Introduction

In today’s distributed computing environment, systems often need to communicate across networks efficiently. Remote Procedure Call (RPC) frameworks provide a mechanism for one system to execute functions or methods on another, abstracting the complexities of network communication, serialization, and data exchange. This guide explores RPC frameworks, their benefits, and the most popular options available.

What is an RPC Framework?

An RPC framework allows applications to invoke functions on remote systems as if they were local, enabling seamless communication between different services or microservices. These frameworks handle:

• Network communication (request and response handling)
• Serialization (data encoding/decoding)
• Error handling (timeouts, retries, exceptions)
• Security (authentication, encryption, and authorization)

Benefits of Using RPC Frameworks

• Simplifies communication between distributed systems
• Enhances performance with efficient serialization and transport mechanisms
• Supports multiple languages, making it ideal for heterogeneous environments
• Reduces development complexity by abstracting network operations

Popular RPC Frameworks

1. gRPC (Google RPC)

• Developed by Google, based on Protocol Buffers (protobufs)
• Uses HTTP/2, enabling efficient communication
• Supports bi-directional streaming and authentication
• Ideal for microservices and high-performance applications

2. Apache Thrift

• Created by Facebook to enable cross-language service communication
• Supports multiple serialization formats and transport protocols
• Uses an IDL (Interface Definition Language) for defining services
• Good for polyglot environments where multiple languages interact

3. JSON-RPC

• A lightweight RPC protocol using JSON for message exchange
• Enables batch processing (multiple requests in a single call)
• Commonly used in web applications and blockchain (Ethereum API)

4. XML-RPC

• Uses XML for message encoding
• Simpler than SOAP but less efficient than gRPC or JSON-RPC
• Often used in legacy systems and CMS applications (e.g., WordPress)

5. D-Bus

• Used for Inter-Process Communication (IPC) in Linux-based environments
• Enables communication between system components like GNOME and KDE

6. RPyC (Remote Python Call)

• A Python-specific RPC framework
• Allows remote object sharing and method execution
• Useful for Python-based distributed applications

7. SOAP (Simple Object Access Protocol)

• XML-based enterprise-level RPC framework
• Provides built-in security and standardization
• Commonly used in banking, finance, and legacy enterprise systems

Comparing RPC Frameworks

Choosing the Right RPC Framework

• If you need high-performance microservices, use gRPC.
• For multi-language service communication, consider Apache Thrift.
• If your app requires lightweight JSON-based RPC, go with JSON-RPC.
• D-Bus is best for Linux IPC.
• If you are developing enterprise solutions, SOAP is a reliable choice.

Conclusion

RPC frameworks are essential for modern distributed applications, enabling efficient and scalable communication. Choosing the right framework depends on your use case, language support, and performance needs. Whether you’re working on microservices, web applications, or enterprise systems, there’s an RPC framework suited for your requirements.

The post Understanding RPC Frameworks: A Guide to Remote Procedure Calls appeared first on Artificial Intelligence.

To reduce and track the spread of COVID-19, researchers and provider organizations have increasingly turned to artificial intelligence and machine learning tools to improve their surveillance efforts.

For more coronavirus updates, visit our resource page, updated twice daily by Xtelligent Healthcare Media.

From predicting patient outcomes to anticipating future hotspots across the country, big data analytics systems have helped health leaders stay ahead of the pandemic, resulting in more efficient care delivery.

However, healthcare organizations’ level of pandemic preparation is only as good as the data available to them. While the industry is no stranger to data issues, the COVID-19 pandemic has brought a host of unique challenges to the forefront of care delivery.

The novel, global nature of the virus has led to significant gaps in COVID-19 data, with inconsistencies in information leaving officials unsure of the effectiveness of public health interventions.

“It’s now well-known that asymptomatic infections are a common phenomenon in the spread of coronavirus. And it’s very important to understand that phenomenon because, depending on how many asymptomatic infections there are, public health interventions might be different,” Lucy Li, PhD, data scientist at the Chan Zuckerberg Biohub, told HealthITAnalytics.

Researchers at the Chan Zuckerberg Biohub are working to overcome this challenge. Using machine learning and cloud computing technology, Li estimated the number of undetected infections at 12 locations in Asia, Europe, and the US over the course of the pandemic.

The results showed that a wide range of infections were undetected in these locations, with the rate of undetected infections as high as over 90 percent in Shanghai.

Additionally, when the virus was first transmitted to these 12 locations, over 98 percent of infections were undetected during the first few weeks of the outbreak. This suggests that the pandemic was already well underway by the time intense testing began to occur.

These findings have important implications for public health policy and provider organizations, Li noted.

“For disease outbreaks where you can detect every single infection, rapid testing and just a small amount of contact tracing is enough to get the epidemic under control. But for coronavirus, because there are so many asymptomatic infections out there, testing alone won’t help control the pandemic,” she said.

“Because usually when you do testing, you’re testing symptomatic patients. But that’s only a subset of the total number of infections out there. You’re really missing a lot of people who are able to spread the infection, but are not quarantining. Being able to get a sense of what that number might be is helpful for allocating resources.”

Li’s research was supported by the AWS Diagnostic Development Initiative, a global effort to accelerate diagnostic research and innovation during the COVID-19 pandemic and to help mitigate future disease outbreaks.

