and serverless functions. In this session, we'll explore the ways that Apache Kafka and Micronaut work together to enable us to build fast, efficient, event-driven applications. Then we'll see it in action, using the AWS Lambda Sink Connector for Confluent Cloud.

Persist your data in an ephemeral k8 ecosystem

LibbySchulze

Storage os kubernetes clusters need persistent data

LibbySchulze

Jolt: Distributed, fault-tolerant test running at scale using Mesos

Mesosphere Inc.

Introducing Kubestr - A New Way to Explore Your Kubernetes Storage Options

LibbySchulze

Advanced Model Inferencing leveraging Kubeflow Serving, KNative and Istio

Animesh Singh

Model Inferencing use cases are becoming a requirement for models moving into the next phase of production deployments. More and more users are now encountering use cases around canary deployments, scale-to-zero or serverless characteristics. And then there are also advanced use cases coming around model explainability, including A/B tests, ensemble models, multi-armed bandits, etc. In this talk, the speakers are going to detail how to handle these use cases using Kubeflow Serving and the native Kubernetes stack which is Istio and Knative. Knative and Istio help with autoscaling, scale-to-zero, canary deployments to be implemented, and scenarios where traffic is optimized to the best performing models. This can be combined with KNative eventing, Istio observability stack, KFServing Transformer to handle pre/post-processing and payload logging which consequentially can enable drift and outlier detection to be deployed. We will demonstrate where currently KFServing is, and where it's heading towards.

Google Cloud and Data Pipeline Patterns

Lynn Langit

Kubernetes on IBM Cloud + DevOps コンテナCIで簡易アプリ作ってみた

Shoichiro Sakaigawa

Democratizing machine learning on kubernetes

Docker, Inc.

One of the largest challenges facing the machine learning community today is understanding how to build a platform to run common open-source machine learning libraries such as Tensorflow. Both Joy and Lachie are both passionate about making machine learning accessible to the masses using Kubernetes. In this session they'll share how to deploy a distributed Tensorflow training cluster complete with GPU scheduling on Kubernetes. We'll also share how distributed Tensorflow training works, various options for distributed training, and when to choose what option. We'll also share some best practices on using distributed Tensorflow on top of Kubernetes, based on our latest performance tests performed on public cloud providers. All work presented in this session will be accessible via a public Github repository.

Deep Learning with Microsoft Cognitive Toolkit

Barbara Fusinska

What's hot

Scaling Galaxy on Google Cloud Platform

Lynn Langit

Containers and Kubernetes

Altoros

Creating a Kafka Topic. Super easy? | Andrew Stevenson and Marios Andreopoulo...

HostedbyConfluent

Distributed architecture in a cloud native microservices ecosystem

Zhenzhong Xu

[Lakmal] Automate Microservice to API

Lakmal Warusawithana

Microservice Plumbing - Glynn Bird - Codemotion Rome 2017

Codemotion

When HPC meet ML/DL: Manage HPC Data Center with Kubernetes

Yong Feng

Tectonic Summit 2016: Multi-Cluster Kubernetes: Planning for Unknowns

CoreOS

Managing Your Cloud Spend With PlanForCloud - RightScale Compute 2013

RightScale

Manage thousands of k8s applications with minimal efforts using kube carrier

LibbySchulze

Cncf event driven autoscaling with keda

JurajHantk

AI & Machine Learning Pipelines with Knative

Animesh Singh

Event-driven Applications with Kafka, Micronaut, and AWS Lambda | Dave Klein,...

HostedbyConfluent

Persist your data in an ephemeral k8 ecosystem

LibbySchulze

Storage os kubernetes clusters need persistent data

LibbySchulze

Jolt: Distributed, fault-tolerant test running at scale using Mesos

Mesosphere Inc.

