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Hybrid cloud and low-code approaches gained favor during Covid-19 crisis


In the latest phase of the Covid-19 crisis, technologies that take more of the burdens off already over-burdened IT departments, and make technology more of an ambient part of corporate life, are in. Low-code/no-code adoption is seeing an upswing in technology investment planning, as is robotic process automation. These are among the findings of a new survey of 900 tech executives from KPMG and HFS Research, which measured changes in technology investment planning between March and June of this year. Everyone held back and paused IT investment planning when the Covid-19 crisis first raged through the world in March. But, as business and tech leaders regained their footing, they recognized that investment in certain technologies -- those that provide a clear and sharp return -- needed to move to the forefront.

Top ten myths of technology modernization in insurance


Modernization of technology can make a significant impact across many parts of the insurance industry, including underwriting, policy administration, and claims. McKinsey research shows that the potential benefits of modernization include a 40 percent reduction in IT cost, a 40 percent increase in operations productivity, more accurate claims handling, and, in some cases, increased gross written premiums and reduced churn. 1 1. Technology modernization is vital, but--given the significant value at stake and the size of the investment--it should be approached with a healthy dose of caution. Indeed, many insurers miss out on the full benefits of the program for several reasons. First, they don't have a clear view of what sort of actions are needed or the impact such actions could have, which may lead them to undersell both the business value at stake and what is needed to capture it. This approach can enhance the customer experience somewhat, but it doesn't address core challenges such as the ability to reconfigure products quickly or scale users rapidly. is all that is needed, only to find that some capabilities (such as rapid product configurations) require modernization of core systems.

Atlassian continuously profiles services in production with Amazon CodeGuru Profiler


This is a guest post by the Jira Cloud Performance Team at Atlassian. In their own words, Atlassian's mission is to unleash the potential in every team. Our products help teams organize, discuss, and complete their work. And what teams do can change the world. We have helped NASA teams design the Mars Rover, Cochlear teams develop hearing implants and hundreds of thousands of other teams do amazing things.

Using the power of machine learning to detect cyber attacks - Fintech News


As the world becomes increasingly digital, we are unlocking more value and growth than ever before. However, a challenge that governments, enterprises and well as individuals leveraging technology are constantly facing is the growing threat of cyberattacks that looms large over us. Cyber security solutions provider SonicWall's 2019 report revealed 10.52 billion malware attacks in 2018, a 217% increase in IoT attacks and 391,689 new variants of attack that were identified. What's more is that cyber criminals today are evolving with technology and upping their game. Such incidents don't just have the potential to bring businesses to a standstill but can also inflict serious damages to their resources and repute.

Review the top sessions from recent cloud conferences


If there's a silver lining to social distancing, it's the fact that it gives us a chance to catch up on content we otherwise might have missed. There are always too many sessions to attend at cloud conferences -- from service introductions and updates to best practices and use cases -- that could change the way you use cloud technologies. The global health crisis has made it unlikely any of us will gather for a conference in 2020. Given the dangers of COVID-19, it seems unwise for thousands of professionals from around the world to gather in a crowded convention center. While the in-person conference experience is off the table for the near future, there are plenty of resources still available to review from cloud conferences over the past year.

Writing simple unary RPC for machine learning applications with gRPC in Python


Writing a scalable application needs a better design principle. Let's say we want to design a e-Commerce application, it may have a different set of clients (browser, mobile device, etc.), also in the middle of the development stage a deep-learning based recommendation system which works on images to extract product information needs to be written to improve the user experience, but the main application was written in PHP. To make the design as modular as possible, it's better to choose a microservices based design strategy instead of writing the complete application as one cohesive unit, sharing the same memory space (monolith). If we want to extract car models from image data, we can solve the task with two steps, first, we localize the cars with semantic segmentation, finally, for each segmented car, we apply a classification model to get the car model. A simple scenario with microservices architecture would be, team 1 is working on the segmentation model which they developed and deployed with microservice 1.

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What is driving the machine identity crisis? - Help Net Security


Every machine needs a unique identity in order to authenticate itself and communicate securely with other machines. This requirement is radically changing the definition of machines--from traditional physical devices, like laptops and servers, to virtual machines, containers, microservices, IoT devices and AI algorithms. According to Kevin Bocek, vice president at Venafi, all of these device types have been critical to innovation and digital transformation--yet little is done to safeguard their identities. "While the number of machines in the cloud, hybrid infrastructure and enterprise networks is exploding, most organizations are still attempting to protect machine identities using human methods like spreadsheets," said Bocek. "However, this approach creates its own set of problems--businesses can't keep up with the changes in volume and are being exposed to unacceptable risks."

A Scalable Method for Scheduling Distributed Energy Resources using Parallelized Population-based Metaheuristics Artificial Intelligence

Recent years have seen an increasing integration of distributed renewable energy resources into existing electric power grids. Due to the uncertain nature of renewable energy resources, network operators are faced with new challenges in balancing load and generation. In order to meet the new requirements, intelligent distributed energy resource plants can be used. However, the calculation of an adequate schedule for the unit commitment of such distributed energy resources is a complex optimization problem which is typically too complex for standard optimization algorithms if large numbers of distributed energy resources are considered. For solving such complex optimization tasks, population-based metaheuristics -- as, e.g., evolutionary algorithms -- represent powerful alternatives. Admittedly, evolutionary algorithms do require lots of computational power for solving such problems in a timely manner. One promising solution for this performance problem is the parallelization of the usually time-consuming evaluation of alternative solutions. In the present paper, a new generic and highly scalable parallel method for unit commitment of distributed energy resources using metaheuristic algorithms is presented. It is based on microservices, container virtualization and the publish/subscribe messaging paradigm for scheduling distributed energy resources. Scalability and applicability of the proposed solution are evaluated by performing parallelized optimizations in a big data environment for three distinct distributed energy resource scheduling scenarios. Thereby, unlike all other optimization methods in the literature, the new method provides cluster or cloud parallelizability and is able to deal with a comparably large number of distributed energy resources. The application of the new proposed method results in very good performance for scaling up optimization speed.

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