The initiative allows individuals to take advantage of the cloud and other innovative tools, something that Li said was essential for her research.

“The data I’m using are the viral genomes – the viral DNA. As the viral genomes spread through the population, they accumulate mutations. Generally, these mutations are not good or bad, they’re just changes in the genome. Every time the virus is spread to a new person, it could accumulate new mutations. So, if we know how quickly the virus mutates, we can infer how many missing transmission links there were in between the observed genomes,” she said.

“That’s the data I’m fitting the models to. And because there are many different scenarios that could explain what we see in the viral genomes, I have to leverage machine learning and cloud computing to test all of those hypotheses and to see which one can explain the observed changes in the viral genomes.”

These data analytics tools are well-suited to meeting the challenges brought on by COVID-19, Li pointed out.

“In order to try to quantify the unreported infections, we formulate models of how disease spreads in the population. And then we generate many simulations from these models, and we find out which of those simulations fits the data that we see,” she said.

“That allows us to test different levels of under-reporting and understand which of those can best explain the data that we see. That’s not really possible without a lot of computational resources, and it’s a very time-intensive process. The machine learning tool allows us to explore different explanations of the data that we’re seeing, and we can test many hypotheses. It’s a crucial tool for this type of analysis.”

With machine learning and cloud computing technologies, Li was able to streamline a previously time-consuming task.

“Before cloud computing became more common and these big computational resources became available, some of these analyses could take months to run. I’ve seen papers that were based on months of running a very complex model,” Li said.

“But by having access to more computational resources in the cloud, we can shorten that time from months to days, because we’re able to leverage much more memory and better parallelize our analysis.”

The research could help public health officials monitor the rate of under-reporting in real-time, which could indicate how well current surveillance systems are operating.

“The better the current public health surveillance system is at detecting infections, the fewer underreported cases we would have. But if we see the underreported cases increasing, that would suggest that there needs to be more testing in the population. The results of this research can help the public health department determine how much more testing they would need,” Li said.

“This type of research can also help indicate how close we are to the end of the pandemic. By tracking how many people in the population have been infected by the virus or the number of undetected cases, we could get a sense of how far are we from eliminating this disease.”

With the amount of information generated by the COVID-19 pandemic, analytics tools are critical for uncovering new insights and potential solutions.

“Since the start of the pandemic, we’ve racked our brains to figure out what we can do to help the public health departments in reducing the spread of COVID-19. The number one request that we get from public health departments is information. And sometimes, just presenting the raw data to these departments is sufficient by itself,” Li concluded.

“But quite often, we need to use machine learning and mathematical models to infer these parameters or numbers that we can’t directly see in the data. There has been so much effort from different research groups around the world in developing new models to help us tease out the underlying information that’s not obvious from the data alone.”

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Microsoft CEO Satya Nadella announced the preview of Azure Orbital at Microsoft Ignite 2020 in New Orleans. According to Microsoft, Orbital is ‘Ground Station as a Service (GSaaS)’, which is aimed at helping its customers to communicate with, and analyse data from, their satellites or spacecrafts on a subscription basis.

The Redmond headquartered company, however, has competition in the skies. Almost five months earlier, International Business Machines Corp. (IBM) had announced a beta of its Cloud Satellite service. But it is Amazon Web Services Inc. (AWS), the cloud computing arm of Amazon.com, which has a head start in space.

Around two years ago, it launched the AWS Ground Station to allow its customers to control their satellite communications, process data, and scale operations without having to build or manage their own ground station infrastructure. On 30 June, AWS said it was establishing a new space unit called the Aerospace and Satellite Solutions.

These are but a few cases in point to demonstrate that leading cloud computing service providers have begun flexing their muscles in space too. But why is there a sudden race to outer space?

According to the International Telecommunication Union (ITU), non-geostationary satellite orbits (NGSOs) such as medium earth orbits (MEO) and low earth orbits (LEO) are being increasingly used worldwide. NGSOs, unlike fixed or geostationary satellite orbits, move across the sky during their orbit around the earth. With space launches becoming more affordable and accessible, a slew of private companies are starting to rely on this new array of satellites.

They are used for applications like weather forecasting, surface imaging, communications, and video broadcasts. However, the data from these satellites need to be processed and analysed in data centres on the ground, which explains the term ‘ground stations’.

While the cost of the satellite itself is falling, building and running ground stations can cost up to $1 million or more, according to a recent blog post by Jeff Barr, chief evangelist for AWS. Complex data processing also requires a lot of computing power, and the huge data storage requirements only add to the cost.

Leading cloud computing service providers are now starting to offer satellite operators the option to use these ground stations on a ‘pay-per-use’ or subscription basis, thus, helping the latter save on capital expenditure costs by employing an operating expenditure model.

These ground stations, thus, can help satellite operators download high-resolution imagery faster, more regularly, and analyse the data with artificial intelligence (AI) tools—all of which results in faster and enhanced monitoring of changing climate patterns, forests and agriculture, among other things.

While Microsoft and IBM are testing their services, AWS Ground Station already has customers such as NASA’s Jet Propulsion Laboratory and satellite operators Iridium Communications and Spire Global. It also has private sector customers such as Lockheed Martin, Maxar Technologies and Capella Space.

