But, still, there are a lot of high-paying certification programs that professionals can opt for to compete in the competitive world.

If you are planning for a next move in USA, then completing these IT certification programs will boost your skills and abilities, and it will surely help you to stand out in the job market of united states.

With the changing business landscape, the demand for skilled individuals has increased immensely and organizations are focusing more on candidates who have completed certificate courses in these specific fields.  The demand for below-mentioned skills is sky-high, and the supply of professionals is meager.

Master in DevOps Engineering (MDE):- DevOps positions are consistently ranked among the highest paying salaries in the IT industry. DevOps is here to stay. And with that, the need to obtain a DevOps certification is no longer trendy, it’s quickly becoming a non-negotiable. Having DevOps certification makes you ready to work in a team of cross-functional members, including QA, developers, operation engineers, and business analysts.

DevSecOps Certified Professional (DSOCP):- Security in IT’s is a significant issue in today’s digital era, and Cyber attacks are on the rise and the Cyber threats won’t go away overnight. With this harsh reality, it’s inconceivable that any organization today would neglect the security aspect of the DevOps methodology. With this in mind, all businesses are driven digitally and IT management have moved to prioritize security and compliance at all levels. As more organizations see the benefit of end-to-end security implementation, DevOps will either fade away or get absorbed into DevSecOps.

SRE Certified Professional (SRECP):- SRE certification proves that the professional have understanding of set principles and practices designed to help organizations reliably and economically scale services. A certified professional can make an organization’s infrastructures far more stable, predictable, and scalable – all essential elements of software engineering, development, and operations.

Docker & Kubernetes Certification:- The container management tool Docker and Kubernetes are used in DevOps process to manage software parts as isolated, self-sufficient containers that can be deployed and run in any environment. In today’s market, professionals with Docker and Kubernetes skills are highly sought.

Master in Microservices:- Designing your product or application architecture can be tough as much a business decision as a technological one. Microservices is a particular way of developing software, where applications are structured as a collection of autonomous services OR we can say its a way where a large complex application are broken down into individual small-apps that are responsible for one specific product function. This skill allows large companies to gain agility and new tech capabilities to meet the growing customer demands.

Master in Big Data:- Big data professionals helps organizations to work with their data efficiently and use that data to identify new opportunities. Different techniques and algorithms can be applied to predict from data. Multiple business strategies can be applied for future opportunities and success of the organization and that leads to smarter business strategies, more efficient operations, and higher profits. With huge opportunities and investment in the Big Data technologies, certified professionals carrying the skills of big data are in huge demand.

Master in Artificial Intelligence:- This is one of the best certifications you can own if you want to lead the AI-driven technological revolution. It is not just about replacing the human component of the industry. It’s also about making it easier to make decisions based on observable patterns, use logic and reasoning to form conclusions, and build pathways to boost efficiency and production. It is not an easy discipline, but this is the reason why salaries in the AI industry are much higher than average.

Master in Machine Learning:- Machine Learning is one of the fast-emerging technology with high demand in the industry. Whether it be medicine, cybersecurity, automobiles, etc. all these fields are exploring the capabilities of machine learning. It’s obvious that learning more about Machine Learning and becoming a Certified Machine Learning Professional is a great idea and may even be a very wise career move! Naturally, you will be a hot asset for potential employers if you possess domain knowledge and skills in this field.

Master in Data Science OR Analytics:- Data Science is the latest tech trend that has taken the industry by storm. Companies and organizations, irrespective of their trade, are adopting Data Science tools, technologies, and solutions to promote innovation, increase productivity, boost sales, and maximize customer satisfaction. Once you receive the certification, you can apply for promising roles like Data Science, Data Analyst, and many more DATA-driven roles.

Dynatrace

Quantum Computing

AIOps

DataOps

OpenShift/Tanzu/Rancher/Linkerd/Envoy/Traefik/istio/consul

These certifications program will require a few months of hard work and an investment of time and money but once you’ll successfully complete the course you will be ready to achieve your goals no matter what they are.

Wrapping Up

Hope you would found our list of In-Demand IT Certifications For A Great Career in USA useful. Whether you’re a new working professional or an experienced professional, you won’t have trouble following these courses. At DevOpsSchool.com All of these certification courses are delivered by best-in-class trainers and mentors who will guide you every step of the way. Before selecting any certifications program, one only needs to be clear about their goal, know which path to take, and preserve in order to make the best of that opportunity.

