Google Cloud Platform provides us with a wealth of resources to support data science, deep learning, and AI projects. Now all we need to care about is how to design and train models, and the platform manages the rest tasks. In current pandemic environment, the entire process of an AI project from design, coding to deployment, can be done remotely on the Cloud Platform. IMPORTANT: If you get the following notification when you create a VM that contains GPUs. You need to increase your GPU quota.
Google has one of the largest machine learning stacks in the industry, currently centering on its Google Cloud AI and Machine Learning Platform. Google spun out TensorFlow as open source years ago, but TensorFlow is still the most mature and widely cited deep learning framework. Similarly, Google spun out Kubernetes as open source years ago, but it is still the dominant container management system. Google is one of the top sources of tools and infrastructure for developers, data scientists, and machine learning experts, but historically Google AI hasn't been all that attractive to business analysts who lack serious data science or programming backgrounds. The Google Cloud AI and Machine Learning Platform includes AI building blocks, the AI platform and accelerators, and AI solutions.
With an electrical engineering education from Purdue University (PhD) and the Indian Institute of Technology Bombay (BS, MS), and nearly 25 years of experience in the semiconductor, systems, and hyperscale service provider industries, he has a broad perspective on hardware design and deployment. The elasticity of cloud infrastructure is a key enabler for enterprises and internet services, creating a shared pool of compute resources that various tenants can draw from as their workloads ebb and flow. Cloud tenants are spared the details of capacity and supply planning. This worked well because modern server systems are very efficient at a multitude of general computing tasks. Deep learning, however, creates new complexities for this model.
Imagine you would like to train a deep learning model where you have thousands of images, but your system does not have any GPU. It would be hard to train large training models without GPU, so you will generally use google collab to train your model using google's GPU's. Consider your system memory is full, and you have important documents and videos to be stored and should be secured. Google drive can be one solution to store all your files, including documents, images, and videos up to 15GB, and offers security and back-up. Above mentioned scenarios are some of the applications of Cloud Computing, one of the advantages of using cloud computing is that you only pay for what we use.
"Our research is investigating a training algorithm to obtain a global model as if it is trained by aggregating data in a single server, even when the data are placed in distributed servers, such as in edge computing," according to the statement. NTT's proposed technology has enabled developers to successfully train a global model in early experiments-even in cases where different types of data are used and the communication between servers is "asynchronous," meaning that each compute node's results are not dependent on receiving data and results from another node. NTT notes that interest in edge computing is growing because of the benefits for lower application latency, and expects that there will be community interest in the application of its research to edge compute and networking services. The company said it will continue to develop the technology for commercial applications, and will release the source code to promote collaboration.
The TensorFlow ecosystem has become very popular for developing applications involving deep learning. One of the reasons is that it has a strong community and a lot of tools have been developed around the core library to support developers. In this tutorial, I will guide you through how to prototype models in google colab, train it on Google Cloud AI Platform, and deploy the finalized model on Google Cloud AI Platform for production. I will include the working Google colab notebooks to recreate the work. Google colab is a free resource for prototyping models in TensorFlow and comes with various runtime.
The remarkable predictive performance of deep neural networks (DNNs) has led to their adoption in service domains of unprecedented scale and scope. However, the widespread adoption and growing commercialization of DNNs have underscored the importance of intellectual property (IP) protection. Devising techniques to ensure IP protection has become necessary due to the increasing trend of outsourcing the DNN computations on the untrusted accelerators in cloud-based services. The design methodologies and hyper-parameters of DNNs are crucial information, and leaking them may cause massive economic loss to the organization. Furthermore, the knowledge of DNN's architecture can increase the success probability of an adversarial attack where an adversary perturbs the inputs and alter the prediction. In this work, we devise a two-stage attack methodology "DeepPeep" which exploits the distinctive characteristics of design methodologies to reverse-engineer the architecture of building blocks in compact DNNs. We show the efficacy of "DeepPeep" on P100 and P4000 GPUs. Additionally, we propose intelligent design maneuvering strategies for thwarting IP theft through the DeepPeep attack and proposed "Secure MobileNet-V1". Interestingly, compared to vanilla MobileNet-V1, secure MobileNet-V1 provides a significant reduction in inference latency ($\approx$60%) and improvement in predictive performance ($\approx$2%) with very-low memory and computation overheads.
Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.
Build and Deploy Machine Learning programs on the cloud Last Updated:04/2020 Instructor: Vinay Phadnis Preview this course About this Course This course the fundamentals of Cloud Computing and work on a specific skillset required to build and deploy Machine Learning related applications on cloud infrastructure. This course is divided into 5 sections: Introduction to Cloud Computing: This section deals with the basic concepts related to cloud infrastructure. Can act as a good entry point for beginners just starting with cloud computing. Fundamentals of Machine Learning: This section deals with the basic concepts related to Machine Learning like Neural Networks, Optimisation, Deep-Learning. Can act as a good entry point for beginners getting started with Machine Learning Hands-on Machine Learning: We will be creating a project which can classify hand-written digits from the MNIST dataset on Google Cloud App Engine: This section deals with various aspects of project development lifecycle.
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.