Goto: Amazon DynamoDB: Building NoSQL Database-Driven ApplicationsThis course introduces you to NoSQL databases and the challenges they solve. Expert instructors will dive deep into Amazon DynamoDB topics such as recovery, SDKs, partition keys, security and encryption, global tables, stateless applications, streams, and best practices. DynamoDB is a key-value and document database that delivers single-digit millisecond performance at any scale. It's a fully managed, multiregion, multimaster database with built-in security, backup and restore, and in-memory caching for internet-scale applications. DynamoDB can handle more than 10 trillion requests per day and support peaks of more than 20 million requests per second.
Do you want to get started with data science but lack the appropriate infrastructure or are you already a professional but still have knowledge gaps in deep learning? Then you have two options: 1. Rent a virtual machine from a cloud provider like Amazon, Microsoft Azure, Google Cloud or similar. To build our system, we need to consider several points in advance. One of the key points is the choice of the right OS. We have the option to choose between Windows 10 Pro, Linux and Mac OS X.
Site Reliability Engineers (SREs) play a key role in issue identification and resolution. After an issue is reported, SREs come together in a virtual room (collaboration platform) to triage the issue. While doing so, they leave behind a wealth of information which can be used later for triaging similar issues. However, usability of the conversations offer challenges due to them being i) noisy and ii) unlabelled. This paper presents a novel approach for issue artefact extraction from the noisy conversations with minimal labelled data. We propose a combination of unsupervised and supervised model with minimum human intervention that leverages domain knowledge to predict artefacts for a small amount of conversation data and use that for fine-tuning an already pretrained language model for artefact prediction on a large amount of conversation data. Experimental results on our dataset show that the proposed ensemble of unsupervised and supervised model is better than using either one of them individually.
Serverless computing is a new type of cloud-based computation infrastructure initially developed for web microservices and IoT applications. As it frees model developers from concerns regarding capacity planning, configuration, management, maintenance, operating and scaling of containers, VMs and physical servers, serverless computing has gained popularity with machine learning (ML) researchers in recent years. Moreover, the benefits of serverless computing have also piqued interest in adopting it to data-intensive workloads such as ETL (extract, transform, load), query processing and ML, where it can provide significant cost reductions. Riding this trend, a research team from ETH Zürich and Microsoft recently conducted a systematic, comparative study of distributed ML training over serverless infrastructures (FaaS) and "serverful" infrastructures (IaaS), aiming to identify and understand the system tradeoffs involved in distributed ML training with serverless infrastructures. Serverless computing is offered by major cloud service providers such as AWS Lambda, Azure Functions and Google Cloud Functions.
The term Artificial Intelligence (AI) was used for the first time by John McCarthy during a workshop in 1956 at Dartmouth College. The first AI application programs for playing checker and chess were developed in 1951. After the '50s, AI was on the rise and fall until the 2010s. Over the years, there have been some investments in AI by vendors, universities, institutions. Sometimes, hopes were high and sometimes hopes were low.
The saturation of mobile devices and ubiquitous connectivity has steeped the world in an array of wireless connectivity, from the growing terrestrial and non-terrestrial cellular infrastructure and supporting fiber and wireless backhaul networks to the massive IoT ecosystem with newly developed protocols and SoCs to support the billions of sensor nodes intended to send data to the cloud. By 2025, the global datasphere is expected to approach 175 zettabytes per year. What's more, the number of connected devices is anticipated to reach 50 billion by 2030. However, the traditional distributed sensing scheme with the cloud-based centralized processing of data has severe limitations in security, power management, and latency -- the end-to-end (E2E) latencies for ultra-reliable low-latency communications found in 5G standards are on the order of tens of milliseconds. This has led to a demand to drive data processing to the edge, disaggregating computational (and storage) resources to reduce the massive overhead that comes with involving the entire signal chain in uplink and downlink transmissions. New advances in machine learning (ML) and deep neural networks (DNNs) with artificial intelligence promise to provide this insight at the edge, but these solutions come with a huge computational burden that cannot be satisfied with conventional software and embedded processor approaches.
Click here to learn more about Gilad David Maayan. If you're running demanding machine learning and deep learning models on your laptop or on GPU-equipped machines owned by your organization, there is a new and compelling alternative. All major cloud providers offer cloud GPUs – compute instances with powerful hardware acceleration, which you can rent per hour, letting you run deep learning workloads on the cloud. Let's review the concept of cloud GPUs and the offerings by the big three cloud providers – Amazon, Azure, and Google Cloud. A cloud graphics processing unit (GPU) provides hardware acceleration for an application, without requiring that a GPU is deployed on the user's local device.
There has been a rise in the use of Machine Learning as a Service (MLaaS) Vision APIs as they offer multiple services including pre-built models and algorithms, which otherwise take a huge amount of resources if built from scratch. As these APIs get deployed for high-stakes applications, it's very important that they are robust to different manipulations. Recent works have only focused on typical adversarial attacks when evaluating the robustness of vision APIs. We propose two new aspects of adversarial image generation methods and evaluate them on the robustness of Google Cloud Vision API's optical character recognition service and object detection APIs deployed in real-world settings such as sightengine.com, picpurify.com, Google Cloud Vision API, and Microsoft Azure's Computer Vision API. Specifically, we go beyond the conventional small-noise adversarial attacks and introduce secret embedding and transparent adversarial examples as a simpler way to evaluate robustness. These methods are so straightforward that even non-specialists can craft such attacks. As a result, they pose a serious threat where APIs are used for high-stakes applications. Our transparent adversarial examples successfully evade state-of-the art object detections APIs such as Azure Cloud Vision (attack success rate 52%) and Google Cloud Vision (attack success rate 36%). 90% of the images have a secret embedded text that successfully fools the vision of time-limited humans but is detected by Google Cloud Vision API's optical character recognition. Complementing to current research, our results provide simple but unconventional methods on robustness evaluation.
At the Google I/O event, the company has announced a revamped cloud-based machine learning platform branded as Vertex AI. It's unusual for Google to announce cloud-related services at Google I/O, a forum for launching consumer and developer technologies. Since Cloud Next - the flagship cloud user conference - is postponed to October, Vertex AI found its place in I/O announcements. This is the third iteration of the Google Cloud ML platform since its original launch. What prompted Google to launch Vertex AI? Let's find out.
Deep Neural Network (DNN), one of the most powerful machine learning algorithms, is increasingly leveraged to overcome the bottleneck of effectively exploring and analyzing massive data to boost advanced scientific development. It is not a surprise that cloud computing providers offer the cloud-based DNN as an out-of-the-box service. Though there are some benefits from the cloud-based DNN, the interaction mechanism among two or multiple entities in the cloud inevitably induces new privacy risks. This survey presents the most recent findings of privacy attacks and defenses appeared in cloud-based neural network services. We systematically and thoroughly review privacy attacks and defenses in the pipeline of cloud-based DNN service, i.e., data manipulation, training, and prediction. In particular, a new theory, called cloud-based ML privacy game, is extracted from the recently published literature to provide a deep understanding of state-of-the-art research. Finally, the challenges and future work are presented to help researchers to continue to push forward the competitions between privacy attackers and defenders.