Chapter 7 - Automation "There's a lot of automation that can happen that isn't a replacement of humans but of mind-numbing behavior." - Stewart Butterfield, Founder (Slack) We have talked about a variety tensorflow.keras import layers, optimizers train_ds, val_ds, test_ds = tfds.load( 'oxford_flowers102', split=['train', 'validation', 'test'], as_supervised=True, read_config=tfds.ReadConfig(try_autocache=False) return image, label train_ds = train_ds.map(resize_image) val_ds = val_ds.map(resize_image) test_ds = test_ds.map(resize_image) Note that the create_model() function here has two additional parameters:

#IstioCon Building resilient systems inside the mesh: abstraction and automation of Virtual Service generation Vladimir Georgiev, Thought Machine #IstioCon Sync calls failures inside the mesh

State of the UI: Leveraging Kubernetes Dashboard and Shaping its Future Dan Romlein UX Designer, Google Spencer Sugarman UX Researcher, Google Talk Goals 1. What Dashboard is and why you should rd SIG-UI Team Piotr Bryk, Google Jeffrey Sica, University of Michigan Jim Angel, General Motors Sebastian Florek (co-lead), Loodse Marcin Maciaszczyk, Loodse Peng Xiao, Alauda Why a UI? 60% of survey takers use a UI to monitor or manage their resources in Kubernetes https://unsplash.com/ https://github.com/kubernetes/dashboard/issues /3256#issuecomment-437199403 UIs help you understand

Train-Val-Test划分 主讲人：龙良曲 Recap How to detect Splitting Train Set Test Set For example 60K 10K test while train train test trade-off Overfitt ing For others judge ▪ Kaggle Train Set Test Set Set Val Set Unavailable train-val-test K-fold cross-validation Train Set Test Set Val Set k-fold cross validation ▪ merge train/val sets ▪ randomly sample 1/k as val set 下一课时 减轻Overfitting Thank

also introduce core areas of efficiency techniques (compression techniques, learning techniques, automation, efficient models & layers, infrastructure). Our hope is that even if you just read this chapter case). For example, if you are deploying a model on devices where inference is constrained (such as mobile and embedded devices), or expensive (cloud servers), it might be worth paying attention to inference liable for data breaches. The law went into effect in 2018. Figure 1-5: Growth in the number of mobile and IoT devices over time. The lighter blue bars represent forecasts. (Data Source: 1, 2) In this

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restore production workloads in Kubernetes environments and protects production and development, or test workloads to ensure that the data is easy to backup and restore. PowerProtect Data Manager enhances infrastructure platform and modernize your application architectures on your schedule. • Extensive automation for predictability and simpler workflows PowerFlex offers full-stack IT Operations Management Management (ITOM) and Life Cycle Management (LCM) with PowerFlex Manager. It provides extensive automation capabilities with PowerFlex Manager REST APIs and custom Ansible modules to integrate with your infrastructure

goals. High quality models have an additional benefit in footprint constrained environments like mobile and edge devices where they provide the flexibility to trade off some quality for smaller footprints others’. Let’s understand it with an example. Assume that we are working on a model for a home-automation device. Figure 3-4 shows the high level workflow of such a device. The model continuously classifies indicates the absence of an acceptable keyword in the input signal. Figure 3-4: Workflow of a home-automation device which detects three spoken words: hello weather and time. The output is none when none

even whole channels. Libraries like XNNPACK3,4 can help accelerate networks on a variety of web, mobile, and embedded devices, provided the user can design networks that match their constraints. One might val_loss: 0.5619 - val_accuracy: 0.8460 # Evaluate the pruned model on the test set. model_for_pruning_acc = model_for_pruning.evaluate(test_prep_ds.batch(256))[1] print('Accuracy: ', model_for_pruning_acc) Accuracy: 0.8471 Recall that the regular model performed with a 85.11% accuracy on the test set. Our pruned model performed with an accuracy of 84.71%. It's a slight drop in performance. Let's go ahead

Architecture and Features • CRD and operator design • Pipeline/Stage/Task/Task Template/Version Control/UI generation/Volume... • Logging, monitoring, autoscaling, high availability • Extensibility/Integration prompt innovation • New features of k8s may help enhance or improve DevOps • Help integration test - use sidecar container as dependent environment • Encapsulate API / SDK of other tools using image Architecture and Features • CRD and operator design • Pipeline/Stage/Task/Task Template/Version Control/UI generation/Volume... • Logging, monitoring, autoscaling, high availability • Extensibility/Integration