Talk to our certification advisor if you need more information and guidance. (contact@devopsschool.com or +91 700 483 5930 (Call/WhatsApp))

The post The 13 In-Demand IT Certifications For A Great Career in USA appeared first on Artificial Intelligence.

Unlike digital-first organisations, traditional businesses have a wealth of enterprise applications built up over decades, many of which continue to run core business processes.

In this series of articles we investigate how organisations are approaching the modernisation, replatforming and migration of legacy applications and related data services. 

We look at the tools and technologies available encompassing aspects of change management and the use of APIs and containerisation (and more) to make legacy functionality and data available to cloud-native applications.

This post is written by Ben Stopford, lead technologist, office of the CTO at Confluent – the company is known for its work as an event streaming platform for Apache Kafka.

Stopford writes as follows…

Both mainframes and databases have an important place in today’s world, but one that is no longer as exclusive as it once was. When customers interact with companies today they connect to a host of different backend systems spanning mobile, desktop, call centres and stores. Throughout this, they expect a single joined-up experience whether its viewing payments and browsing catalogues, being guided by machine learning routines or interfacing with sensors they interact with in the world.

This is a job that is bigger than a mainframe, application or database.

It’s a job that necessitates a whole estate of different applications that work together.

Microservices are an example of this. Cloud is a facilitator. But whatever the incarnation, data always needs to flow from application to application, microservice to microservice or data centre to data centre. In fact, any company that blends software, data and people together in some non-trivial way has to face this problem head-on.

Data in-flight

Event streaming systems help with this by providing first-class data infrastructure that connects and processes data in flight: a real-time conduit that connects microservices, clouds and on-premise data centres together. The architectures that emerge, whether they are at Internet giants like Apple and Netflix or at the stalwarts of the FTSE 500, all have the same aim: to make the many disparate (but critical) parts of a software estate appear, from the outside, to be one.

Of course, mainframes and databases will always be a part of these estates, but increasingly they are individual pieces in an ever-growing and ever-evolving puzzle.

From monoliths to microservices

Think for a moment about why you chose microservices?

If the answer is simply to handle higher throughputs or provide more predictable latencies, there are other, less intrusive solutions available that will scale monolithic applications.

A better reason and rationale is an understanding that microservices let you scale in ‘people-terms’: that means teams working autonomously, releasing independently of one another and not being held up by the teams they depend on, or those that depend on them. Event streaming plays an important role in this, as without it, a microservice architecture inherits many of the issues of the monolith you’re trying to replace.

These issues can be manageable in some cases, for example, if the whole company is one big web application, but where business processes run independently of one another it’s desirable to keep the software and teams that run those different services as decoupled as possible.

For example, connecting your on-premise website to a fraud detection system that runs in the cloud using event streams ensures that both the fraud detection can scale elastically using cloud resources while also being supplied by real-time datasets. But more importantly, should the fraud system go down, the main website remains unaffected.

Events streams provide the physical separation needed to do this. A kind of central nervous system that connects applications at the data layer while keeping them decoupled at the software layer. Whether you’re building something greenfield, evolving a monolith, or going cloud-native, the benefits are the same.

Take events to heart

[So my core technology proposition here is that] event streaming needs to be at the heart of any IT infrastructure… [but why is this so?]

A good way to consider a question like this is to start with the end [goal] in mind. Most companies would like their data to be secure, organised and available in a self-service manner so that teams get access to real-time data of high quality and with minimal fuss. That’s the nirvana for most large organisations, whether it’s the data science team trying to project forward revenues or an application team trying to improve sales conversion rates.

But building a platform that provides secure, self-service data involves solving a host of subtle underlying concerns. Concerns which have many overlaps with data management techniques applied in databases. Data in flight isn’t that different from data at rest after all. So, while the business benefits of connecting the data in a company in an organised way should be relatively self-explanatory, it’s the devil in the details that leads efforts to go awry.

What makes event streaming systems interesting for organisation-level initiatives like these is that, like databases, they are a first-class data infrastructure with processing, transactions, SQL, schema validation and more built-in. Tools that are as important when managing the streams of data that flow between applications as they are when managing the data in the databases that each application uses.