Lucrative market

The worldwide cloud infrastructure services market continued to surge in the April-June quarter of this calendar year to touch $34.6 billion, according to research firm Canalys. The growth was attributed to the consumption of cloud-based services for online collaboration and remote working tools, e-commerce, remote learning, and content streaming which hit new records during the lockdown.

During this period, AWS was the leading cloud service provider, accounting for 31% share of the total spend. Microsoft Azure came second, followed by Google Cloud and Alibaba Cloud.

The revenue of the cloud unit of Amazon totalled $10.81 billion in the April-June quarter of this calendar year, accounting for 12% of its parent’s revenue.

Microsoft, on the other hand, said its “commercial cloud surpassed $50 billion in annual revenue for the first time” for the quarter ended June 30 (which is also its financial year ending). But it does not spell out what this ‘commercial cloud’ consists of.

Nevertheless, the space forays will only add to the revenue of all these companies.

Battle lines in India

Space deals will add spice in India too. India’s cloud computing market was estimated at $2.5 billion in 2018, dominated by infrastructure as a service (IaaS) and software as a service (SaaS), according to industry body Nasscom. It is forecast to touch over $7 Billion in 2022.

AWS, Microsoft and Google are leaders on the local turf too. Last August, for instance, Microsoft signed a deal with Reliance Jio Infocomm Limited (Jio)—a subsidiary of Mukesh Ambani-owned Reliance Industries Ltd (RIL). The agreement included deploying the Microsoft Azure cloud platform in Jio’s data centers in locations across India.

This January, Google Cloud signed a deal with Bharti Airtel to cater to small and medium enterprises (SMEs) in India. However, Google said this July that it was pumping in $4.5 billion into Airtel’s rival Jio Platforms in exchange for a 7.7% stake. Not surprisingly, a month later, Bharti Airtel announced a multi-year agreement with AWS to deliver cloud solutions to big companies and SMEs in India.

According to Alok Shende, Managing Director of Ascentius Insights, the “fusion of cloud computing with networking, linked by a satellite, is expected to shave off milliseconds in transferring data from source to destination. This is the holy grail in many applications, more specifically in finance and in mission-critical applications. There are many India-centric applications (like defence and in the stock markets) where this could play a powerful role.”

He believes that “for Microsoft, particularly, this move opens a new avenue to entrench itself in the enterprise market where it has traditionally been a strong player on the application side but has lost the leadership position in terms of market share for cloud.”

Jayanth Kolla, founder and partner of Convergence Catalyst points out that India has always been a strong player in the space sector with the Indian Space Research Organization (Isro) developing and launching satellites at a fraction of global costs. He believes that the Indian government’s decision to open up India’s space sector to private players is an encouraging sign.

“It has already resulted in Indian space tech startups such as Pixxel, Bellatrix Aerospace, Vesta Space and Agnikul raising over $20 million funding from venture capitalists (VCs) in the last six months. TV media, agriculture, telemedicine and logistics are a few sectors that can benefit from strong satellite communication and space technology development. The ground station services launch by Microsoft and AWS will only expedite this ecosystem development significantly in India,” says Kolla.

Sanchit Vir Gogia, chief analyst and founder of Greyhound Research, concurs that the timing of this space move is right since many organizations are now beginning to try new use-cases by tapping into geospatial data (data related to a specific location on earth) that is omnipresent, given the proliferation of devices and edge computing devices.

“This space is increasingly getting busy with the likes of AWS and IBM investing money and resources to cater to this opportunity,” notes Gogia. He cautions, however: “We believe the trick in making such an offering successful is to ensure that it is cheap to start with, since most of these projects are nothing more than trials and, hence, have an extremely high failure rate.”

The distributed cloud

Space is just an additional frontier for the leading cloud services providers. It all began when companies, which traditionally used servers for their computing needs, realised that they could lower costs by accessing IT resources over the internet, and paying only for the services they needed, reducing capex—a trend we now know as cloud computing.

Many companies today use private clouds (on-premise), public clouds (on a network, typically the internet) and hybrid clouds (combining public and private). User companies, though, became wise and began adopting a ‘multi-cloud’ vendor approach to avoid being locked in by any single technology or cloud vendor.

With billions of devices getting connected to each other as part of the Internet of Things (IoT) trend, computing is now also getting done at the so-called “edge”, which simply means near the source of the data.

General Electric Co. (GE), for instance, believes cloud computing is best suited to situations that demand actions such as significant computing power, management of huge data volumes from across plants, asset health monitoring and machine learning. Edge computing, on the other hand, makes sense in places like mines or offshore oil platforms that have bandwidth constraints, which make it impractical or very expensive to transmit data from machines to the cloud.

During his speech at the Ignite event, for instance, Nadella pointed out that Microsoft was “extending Azure from under the sea to outer space”. He was referring to Project Natick that aims to serve customers in areas near large bodies of water. Natick uses AI to monitor signs of failure in its servers and other equipment.

Going forward, Microsoft says it will explore powering a Natick data center by “a co-located ocean-based green power system, such as offshore wind or tide, with no grid connection”.

Similarly, other than deploying internet balloons in space to provide broadband services, Google also provides services to companies like Planet Labs Inc. The US-based aerospace and data analytics company uses Google Cloud platform to process all of its satellite images and Google Cloud storage to host its image archive.