Introducing Kubestr - A New Way to Explore Your Kubernetes Storage Options

LibbySchulze

Advanced Model Inferencing leveraging Kubeflow Serving, KNative and Istio

Animesh Singh

Google Cloud and Data Pipeline Patterns

Lynn Langit

Kubernetes on IBM Cloud + DevOps コンテナCIで簡易アプリ作ってみた

Shoichiro Sakaigawa

What's hot (20)

Scaling Galaxy on Google Cloud Platform

Containers and Kubernetes

Creating a Kafka Topic. Super easy? | Andrew Stevenson and Marios Andreopoulo...

Distributed architecture in a cloud native microservices ecosystem

[Lakmal] Automate Microservice to API

Microservice Plumbing - Glynn Bird - Codemotion Rome 2017

When HPC meet ML/DL: Manage HPC Data Center with Kubernetes

Tectonic Summit 2016: Multi-Cluster Kubernetes: Planning for Unknowns

Managing Your Cloud Spend With PlanForCloud - RightScale Compute 2013

Manage thousands of k8s applications with minimal efforts using kube carrier

Cncf event driven autoscaling with keda

AI & Machine Learning Pipelines with Knative

Event-driven Applications with Kafka, Micronaut, and AWS Lambda | Dave Klein,...

Persist your data in an ephemeral k8 ecosystem

Storage os kubernetes clusters need persistent data

Jolt: Distributed, fault-tolerant test running at scale using Mesos

Introducing Kubestr - A New Way to Explore Your Kubernetes Storage Options

Advanced Model Inferencing leveraging Kubeflow Serving, KNative and Istio

Google Cloud and Data Pipeline Patterns

Kubernetes on IBM Cloud + DevOps コンテナCIで簡易アプリ作ってみた

Similar to Tensorflow London 13: Barbara Fusinska 'Hassle Free, Scalable, Machine Learning with Kubeflow'

Democratizing machine learning on kubernetes

Docker, Inc.

Deep Learning with Microsoft Cognitive Toolkit

Barbara Fusinska

S8277 - Introducing Krylov: AI Platform that Empowers eBay Data Science and E...

Henry Saputra

The Krylov Project is the key component in eBay's AI Platform initiative that provides an easy to use, open, and fast AI orchestration engine that is deployed as managed services in eBay cloud. Using Krylov, AI scientists can access eBay's massive datasets; build and train AI models; spin up powerful compute (high-memory or GPU instances) on the Krylov compute cluster; and set up machine learning pipelines, such as using declarative constructs that stitch together pipeline lifecycle.

201908 Overview of Automated ML

Mark Tabladillo

Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.

Machine Learning and AI

James Serra

MLOps with Kubeflow

Saurabh Kaushik

Recreating "The Clock" with Machine Learning and Web Scraping

KP Kaiser

Slides from my 2019 PyGotham talk. “The Clock” is a 2010 art installation by Christian Marclay. It is an experimental film that features over 12,000 individual shots of clocks from movies and television, edited in such a way that the film itself functions as a clock. In this talk, we’ll use modern machine learning models and video web scraping to recreate the concept behind “The Clock”. We’ll use Kubernetes to orchestrate building a modern video scraper, capable of getting around the walls of YouTube and Instagram to grab user content.

Microsoft DevOps for AI with GoDataDriven

GoDataDriven

Artificial Intelligence (AI) and machine learning (ML) technologies extend the capabilities of software applications that are now found throughout our daily life: digital assistants, facial recognition, photo captioning, banking services, and product recommendations. The difficult part about integrating AI or ML into an application is not the technology, or the math, or the science or the algorithms. The challenge is getting the model deployed into a production environment and keeping it operational and supportable. Software development teams know how to deliver business applications and cloud services. AI/ML teams know how to develop models that can transform a business. But when it comes to putting the two together to implement an application pipeline specific to AI/ML — to automate it and wrap it around good deployment practices — the process needs some effort to be successful.

Build and Monitor Machine Learning Services in Kubernetes

KP Kaiser

There are many options to build and deploy machine learning models to production at scale. We’ll walk through the growing suite of tools to centralize model building and deployment at scale, with technologies like Kubeflow, Seldon and TensorRT. Finally, we’ll address techniques for monitoring these new services. You’ll walk away with an understanding of a complete development lifecycle for scalable machine learning services.

