service 01 Mobile App First mobile app was launched 02 2010 2015 Milestone Go-Jek 1.0 Call-center for ojek service 01 Mobile App First mobile app was launched 02 More Apps! Incl. growth 2010 2015 2017 Milestone Go-Jek 1.0 Call-center for ojek service 01 Mobile App First mobile app was launched 02 More Apps! Incl. growth of Go-Pay into P2P, withdrawal, etc 03 2010 2015 2017 2018 Fintech Acquired three fintech companies in Indonesia 04 Milestone With great number of apps comes great headache a typical flow of an app in Go-Jek… Infrastructure as Code Started in the

import numpy as np from matplotlib import pyplot as plt from tensorflow.keras import applications as apps from tensorflow.keras import layers, optimizers train_ds, val_ds, test_ds = tfds.load( 'oxford_flowers102' Initalize the core model core_args = dict(input_shape=(IMG_SIZE, IMG_SIZE, 3), include_top=False) core = apps.resnet50.ResNet50(**core_args) core.trainable = False # Setup the top model = tf.keras.Sequential([ IMG_SIZE, 3], dtype = tf.uint8), layers.Lambda(lambda x: tf.cast(x, tf.float32)), layers.Lambda(lambda x: apps.resnet.preprocess_input(x)), core, layers.Flatten(), layers.Dropout(dropout_rate), layers.Dense(NUM_CLASSES

Learning to adapt such models for the real world, and a rapid growth in data collected via websites and apps (Facebook, Instagram, Google, etc.), it became possible to train models that performed well on unseen 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

them to transform large and complex models into smaller and efficient models capable of running on mobile and edge devices. We have also set up a couple of programming projects for a hands-on model optimization from sequential data, depthwise separable convolution extended the reach of convolution models to mobile and other devices with limited compute and memory resources. This layer aims to reduce the footprint IEEE conference on computer vision and pattern recognition. 2017. on mobile and edge devices. Let’s say you want to design a mobile application to highlight pets in a picture. A DSC model is a perfect

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Amazon API Gateway, Dynamo dB, Amazon SNS, Amazon Kinesis, CloudFront, Amazon SES, CloudTrail , mobile app etc. Step 4: Executes AWS Lambda Code only when triggered by AWS services under the scenarios  push notification  data streams collection  hosting of website  email sending  mobile app, etc. Step 5 : Remember that AWS charges only when the AWS lambda code executes, and not otherwise click Secure Submit and it will validate the card with the bank and will give you the OTP on your mobile which is linked with the card. You can find a window as shown below:

from ng1 to ng2 (#3152) ● Migrating metrics from Heapster to Kubernetes Metrics API (#2986) ● Apps list page (#2980) Demo Future of Dashboard How do people use Dashboard today, and want to Kubernetes in GCP and on-prem ● Custom Resource Definitions support ● Service topology view ● Mobile device support ● Cost estimates ● CI/CD pipelines ● ...and more! Additional feature requests

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 function (discussed in chapter 2) and the adam optimizer. from tensorflow.keras import applications as apps from tensorflow.keras import layers, optimizers, metrics DROPOUT_RATE = 0.2 LEARNING_RATE = 0.0002 Initialize the core model core_args = dict(input_shape=(IMG_SIZE, IMG_SIZE, 3), include_top=False) core = apps.resnet50.ResNet50(**core_args) core.trainable = False # Create the full model with input, preprocessing

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