These moves have given rise to a trend called ‘Distributed Cloud’, which research firm Gartner describes as “distribution of public cloud services to different physical locations”.

By 2023, posits a 22 January note by Gartner, “the leading cloud service providers will have a distributed ATM-like presence to serve a subset of their services for low-latency application requirements… ‘Micro data centers’ will be located in areas where a high population of users congregates, while pop-up cloud service points will support temporary requirements like sporting events and concerts.”

Greyhound Research believes offerings such as ground stations will be highly valuable in the next wave of investments in more distributed computing environments. “More than 7 in 10 of our end-user inquiries with global majors have confirmed that organizations, in the next 3-5 years, will use a large variety of computing environments and make them more contextual to the use-case,” says Gogia. “This change is likely to be paced multiple times, given the investments in edge networks and 5G that allow remote sites in utilities, oil and gas, manufacturing, and many other scenarios,” he adds.

The distributed cloud market is forecast to reach $3.9 billion by 2025, growing at a CAGR of 24.1% during the forecast period from 2020-2025, according to market research firm, IndustryARC. Security, though, remains a concern if proper protocols and policies are not adhered to in a distributed cloud.

For now, though, ground stations that cater to satellite companies will remain one big component of the distribution cloud. A race is clearly on and all the main players are looking up at the sky.

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In this world where there is competition everywhere for example in terms of education, sports, jobs, etc. and people always try to show their best to achieve the point of success by defeating the others. It can be thought of as a war that is going around the world with a different name just to suppress the actual meaning i.e. competition. This war will never end until the earth doesn’t get demolished. So, the gist is we need to stay ahead of everybody in the domain we are interested in. To talk in the context of this and also referring to the intended topic of the day we will be collating both the terms together and now let’s move forward. Today, the very first competition that arises amongst the people is how tech-savvy they are that is how much they have the technical knowledge and expertise to solve any problem. Everyone is trying to engineer new things or upgrading the obsolete ones.

So, to help aid in these upgrades we have many technologies around. These technologies are somewhere or the other related to computer-related applications and here we will be discussing the top 5 technologies that a person needs to learn to stay ahead in 2020. Let’s begin!

1. Artificial Intelligence: The very first and topmost technology that one should know of. The technology rotates about the topic of automating the machinery and creating humanoids that can help aid people to solve any kind of task. The concept was started in the 1960s and since then have evolved so much that people are shifting their career towards this field. The field consists of various sub-components that one can master to achieve the desired success. The components include Machine learning, Deep learning, and subsets to Deep learning. With this technology in hand, one can land in his/her dream job and can also get high pay because of the demand for this technology in every employable sector of the world.
2. Internet of Things (IoT): Yet another technology that is closely linked with AI. The idea behind this technology is connecting various tech devices with a single parent device to monitor everything that is taking place within the device and fix underlying issues if any. Also, the idea is to gather and share different kinds of data so that there is no discrepancy in the same. All gadgets that we use are somewhere or the other linked with IoT starting from the mobile device, washing machines, etc. IoT can even help inn minimizing electricity by developing a cleaner and greener city. With this technology in hand, one can come up with flying colors and get a highly paid job.
3. Augmented Reality and Virtual Reality: These are the most used technologies by modern-day people. Many researchers and scientists prefer to work in Augmented reality to practice their work and then work in the real world. Many AI-based environments especially in reinforcement learning use these technologies to test the humanoid in a virtual environment first and then use it in the real world. Today AR and VR are also been implemented in mobile phones and tablets to make things more real for the people. Learning these technologies may aid you in getting a good job in the market out there.
4. Blockchain Technology: The world’s more trending and safest mode of transaction wherein people use virtual coins to pay their bills. This technology gave rise to the Bitcoin and Etherium we see today. There are many such cryptocurrencies out there regarding which a person can take the knowledge and also learn the behind the scenes of how this Blockchain works. This technology is gaining immense popularity since its creation and many people are investing their money on it. So, I would prefer to learn about this and get a good job in the top tier companies.
5. Cognitive Cloud Computing: The cloud as we see today was not the same in the past. Here I am talking about the cloud-based services that are provided by companies like Microsoft, Google, Amazon, etc. These services since time have evolved so much that now we can do nearly any type of computational work in the cloud-like hosting websites, storing databases, performing AI-related work, deploying the work, etc. These things were at first not in the reach of normal people but, with the help of the modern-day cloud, we can get access to these things and use it for our benefit. Today many companies are hiring cloud-based engineers rather than hard-coding ones because of the fast implementation with it and are paying huge salaries to them. So, I would personally suggest learning this technology to get a decent job out there and stand ahead of others.

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The coronavirus outbreak has profoundly altered our daily lives. In a matter of weeks, industries across sectors essentially ground to a halt. Prevention and containment strategies pursued by the government witnessed people shifting to remote working and learning, embracing the new normal.

Amidst companies downsizing operations, lay-offs, and an economic crisis, a sense of fear and uncertainty is palpable across the Class of 2021.

As the newest cohort of graduates embark on their journey of entering the workforce, they will have to cope with a market hit by a pandemic and an unstable global economy. Companies are looking to hire people who can hit the ground running. Their success depends on how quickly and effectively they can address a business challenge.

