Machine Learning made easy
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The document outlines a coding demo titled 'Machine Learning Made Easy' presented by Ian Schröder and Keith Tenzer on February 21, 2019. It covers the fundamental concepts of machine learning, including its accessibility, applications, and the use of containers for ease of deployment. The demo highlights resources, methodologies, and frameworks for getting started with machine learning, along with a GitHub repository for further exploration.
Machine Learning made easy
- 1. Cloud Native Night Feb/21 A coding demo "Machine Learning made easy” Ian Schröder, Solution Specialist Middleware, jschroed@redhat.com Keith Tenzer, Solutions Architect, ktenzer@redhat.com 21 February 2019
- 2. AGENDA 2 What is Machine Learning? 3 important Points DEMO
- 3. 3 ABOUT KEITH AND IAN 20 Linux/Storage Admin, Developer, Architect 1 http://keithtenzer.com 15 Joined Red Hat 47 Years experience working with people 1 Deinecholeben.blogspot.de 18 Joined Red Hat
- 4. 4 WHAT IS MACHINE LEARNING? Deep Learning Machine Learning Artificial Intelligence Algorithms that parse data, learn from the data and then apply learning to make informed decisions. A broad field where machines are cognitive, able to analyze environment in order to make decisions. A subset of machine learning, algorithms can determine on their own if prediction is accurate or not, meaning they learn without supervision.
- 5. 5 ● Machine learning is open for everyone ○ Machine Learning is not a proprietary offering from the major cloud providers ● Machine learning needs access to Data and Runtimes Take control with Container ○ Agile microservice frameworks, Apps and runtimes offers flexibility ○ Cloud-to-Cloud and On-Premise, mash your own network ○ Connect data-lakes to find information in your data ● Machine learning can be easy THREE IMPORTANT POINTS
- 7. 7 CONTAINER MAKE ML easy Python Compilers Polyglot Applications Datasets Models CI/CD GPUs Lots of memory Lots of Data ML Frameworks Lots of decoupled moving parts API´s
- 8. 8 MACHINE LEARNING GETTING STARTED...
- 9. 9 HOW MACHINE LEARNING WORKS Choose Use Case Categorize Data Label Data Setup layers (neural network) Accuracy, optimize and training metrics Compile model Train model with training dataset Evaluate accuracyImprove Training Cycle Application Data Ingestion Analyze dataset based on model Predictions, Findings and Decisions Run Cycle
- 11. 11 MACHINE LEARNING AREA 2 START Anomaly Detection = Predictive
- 12. 12 ● GitHub Repository - https://github.com/ktenzer/openshift-ml-demo ● Blog - https://keithtenzer.com/2018/12/14/getting-started-with-machi ne-learning/ RESOURCES
- 13. Q&A
- 14. Keith Tenzer Senior Solution Architect Red Hat GmbH ktenzer@redhat.com +49 151 44051401 linkedin.com/company/red-hat youtube.com/user/RedHatVideos facebook.com/redhatinc twitter.com/RedHat Ian Schroeder Solution Specialist Middleware Red Hat GmbH ian.schroeder@redhat.com +49 172 8471401
- 15. BACKUP SLIDES
- 16. 16 DEMO APPLICATION ROLLOUT PodContainer Fedora Base C++ Dev tools Python + devel Python modules Nodejs BUILD DEPLOY RUN Container Fedora Base C++ Dev tools Python + devel Python modules Nodejs /deepspeech https://github.com/ktenzer/openshift-ml-demo Download model Start Nodejs 8080 Start Script Registry OpenShift
- 17. 17 OPENSHIFT: OUR FOUNDATION FOR AI / MLLEVERAGE OPTIMAL CLOUD-NATIVE APP DEV CAPABILITIES AWS Microsoft Azure Google CloudOpenStackDatacenterLaptop APPLICATION LIFECYCLE MANAGEMENT ENTERPRISE CONTAINER HOST (RHEL) CONTAINER ORCHESTRATION AND MANAGEMENT (KUBERNETES) Much more than just RHEL + Docker + K8S: ● Security + CI/CD pipelines + hybrid cloud management + container-native storage + networking ● Microservices infrastructure: Istio service mesh for routing & traffic control, security, availability, and identity services ● Certified plugin/interoperability with leading storage, network vendors ● Available & optimized for private & public clouds in self-managed or RHT managed offerings ● Fully integrated with RHT middleware platforms & services
- 18. 18 GPU SUPPORT IN OPENSHIFT ● Joint collaboration with strategic partners for drivers, plugins and container images ● Device Manager GA ● Scheduler: Priority and preemption ● Seamless install experience of drivers, plugins and dependencies ● Container images in RHCC/ISV Registry ● Certifications and support RHEL Host Device Manager plugin Kublet Device Manager Kubernetes SchedulerRHEL base image + Vendor libraries for GPUs + Frameworks for AI/ML such as Tensorflow or Pytorch Device drivers for GPU
- 19. 19 Upstream Community Project with Google, RedHat, Microsoft, Intel, Caicloud and others Democratizing AI with Machine Learning for Everyone Challenges in ML that Kubeflow is addressing: Composability, Portability, Scalability KUBEFLOW
- 20. 20 End-to-End Kubeflow Workflow Experiment with Jupyter Deploy Kubeflow on OpenShift Deploy TensorFlow, PyTorch, Caffe2, Horovod to build model Katib for Hyperparameter Tuning Kubeflow Volume Controller Model Serving and Inference with TensofFlowServing or SeldonCore Data Scientists S3 Object Store Build container image of model Integrate model with your application Operators App Developers Consumers Argo for workflow design