The road to evolution

But this analogy only goes so far i.e. event streaming systems differ from databases in many significant ways.

+The home ground lies in high throughput connectivity, interfacing with the different components in an IT architecture effortlessly while also providing the processing primitives of a first-class data product. It’s the combination of these database-like tools applied to at-scale, real-time datasets that makes all the difference. After all, real-world architectures aren’t static entities, drawn on a whiteboard.

The post IT modernisation series – Confluent: Event streaming moves monoliths to microservices appeared first on Artificial Intelligence.

PagerDuty, a global leader in digital operations management, announced PagerDuty for Cloud Operations, a new solution to help IT and DevOps teams transform their operations practices and capabilities as they move applications and services to the cloud.

The solution helps companies accelerate their journey across the cloud adoption lifecycle from workload migration to cloud-native applications, microservices, and serverless.

The combination of PagerDuty’s platform and integrations with Amazon Web Services (AWS) solutions allows teams to automatically detect incidents, respond in real time, and automate their workflows. PagerDuty and AWS customers can benefit from improved ability to prevent outages, a better customer experience, and increased team productivity.

Cloud spending is expected to grow from $183 billion in 2018 to $500 billion by 2023 as companies migrate to the cloud, shifting from a monolithic IT service architecture to a mesh of microservices where developers are empowered to build and operate their own services.

Traditional centrally controlled IT operating procedures, known as ITIL, weren’t built for the complexity this proliferation of services creates, causing inability to address service outages and problems in real time.

A new cloud operating model is emerging to address the complexity, and PagerDuty for Cloud Operations has been created to ensure teams can work together to prevent and rapidly resolve incidents in this new model.

“PagerDuty for Cloud Operations is crucial to accelerating our ongoing digital transformation as we migrate more critical apps and services to the cloud,” said Mark Huber, Senior Director, Engineering Enablement, Cox Automotive.

“Already, the PagerDuty platform has reduced our incidents by 75%, improved productivity among our developers by 20%, and helped us save millions of dollars a year on operational costs.”

“Traditional operations management approaches don’t meet what organizations need as they migrate workloads to the cloud, develop new cloud-native applications, and adopt containers, microservices, and serverless computing,” said Jukka Alanen, SVP Business Development and Corporate Strategy, PagerDuty.

“PagerDuty for Cloud Operations brings a new operations management model, focused on real-time, automated, and intelligent operations, which enables organizations to maximize their cloud adoption.”

The core components of PagerDuty for Cloud Operations

PagerDuty’s digital operations management platform, which enables teams to detect and understand incidents and other time-sensitive issues, respond in real time, orchestrate and automate workflows, and continuously learn, analyze, and improve operations.

This platform and the additional products such as Event Intelligence, Modern Incident Response, and Analytics are designed for both cloud-native operations as well as a hybrid of cloud and on-premises operations.

Joint integrations between PagerDuty and AWS services that automate visibility and workflows and enable real-time actions based on machine signals from cloud services and applications. These integrations include:

• Monitoring: Amazon CloudWatch, AWS Personal Health Dashboard
• Management and automation: Amazon EventBridge
• Security: AWS Security Hub, Amazon GuardDuty, AWS CloudTrail

PagerDuty’s Digital Operations Maturity Model, which provides benchmarking data and best practices for organizations to help transform their operations as part of their cloud adoption journey. The maturity model helps organizations move from manual, reactive operations toward automated, proactive operations, aligned with what is needed for the cloud.

Solution architecting experience, building on thousands of shared customers between AWS and PagerDuty, with reference architectures, technical integration guidance, and best practices.

The post PagerDuty for Cloud Operations helps orgs accelerate their journey to the cloud appeared first on Artificial Intelligence.

Software services and technology companies are cutting the flab in middle management but they are also hiring, in the thousands, for specific skills.

The IT sector, which employs more than 4 million people, is expected to add 2.2 lakh employees over the next four year, according to staffing services provider Teamlease. Another 5.6 lakh jobs are estimated to be created by e-commerce and tech startups.

Swathi Moorthy tells us about the hot skills employers are looking for and how paying they are.