And for that, companies seek candidates who need little hand holding.

Skill-gap isn’t a new phenomenon. It’s been one of the gravest concerns in the Indian education system. The inability of equipping students with future-ready skills can be attributed to the dated curricula, poorly trained faculty, and flawed teaching and learning pedagogy.

While overhauling a curriculum won’t happen overnight, institutes can consider updating their elective courses to bridge the gap. Numerous colleges do offer MOOC-based learning; however, it has not ended up being viable with studies indicating the dropout rate for these courses to be as high as 95 per cent.

The process will enhance their capability, and a hands-on approach and practical experience will give them an added advantage in the job market.

With businesses moving to the Cloud and the ever-increasing dependence on data, demand for professionals with skills in Cloud computing, data analytics, and Artificial Intelligence and Machine Learning has witnessed a significant surge.

ement: Risk management plays a crucial role to address ambiguities during a pandemic. The art of judging a risk even before it arises, and analysing and predicting its consequences has been gaining importance in the past three months. It is one of the most critical skills required in the post-Covid-19 world, evaluating and anticipating consequences and equipping for the impact.

3. Data Analytics: An analytics engineer works on transforming, testing, deploying, and documenting data. It’s one of the most important skills that companies look for in their recruits. The industry is always on the lookout for professionals who can study and determine trends in data and develop algorithms to make raw data more useful to enterprises.

4. Business Analytics: Businesses require professionals to create and actualise all-inclusive tools and strategies that permit raw data to be changed into business knowledge. These bits of knowledge are regularly utilised for making decisions and dynamic planning across companies. Organisations are ready to pay higher than average salaries to business analysts that help them stay ahead of the curve.

5. Machine Learning (ML) and Artificial Intelligence (AI): AI centers around the production of smart machines that mirror human intelligence. Machine Learning is the process of utilising software engineering principles, and analytical and data science knowledge, and blending both to make it accessible for use by the product or customers.

Students with skills to feed data into models defined by data scientists and build models using algorithms which can be used to make business decisions will draw more attention.

6. Sales Effectiveness: In a competitive world, where consumers and businesses have several options to choose from, sales becomes a crucial skill to hone. Connecting with the consumer and providing a differentiated and consistent customer experience gives companies the competitive edge required to secure a sale.

Candidates with knowledge of which factors and behaviors impact sales effectiveness, who can leverage value selling over product selling and win every round of negotiation have the potential to turn a business around.

Future graduates must attain a fine blend of theoretical and practical knowledge of relevant skills that the industry demands.

Apart from the traditional curriculum, elective courses in emerging technologies delivered by industry experts enable students to graduate with significant job-ready skills, greater salaries, robust alumni status, and a lifelong learning culture.

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The Rapid Accessibility of Big Data powered with the advances in Machine Learning is propelling the modern Data Science Revolution

Big Data, termed as the rapid inflow of data from a plethora of sources is proliferating and awe-inspiring. Coupled with the advances in machine learning, artificial intelligence and cloud computing, it allows the enterprise to extract coherent, strategic insights from the data pipelines. Combined as data science, they have the potential to be a richly enhanced source of information which is deeper and more detailed than ever had before, and yet comprehensively more engaging covering a broader spectrum.

Spreading from its origins in technology big data is now making waves in the information- and the research-driven world of technology powered by Artificial Intelligence.

Looking in-depth, data science informs the enterprise rather than replacing the legacy research methodologies. The crux is to create perfect data harmony!

The Logic behind Data Silos and Data Analytics

The importance of information flows between data lakes data warehouse and data silos let the modern enterprise know how much they can be enhanced still further with data science. Here is where the true power of data science lies with tangible implications for the way a modern active management business structures on its research efforts.

For the Modern Enterprise: The Commandments of a Big Data Infused Data Science Revolution-

1. Believe in Big data, and the power it possesses, leverage Data Science for tangible returns.

2. Data science paints a detailed and more comprehensive Data Fabric picture.

3. Data science instils life into data byways of fundamental research and quantitative methodologies.

4. Build the Data Pipelines or ready-made clean data for Citizen Data Scientists, let them build models among with the data science analysts!

5. Communicate with a data story, the more Big Data speaks for itself, the more enterprise can benefit from it.

Data is power its magical, it’s time for enterprises to wake up to the untapped data they possess, the insights gleaned from traditional data sources like written text, spoken words, digital information, pictures and numbers is the true treasure to businesses to make an impact and win in the marketplace.

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Pepperdata, the leader in Analytics Stack Performance (ASP), announced the release of its inaugural “Big Data Performance Report” for 2020. The report was compiled after reviewing comprehensive data on the applications contained in the company’s largest enterprise customer clusters, representing nearly 400 petabytes of data on 5000 nodes. This equates to 4.5 million applications running in a 30-day timeframe. The report provides insights into the enormous compute waste that occurs with big data applications in the cloud.

Pepperdata research shows how IT operations teams are dealing with this challenge. The new “Big Data Performance Report” reveals that, within enterprise data applications that are not optimized by solutions that allow for observability and continuous tuning, there exists enormous waste—and tremendous potential to optimize and reduce that waste.