High Performance Computing Pitch Deck

Nicholas Vossburg

Infrastructure Agnostic Machine Learning Workload Deployment

Databricks

When it comes to Large Scale data processing and Machine Learning, Apache Spark is no doubt one of the top battle-tested frameworks out there for handling batched or streaming workloads. The ease of use, built-in Machine Learning modules, and multi-language support makes it a very attractive choice for data wonks. However bootstrapping and getting off the ground could be difficult for most teams without leveraging a Spark cluster that is already pre-provisioned and provided as a managed service in the Cloud, while this is a very attractive choice to get going, in the long run, it could be a very expensive option if it’s not well managed. As an alternative to this approach, our team has been exploring and working a lot with running Spark and all our Machine Learning workloads and pipelines as containerized Docker packages on Kubernetes. This provides an infrastructure-agnostic abstraction layer for us, and as a result, it improves our operational efficiency and reduces our overall compute cost. Most importantly, we can easily target our Spark workload deployment to run on any major Cloud or On-prem infrastructure (with Kubernetes as the common denominator) by just modifying a few configurations. In this talk, we will walk you through the process our team follows to make it easy for us to run a production deployment of our Machine Learning workloads and pipelines on Kubernetes which seamlessly allows us to port our implementation from a local Kubernetes set up on the laptop during development to either an On-prem or Cloud Kubernetes environment

Scaling AI/ML with Containers and Kubernetes

Tushar Katarki

AI is popular and yet faces several challenges in the industry: 1) self-service and automation 2) Deployment into production 3) Access to data. These challenges can be addressed with containers and Kubernetes. They help you build AI-as-a-service with open source tools and Kuberentes. Data Scientists can use the service for data, experimentation and to deliver models into production iteratively with self-service and automation. Using Kubernetes, one is able to run massive machine learning pipelines iteratively in an automated fashion that can be repeated.

Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep L...

Simplilearn

Deep Learning covers all the essential Deep Learning frameworks that are necessary to build AI models. In this presentation, you will learn about the development of essential frameworks such as TensorFlow, Keras, PyTorch, Theano, etc. You will also understand the programming languages used to build the frameworks, the different companies that use these frameworks, the characteristics of these Deep Learning frameworks, and type of models that were built using these frameworks. Now, let us get started with understanding the different popular Deep Learning frameworks being used in industries. Below are the different Deep Learning frameworks we'll be discussing in this presentation: 1. TensorFlow 2. Keras 3. PyTorch 4. Theano 5. Deep Learning 4 Java 6. Caffe 7. Chainer 8. Microsoft CNTK Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline 2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces 4. Build deep learning models in TensorFlow and interpret the results 5. Understand the language and fundamental concepts of artificial neural networks 6. Troubleshoot and improve deep learning models 7. Build your own deep learning project 8. Differentiate between machine learning, deep learning, and artificial intelligence Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training

Productionizing Machine Learning - Bigdata meetup 5-06-2019

Iulian Pintoiu

Sergii Baidachnyi ITEM 2018

ITEM

Taking High Performance Computing to the Cloud: Windows HPC and

Saptak Sen

High Performance Computing (HPC) is expected to be the single largest workload on Windows Azure. This session discusses how Windows HPC Server 2008 R2 SP2 enables our customers to easily run their HPC applications on Windows Azure. It covers different usage scenarios (“bursting” to Windows Azure vs. running everything in Windows Azure),differences between running HPC applications on-premise vs. in Azure,best practices,limitations,etc. Real-world customers and their scenarios are highlighted. The key points are illustrated with live demos of HPC applications running in Windows Azure. This session is a must for everyone who wants to know about HPC and Windows Azure.

