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What Serverless Computing Is and Should Become

Communications of the ACM

Let us examine an illustrative example from big data processing. Consider a simple query that might arise in an ecommerce setting: computing an average over 10 billion records using weights derived from one million categories. This workload has the potential for a lot of parallelism, so it benefits from the serverless illusion of infinite resources. We present two application-specific serverless offerings that cater to this example and illustrate how the category affords multiple approaches. One could use the AWS Athena big data query engine, a tool programmed using SQL (Structured Query Language), to execute queries against data in object storage.


Tighten The Cloud Security With AIOps & It's Features

#artificialintelligence

AIOps, also known as artificial intelligence for IT operations has many use cases in cloud environments like threat intelligence analysis, malware detection, and has the potential to give sound advice on implementation considerations. According to Gartner's research, the implementation of AIOps in enterprises is expected to reach 30% by 2023. With industries adopting the cloud without hesitation, now is the time for businesses to learn about this advanced monitoring technology to optimize their cloud security operations. AIOps brings many advantages to the table. It implements large-scale data monitoring and analysis.


HPE Steps Up AI March With Standalone Version Of Ezmeral

#artificialintelligence

Hewlett Packard Enterprise Wednesday dramatically expanded its artificial intelligence-machine learning (AI/ML) market reach with a standalone release of its Ezmeral Data Fabric. The new standalone Ezmeral edge-to-cloud data fabric brings the fast growing cloud native AI/ML platform to a new multibillion-dollar market where the data fabric offering can be used in multiple enterprise big data buildouts on its own. HPE made the decision to establish a separate standalone version of the data fabric in direct response to customers, said HPE Chief Technology Officer and Head of Software Kumar Sreekanti (pictured above). "It's a huge market opportunity," he said. "Customers have asked for this because it is a very proven platform with phenomenal scale. Many customers want to first deploy the data platform and later on bring in the Ezmeral container platform."


What is Cloud Computing? The Key to Putting Models into Production

#artificialintelligence

A key skill for any Data Scientist is the ability to write production-quality code to create models and deploy them into cloud environments. Typically, working with cloud computing and data architectures falls in the Data Engineer job title. However, every data professional is expected to be a generalist who can adapt and scale their projects. Here is an introduction to popular platforms that I have seen across dozens of job descriptions. This doesn't mean that we have to become experts overnight, but it helps to understand the services that are out there.


What is Cloud Computing? The Key to Putting Models into Production

#artificialintelligence

A key skill for any Data Scientist is the ability to write production-quality code to create models and deploy them into cloud environments. Typically, working with cloud computing and data architectures falls in the Data Engineer job title. However, every data professional is expected to be a generalist who can adapt and scale their projects. Here is an introduction to popular platforms that I have seen across dozens of job descriptions. This doesn't mean that we have to become experts overnight, but it helps to understand the services that are out there.


Google Cloud AI tools

#artificialintelligence

Emerging tools from Google Cloud Platform are very approachable, even for people without AI experience. Let's hone in on one area of AI usage--ML Ops--and then look at some of the new tools that are reducing the barriers to entry. Often, organizations that want to leverage AI don't consider a plan for production at the outset. When there are only a handful of models running in production, a data science team can likely manage the data engineering and modeling processes manually. But as the number of models grows, you need a more structured approach.


Mitigating the hidden risks of digital transformation

#artificialintelligence

Companies are looking to grab any technology-driven advantage they can as they adapt to new ways of working, managing employees, and serving customers. They are making bigger moves toward the cloud, e-commerce, digital supply chains, artificial intelligence (AI) and machine learning (ML), data analytics, and other areas that can deliver efficiency and innovation. At the same time, enterprises are trying to manage risk -- and the same digital initiatives that create new opportunities can also lead to risks such as security breaches, regulatory compliance failures, and other setbacks. The result is an ongoing conflict between the need to innovate and the need to mitigate risk. "There is always going to be some amount of tension relating to managing risk and engaging in digital transformation work," says Ryan Smith, CIO at healthcare provider Intermountain Healthcare.


BBVA taps Google Cloud Chronicle to build AI security platform

#artificialintelligence

In conversation with Álvaro Garrido, chief security officer at BBVA, Finextra learns that the bank will collaborate with Google Cloud to adopt more advanced technology by placing it in a more cost-effective environment, with greater scalability. Garrido explains that after three years of investing heavily in security and working towards becoming a data-driven bank, now is a good time for BBVA to fully reap the benefits of advanced analytics. "We are getting to the point where we need to monitor more, detect better and react faster. I think these are the three components of not only BBVA's security agenda, but what allows cooperation in the financial industry," Garrido says. Mentioning Google Cloud's Network Telemetry and the ability to identify access patterns that may pose security or operational risks in real-time across a number of devices, he adds that the bank will be able to prevent threats "across the security chain - from the traditional computer space or in the IoT. It's the number of devices, [as well as] the granularity and the level of depth of what we monitor."


Fundamentals of AIaaS and AIPaas (AI-as-a-Service and AI Platforms-as-a-Service) - DATAVERSITY

#artificialintelligence

There are many factors that have started making businesses restless and eager to dive into the newest intelligent technologies for their Data Management practices. The business operators have sighed with relief knowing that they no longer have to engage dedicated talents for advanced model development or cloud infrastructure planning. The idea of "managed (hosted) Data Management" suddenly became the No. 1 priority of all businesses. From public utility sectors to finance and healthcare, smart solutions flooded all sectors. Closely following, the advanced technology platforms-as-a-service market globally is forecast to reach about $11 billion by 2023 and surpass "$88,500 million by the end of 2025."


FIXME: Enhance Software Reliability with Hybrid Approaches in Cloud

arXiv.org Artificial Intelligence

With the promise of reliability in cloud, more enterprises are migrating to cloud. The process of continuous integration/deployment (CICD) in cloud connects developers who need to deliver value faster and more transparently with site reliability engineers (SREs) who need to manage applications reliably. SREs feed back development issues to developers, and developers commit fixes and trigger CICD to redeploy. The release cycle is more continuous than ever, thus the code to production is faster and more automated. To provide this higher level agility, the cloud platforms become more complex in the face of flexibility with deeper layers of virtualization. However, reliability does not come for free with all these complexities. Software engineers and SREs need to deal with wider information spectrum from virtualized layers. Therefore, providing correlated information with true positive evidences is critical to identify the root cause of issues quickly in order to reduce mean time to recover (MTTR), performance metrics for SREs. Similarity, knowledge, or statistics driven approaches have been effective, but with increasing data volume and types, an individual approach is limited to correlate semantic relations of different data sources. In this paper, we introduce FIXME to enhance software reliability with hybrid diagnosis approaches for enterprises. Our evaluation results show using hybrid diagnosis approach is about 17% better in precision. The results are helpful for both practitioners and researchers to develop hybrid diagnosis in the highly dynamic cloud environment.