The shift to cloud computing is solidly underway. As Statista reports, “in 2020, the public cloud services market is expected to reach around $266.4 billion U.S. dollars in size, and by 2022 market revenue is forecast to exceed $350 billion U.S. dollars.” However, as the cloud expands, so does cloud wastage. As more complex big data applications migrate, the likelihood of resource misallocation rises. This is why, as Gartner reports, “through 2024, nearly all legacy applications migrated to public cloud infrastructure as a service (IaaS) will require optimization to become more cost-effective.” Without this optimization, the data highlights there will be overspend.

“When we analyzed the data, we were amazed to see how much underutilization and other wasted resources there were—unnecessarily driving costs up,” said Joel Stewart, VP, Customer Success, Pepperdata. “The failure to optimize means companies are leaving a tremendous amount of money on the table—funds that could be reinvested in the business or drop straight to the bottom line. Unfortunately, many companies just don’t have the visibility they need to recapture the waste and increase utilization.”

The research from Pepperdata sheds further light on the nature of cloud wastage. For instance:

• Spark clusters and jobs are dominating spend across clusters. This is where the highest amount of net wastage was found.
• When it comes to wastage, failures are important. Job failures cause serious performance degradation, and consume significant computational resources. In an unoptimized dataset, Pepperdata sees a wide range of failure rates across clusters. Some clusters will fail above 10%, and Spark applications tend to fail more often than MapReduce.
• Prior to implementing Spark optimization: Across clusters, within a typical week, the median rate of maximum memory utilization is a mere 42.3%. The underutilization here represents two states: not enough jobs running to fully utilize the cluster resources or the jobs are wasting resources.
• Prior to implementing cloud optimization: Comparing jobs used and wasted, the average wastage across 40 large clusters is 60+%. This wastage takes an interesting form; typically, with 95% of jobs,  there is little wastage. Major wastage is usually found in 5% to 10% of total jobs.

This is why optimization is inherently such a needle-in-a-haystack challenge, and why machine learning can be such a help. Studies show that ML-powered statistical models predict task failures with a precision up to 97.4%, and a recall up to 96.2%. Applied to Hadoop, the percentage of failed jobs is reduced by up to 45%, with an overhead of less than five minutes.

Cloud optimization delivers big savings. According to Google, even low effort cloud optimization efforts can net a business as much as 10% savings per service within two weeks. Cloud services that are fully optimized and running on extended periods (over six weeks) can save more than 20%.

The research showed:

• With the visibility afforded by real cloud optimization, three quarters of customer clusters immediately win back task hours.
• Most enterprises are able to increase task hours by a minimum of 14%. Some enterprises are able to increase task hours by as much as 52%.
• 25% of users are able to save a minimum of $400,000 per year. At the higher end, the most successful users are able to save a projected $7.9 million for the year.

To cut the waste out of IT operations processes and achieve true cloud optimization, enterprises need visibility and continuous tuning. This requires machine learning and a unified analytics stack performance platform. Such a setup equips IT operations teams with the cloud tools they need to keep their infrastructure running optimally, while minimizing spend.

The post Pepperdata Releases Inaugural “Big Data Performance Report” 2020 appeared first on Artificial Intelligence.

Artificial intelligence (AI) and machine learning (ML) are powering a whole new generation of business intelligence (BI) solutions. And these mission-critical software packages are in turn one of the primary drivers behind the migration of enterprise big data to the cloud.

BI tools are designed to collect and analyze current and actionable data – delivering insights into processes and workflows that can impact business operations in the near term. But what if you need those insights immediately, and you need them in the hands of employees and experts who are working simultaneously across the globe? IT stakeholders are turning to the cloud for faster, more accurate and timelier BI insights – especially in the face of Covid-19 where companies are looking to operate as economically possible and millions are forced into remote working locations. Even before the pandemic, a 2019 survey by TechTarget found that 27% of respondents plan to deploy BI in the cloud in the coming year.

That same study points to an increase in cloud technology as the number two activity that companies are employing to improve employee experience and productivity, and notes that 38% of companies plan to bolster their cloud technology within the next year.

There are multiple reasons that organizations are moving their BI and analytics to the cloud.

First among them is cost: The move streamlines a workforce, so even though there are start-up costs involved in the migration process, the long-term cost-benefit analysis plays out in their favor. Companies are also able to run faster and lighter with cloud-based BI, with no need to run dedicated client-side applications and IT teams freed of the necessity of coordinating upgrades across an entire infrastructure.

Then there’s security: Companies tap into a whole extra layer of security and protection for their data as there is only one point of access, and data can’t accidentally be merged with another company’s, or worse, intentionally and maliciously accessed by someone who does not have access.

Accessibility will also improve, as companies will be no longer tethered to one distinct physical location to store data. When their BI systems are migrated to the cloud, it offers real-time access to critical data and analyses from any laptops, tablets and smartphones, meaning that access to the information required to make better business decisions is constantly within reach.

Scalability will also jump dramatically, as the cloud offers an elastic infrastructure that provides a simple platform for scaling up as a company grows.

And, performance is enhanced since cloud infrastructure is customizable to each company’s specific needs. An added benefit is centralized collaboration, allowing entire teams to work within the same framework with the same tools, no matter how scattered or far-flung they might be.