Making Data Scientists Productive in Azure

Valdas Maksimavičius

Slides from my talk at Big Data Conference 2018 in Vilnius Doing data science today is far more difficult than it will be in the next 5-10 years. Sharing, collaborating on data science workflows in painful, pushing models into production is challenging. Let’s explore what Azure provides to ease Data Scientists’ pains. What tools and services can we choose based on a problem definition, skillset or infrastructure requirements? In this talk, you will learn about Azure Machine Learning Studio, Azure Databricks, Data Science Virtual Machines and Cognitive Services, with all the perks and limitations.

Hybrid Kafka, Taking Real-time Analytics to the Business (Cody Irwin, Google ...

HostedbyConfluent

Apache Kafka users who want to leverage Google Cloud Platform's (GCPs) data analytics platform and open source hosting capabilities can bridge their existing Kafka infrastructure on-premise or in other clouds to GCP using Confluent's replicator tool and managed Kafka service on GCP. Using actual customer examples and a reference architecture, we'll showcase how existing Kafka users can stream data to GCP and use it in popular tools like Apache Beam on Dataflow, BigQuery, Google Cloud Storage (GCS), Spark on Dataproc, and Tensorflow for data warehousing, data processing, data storage, and advanced analytics using AI and ML.

AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)

Amazon Web Services

Algorithmia is a startup with a mission to make state of the art machine learning discoverable by everyone&emdash;they offer the largest algorithm marketplace in the world, with over 2500 algorithms supporting tens of thousands of application developers. Algorithma is the first company to make deep learning, one of the most conceptually difficult areas of computing, accessible to any company via microservices. In this session, you learn how this startup has selected and optimized Amazon EC2 instances for various algorithms (including the latest generation of GPU optimized instances), to create a flexible and scalable platform. They also share their architecture and best practices for getting any computationally-intensive application started quickly.

AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...

James Serra

Discover, manage, deploy, monitor – rinse and repeat. In this session we show how Azure Machine Learning can be used to create the right AI model for your challenge and then easily customize it using your development tools while relying on Azure ML to optimize them to run in hardware accelerated environments for the cloud and the edge using FPGAs and Neural Network accelerators. We then show you how to deploy the model to highly scalable web services and nimble edge applications that Azure can manage and monitor for you. Finally, we illustrate how you can leverage the model telemetry to retrain and improve your content.

Similar to Tensorflow London 13: Barbara Fusinska 'Hassle Free, Scalable, Machine Learning with Kubeflow' (20)

Democratizing machine learning on kubernetes

Deep Learning with Microsoft Cognitive Toolkit

S8277 - Introducing Krylov: AI Platform that Empowers eBay Data Science and E...

201908 Overview of Automated ML

Machine Learning and AI

MLOps with Kubeflow

Recreating "The Clock" with Machine Learning and Web Scraping

Microsoft DevOps for AI with GoDataDriven

Build and Monitor Machine Learning Services in Kubernetes

High Performance Computing Pitch Deck

Infrastructure Agnostic Machine Learning Workload Deployment

Scaling AI/ML with Containers and Kubernetes

Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep L...

Productionizing Machine Learning - Bigdata meetup 5-06-2019

Sergii Baidachnyi ITEM 2018

Taking High Performance Computing to the Cloud: Windows HPC and

Making Data Scientists Productive in Azure

Hybrid Kafka, Taking Real-time Analytics to the Business (Cody Irwin, Google ...

AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)

AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...

More from Seldon

CD4ML and the challenges of testing and quality in ML systems

Tensorflow London 13: Barbara Fusinska 'Hassle Free, Scalable, Machine Learning with Kubeflow'

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Tensorflow London 13: Barbara Fusinska 'Hassle Free, Scalable, Machine Learning with Kubeflow'

Similar to Tensorflow London 13: Barbara Fusinska 'Hassle Free, Scalable, Machine Learning with Kubeflow' (20)

More from Seldon

More from Seldon (20)

Recently uploaded

Recently uploaded (20)

Tensorflow London 13: Barbara Fusinska 'Hassle Free, Scalable, Machine Learning with Kubeflow'