1. Data sciences/artificial intelligence/machine learning
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This is the obvious one. AI/ML/data sciences professionals are one of the most wanted and highly paid across industries. AI, ML and data sciences are not programming languages but concepts that use logic and analysis to generate critical insights from data.

These skills are in great demand in consumer-facing firms in the retail and healthcare sectors.

“I would not be surprised if firms are willing to pay a premium,” said Guruprasad Srinivasan, Chief Operating Officer for India at Quess Corp, a Bengaluru-based staffing firm. “There is not much of supply.”

What is the pay like?

A beginner can get a guaranteed salary of Rs 8-10 lakh per annum and in a couple of years can step it up to Rs 16 lakh. A data scientist with five to six years of experience can get Rs 30 lakh annually but firms are willing to pay 20-30 percent premium.

Geeta M*, a data scientist with six years of experience, was recently hired by a startup for Rs 46 lakh plus stock options.

1. Certified cloud architects for platforms such as Amazon Web Services, Google Cloud and Microsoft Azure

By 2022 internet networks in India are estimated to carry about 646 petabytes (one million gigabytes of data per day), 490 percent more than in 2017. This growth is in part driven by rising consumption of internet services such as online shopping and mobile phone apps. India is likely to have 630 million internet users by the end of 2019.

On the enterprise side, both IT and non-IT firms are now migrating to the cloud to cut costs and improve efficiency. Most startups are cloud-native in India and are one of the fast-growing cloud consumers.

With the draft data protection and privacy bill likely to be tabled in Parliament, firms will need to invest to store data locally.

According to BS Rao, Vice President–Marketing, CtrlS, a data centre provider, all these are driving data centre growth from current 10 million square feet to 50 million by 2035.

This means that there will be a growing need for cloud architects to manage cloud services.

What is the pay like?

Starting pay is Rs 4-5 lakh per annum, with increases of up to 30 percent every year. Five years of experience can earn Rs 15 lakh or more. For an employee with 12 years of experience, the salary can top Rs 25 lakh.

1. Full-stack developer

A full-stack developer is someone who can handle both the frontend, which involves the user or the business, and the backend, essentially the technical part.

So, a full-stack developer should be knowledgeable in languages such as Python and Java (backend) and HTML and Javascript (for frontend), MySql or other databases languages and server technology. In addition, they should also possess project management skills.

What is the pay like?

Newbies can ask for a package of Rs 5 lakh to Rs 7 lakh. Supaul Chanda, Head of Digital at Teamlease, says that with four to five years of experience, the salary may go up to Rs 13-18 lakh per annum, based on the work profile.

1. DevOps

This is another buzzword, which is high in demand. DevOps is a software development practice, a combination of software development and IT operations. Engineers need to work in cross-functional domains, including coding, system administration and automation tools.

DevOps are a key part of digital transformation. At a time when firms are embracing cloud and moving towards automation, there is need for new models for software development lifecycle that combine both IT operations (servers, cloud and network) and businesses such as software development and testing.

What is the pay like?

New entrants can demand a package of Rs 5 lakh. A DevOps engineer with four to five years of experience can earn Rs 15 lakh per annum.

The post Hot skills and top jobs in IT appeared first on Artificial Intelligence.

The data science world in its most puristic state is populated by parallel processing servers that primarily run Hadoop and execute in batch mode, large troves of data that these processors operate on, and statistically and scientifically trained data scientists who know nothing about IT, or about the requirements of maintaining an IT operation.

While there are organizations that include data science specialties within IT and therefore have the IT management and support expertise nearby, there are an equal number of companies that run their data science departments independently of IT. These departments have little clue of the IT disciplines needed to maintain and support the health of a big data ecosystem.

This is also why many organizations are discovering how critical it is to have data science and IT work hand in hand.

For CIOs and data center leaders, who by necessity should be heavily involved in an IT-data science partnership, and what are the important bases that need to be covered to assure IT support of a data science operation?

Hardware

Two or three years ago, it was a basic rule of thumb that Hadoop, the most dominant big data/data science platform in companies, ran in batch mode. This made it easy for organizations to run big data applications on commodity computing hardware. Now, with the move to more real-time processing of big data, commodity hardware is migrating to in-memory processing, SSD storage and an Apache Spark cluster computing framework. This requires robust processing that can’t necessarily be performed by commodity servers. It also requires IT know-how for configuring hardware components for optimal processing. Accustomed to a fixed record, transactional computing environment, not all IT departments have resident skills for working with or fine-tuning in-memory parallel processing. This is a technical area that IT may need to cross-train or recruit for.