TDWI’s recent report on BI and analytics notes that “… demand is rising for systems that can provide views, analytics, and prescriptive recommendations based on data generated by events happening now and predictive insights into what could happen in the future.”

A vivid example of cloud’s analytics advantages is the use of Spark, with its extremely high memory demands. The elasticity of the cloud enables Spark to perform orders of magnitude faster than Hadoop/Hive on-prem. The differences can be dramatic: a 10- to 12-hour Hive query can literally take only 15 minutes with Spark in the cloud.

AI for BI and Analytics

Increasingly, cloud big data vendors and their customers have rich AI-driven BI ecosystems at their disposal, like Snowflake and Tableau (which was acquired by Salesforce). For those using Apache Spark, Databricks provides a unified analytics platform that accelerates innovation by unifying data science, engineering and business with an extensive library of machine learning algorithms, interactive notebooks to build and train models, and cluster management capabilities that enable the provisioning of highly-tuned Spark clusters on-demand.

Businesses of every size are learning that leveraging AI technology can improve business processes and significantly enhance the customer experience. This is happening across several industries — healthcare, finance, and life sciences (despite heavy regulation) are quickly adopting AI-driven business models, and AI is transforming medicine in how and when treatments are discovered and tested.

The Bottom Line

Cloud computing has completely transformed entire industries, computing paradigms and enterprises, and has become the ideal for storing and accessing big data.

The COVID-19 pandemic has only accelerated this move given the need to operate as economically as possible with more employees working remotely. Cloud computing saves both money and time, which makes it immediately attractive to businesses, while also increasing access for global companies, providing a synergic platform for coordination and cooperation between far-flung employees, and it creates an impressive security buffer through a single point of access that ensures companies’ data — its most precious asset and its most critical investment — is protected from malicious actors. AI-powered business intelligence and analytics are driving the migration of enterprise big data to the cloud.

Choosing the right BI platform can dramatically enhance productivity with unprecedented business insights, and a more intimate knowledge of customers and trends.

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Baidu, Inc. recently announced that it will increase its investments in cloud computing, AI education, AI platforms, chipsets, and data centers in the coming ten years as part of its efforts to construct “new infrastructure” for the smart economy of the future.

Under the plan, Baidu aims to have 5 million intelligent cloud servers by 2030 and train 5 million AI professionals within 5 years, which will help facilitate the widespread application of AI in transportation, city management, finance, energy, health care, and manufacturing to eventually achieve industrial intelligence.

“New infrastructure–which encompasses emerging technologies like AI, cloud computing, 5G, IoT, and blockchain–will be the driver for China’s economic development in the coming decades,” said Baidu Chief Technology Officer Haifeng Wang, underscoring the importance of the plan.

As a world-leading AI platform company, Baidu is well positioned to make large contributions to the development of new infrastructure in China, which will support the implementation of AI applications in different industries.”

The investment plan will see Baidu deploy 5 million intelligent cloud servers by 2030, an ambitious target that would create a combined computing capability equal to seven times the total calculable computing power of the world’s existing top 500 supercomputers.

Viewing human capital as a core component of new infrastructure, Baidu also intends to train 5 million AI professionals in the next five years. Baidu has been working with more than 200 leading universities in China to develop courses related to AI and deep learning and has already trained more than 1 million AI experts.

As a developer of both AI infrastructure and AI applications, Baidu is well-positioned to contribute to building new style infrastructure, which is at the core of China’s “New Infrastructure” policy to accelerate economic growth and industrial upgrade.

Baidu has more than 7,000 published AI patent applications in China, the highest in the country. The AI open platform Baidu Brain has made available more than 250 core AI capabilities to over 1.9 million developers, while PaddlePaddle, the largest open-source deep learning platform in China, services 84,000 enterprises. Baidu’s Kunlun and Honghu AI chips are among the highest preforming AI chips and are built for a wide range of scenarios. Baidu Cloud is China’s leader in public cloud and AI cloud services with more than ten data centers across the country.

This new infrastructure is already allowing Baidu to lead the intelligent transformation of different industries. Baidu’s smart finance products serve nearly 200 financial institutions, while Baidu’s intelligent healthcare prouducts are deployed at more than 300 hospitals and 1500 grassroots medical institutions. Baidu Brain for Cities is already in place in Chongqing, Suzhou, and other cities, supporting more intelligent city management. Baidu’s new investments will enhance its ability to rollout AI applications in these scenarios, as well as in manufacturing, energy, and transportation.

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Machine learning-based personalization has gained traction over the years due to volume in the amount of data across sources and the velocity at which consumers and organizations generate new data. Traditional ways of personalization focused on deriving business rules using techniques like segmentation, which often did not address a customer uniquely. Recent progress in specialized hardware (read GPUs and cloud computing) and a burgeoning ML and DL toolkits enable us to develop 1:1 customer personalization which scales.

Recommender systems are beneficial to both service providers and users. They reduce transaction costs of finding and selecting items in an online shopping environment and improves customer experience. Recommendation systems have also proved to improve the decision making process and quality. In an e-commerce setting, for example, recommender systems enhance revenues, for the fact that they are effective means of selling more products. In scientific libraries, recommender systems support users by allowing them to move beyond catalog searches. Therefore, the need to use efficient and accurate recommendation techniques within a system that will provide relevant and dependable recommendations for users cannot be over-emphasized.

At Epsilon we have used machine learning to solve problems of granular product recommendations in a wide range of channels, to drive customer engagements and bottom-line. The usual solutions to predict product recommendations involve creating multiple models for high-level product categories. Such solutions are not scalable, are resource-intensive and don’t make personalized recommendations. In the sections below, we quickly touch upon the methods to build effective recommender systems.

Collaborative Filtering

Collaborative filtering recommends items by identifying other users with similar tastes; it uses their opinion to recommend items to the active user. These recommenders learn from the user-item interactions data from where you can create a N x M sparse matrix that captures all possible user-item interactions. Here N is the number of users and M is the number of items. This data is usually very sparse, meaning very few non-zero elements are there in the matrix. This is also evident from the long-tailed distribution that we can see from the interaction frequency plot for all items.

The sparse matrix representation of the data helps in real-world use cases since the sparse matrix only needs to save the non-zero elements which are relatively few. Collaborative recommenders are used by Netflix where user ratings could be explicitly obtained from customers or implicitly derived from user behavior.

There are many different algorithms which solve the collaborative recommender problems. Broadly they can be classified as:

• Memory-based similarity measures
• Matrix Factorization – e.g. SLIM, WARP, Spark ALS, Funk SVD, SVD++
• Neural Net based – these have the potential to capture even non-linear relations e.g. using Embeddings, Variational Auto-Encoder, Reinforcement Learning, etc.

Content Recommender

These algorithms leverage the metadata available for items and users and try to match the items based on the user’s taste. Item’s metadata are essential attributes that describe an item and user metadata is data that explains the characteristics of the individual users e.g. demographics. Using the past user-item interaction data and these user and item attribute profiles are created for each item and user and similarity matching is then applied to find the top N recommendations.

The distance calculation can be obtained through many different distance metrics but quite often cosine similarity is used. In case the input data is already normalized one can use a simple linear kernel instead of cosine similarity. Another common distance metric is the Euclidean distance but that may not be suitable for recommender if a lot of one hot encoded dummy variables are involved.

Naïve Bayes

This is a simple machine learning algorithm based on Bayes’ theorem of conditional probability. It is naïve because it assumes that all the predictor features are mutually independent. This assumption allows it to make fast predictions and lend it scalability. The assumption of independence between features may not be valid in many real-world cases. It’s commonly used as a baseline method in text classification problem.

In recommender systems, it can be used to predict the likelihood of purchasing a product conditioned upon the likelihood of past purchases. The output scores can be sorted in descending order and top N products can be recommended. Its fast, scalable and performs well in case of categorical predictors.

Sequence Modeling

Sequence modeling is the task of predicting next item/s in the sequence given the earlier items. This term is most often used in context of RNN / LSTM in Natural Language Processing. Concepts similar to text sequence can be applied to other domains as well e.g. stock predictions, likelihood to buy any product, etc.

A simple RNN is illustrated in the figure below. As can be seen the output from the RNN is fed back as an input to it. The same is illustrated in an unrolled form in the figure (right). X_0, X_1, X_{2,  . . . .}  X_t are inputs at different time steps and h_0, h_1, h_{2,  . . . .}  h_t  are the hidden states

There are different configurations which can be used in sequence modeling. These configurations are illustrated in figure below.

• One to One – this is when there is no RNN, the input and outputs are of same size e.g. Image Classification
• One to Many – g. Image captioning where sequence of words is generated to describe the image
• Many to one – e.g. Sentiment classification – given an input text its sentiment is classified as positive or negative
• Many to many (1) – e.g. Neural Machine Translation, Text summarization – given a sequence of text as input another sequence of text is generated as output
• Many to many (2) – e.g. video frames classification – each video frame is classified

When the sequences to be modelled are long, simple RNNs severely suffer from vanishing gradient problem. In such cases, it is better to use a modified RNN architecture which is called LSTM. LSTM has special gates – forget and update which helps it learn from even longer sequences.

In order to predict the likelihood of purchase of a product given the prior sequences we can model it using a many to one architecture. At Epsilon we have successfully built such models for several clients who have seen a significant lift in the purchase rates of different products.

In the retail industry, a customer does not buy a product only once or a few times. A lot of retail products e.g. FMCG items, home décor, apparel, etc. are bought again and again after some time gaps. So, if we know the prior purchase pattern for each product for each customer, we can model it as a sequence and therefore predict the likelihood of purchase of each product in upcoming weeks. The beauty of this solution is that it captures the time aspect of the purchase pattern of customers as well and can recommend products for resell or cross sell depending on the prior purchase sequence. The concept can also be used for demand forecasting at store and product level using multi-variate sequential inputs.

Contrary to sequence modeling, other approaches like collaborative filtering, content filtering, naïve Bayes, etc. cannot capture the time aspect of retail purchases. Looking at the association between past purchases it can help us predict what products the customer might purchase but it can’t be more specific by factoring in the prior purchase sequences. Deep Learning enabled architectures to allow us to include the user and customer metadata as well in a single model. This can help us get more refined models that can better predict the next set of items that the customer is going to purchase.

Recommender systems solve a key problem faced by marketers, that of uniquely addressing a customer with the right product and creative content. This post described how an organization can use its existing transactional data to drive personalization.

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