Software

In the Hadoop world, MapReduce is the dominant programming model for processing and generating big data sets with a parallel, distributed algorithm on a cluster. Apache Spark processes in-memory, enabling real-time big data processing. Organizations are moving to more real-time processing, but they also understand the value that Hadoop delivers in a batch environment. From a software standpoint, IT must be able to support both platforms.

Infrastructure

Most IT departments function with a hybrid computing infrastructure that consists of in-house systems and applications in the data center, coupled with private and public cloud systems. This has required IT to think outside of the data center, and to implement management policies, procedures and operations for systems, applications and data that may be in-house, in-cloud or both. Operationally, this has meant that IT must continue to manage its internal technology assets in-house, but also work with cloud vendors that technology asset management is outsourced to, or work in the cloud themselves if assets are only hosted, with the enterprise continuing to manage them.

Support for data science and big data in this more complicated infrastructure takes the IT technology management responsibility one step further, because the management goals for big data differ from those of traditional, fixed data.

Among the support issues for big data that IT must decide on are:

• How much big data, which is voluminous and constantly building, should be archived, and which data should be discarded?
• What are the storage and processing price points of cloud vendors, and at what point do cloud storage and processing become more expensive than their in-house equivalents?
• What is the disaster recovery plan for big data and its applications, which are becoming mission critical for organizations?
• Who is responsible for SLAs, especially in the cloud world, when a big data production problem occurs?
• How is data shuttled safely and securely between the cloud and the data center?

Insights

Data scientists have expertise in statistical analysis and algorithm development, but they don’t necessarily know how much or which data is available for them to operate on. This is an area where IT excels, because its organizational charter is to track all of the data in enterprise storage, as well as data that is incoming and outgoing.

If a marketing manager wants to develop customer analytics that take into account certain facts that are stored internally on customer records, and also in customers’ purchasing and service histories with the company — and the manager also wants to know what customers are interested in by tracking customer activity on Websites and social media — IT is the most knowledgeable when it comes to determining all paths to achieving a total picture of customer information. And it’s the database group, working in tandem with other IT departments, that develops JOINS of data sets that aggregate all of the data so the algorithms data scientists develop can operate on it to develop truest results.

Without IT’s expertise of knowing where the data is and how to access and aggregate it, analytics and data science engineers would be challenged to arrive at accurate insights that can benefit the business.

IT support of the data science operation is a key pillar of corporate analytics success.

IT enables data scientists to do what they do best — design algorithms to mine the best information from data. At the same time, IT is engaged in its best of class “wheel house” — knowing where to find the data and aggregate it.Mary E. Shacklett is an internationally recognized technology commentator and President of Transworld Data, a marketing and technology services firm. Prior to founding her own company, she was Vice President of Product Research and Software Development for Summit Information

The post How IT Supports the Data Science Operation appeared first on Artificial Intelligence.

Two consecutive budgets read by two Finance Ministers (Arun Jaitley and Piyush Goyal) had provisions for a National Artificial Intelligence Portal. But, almost two years have passed since AI (artificial intelligence) was given prominence.

It looks like the project it stuck in red tape.

Both the Ministry of Electronics and Information Technology (MeitY) and Niti Aayog are now staking claim to the project, leading to a complete standstill.

Presenting his 2018-2019 budget speech Arun Jaitley had mandated the Niti Aayog to establish the national programme on AI to guide research and development in new and emerging technologies.

Almost a year later, presenting the Interim Budget 2019-20 in the Lok Sabha, interim Finance Minister Piyush Goyal had spoken about nine priority areas that have been identified for the development of the AI sector in the country.

Even Finance Minister, Nirmala Sitharaman in her maiden speech had said that the government will be increasing its efforts to improve the skills of youth in fields of Artificial Intelligence (AI), big data, robotics, and other newer skills.

Provisions for AI are galore but the project is stuck in a limbo because both Niti Aayog and MeitY are wanting to take the project forward. Sources within the IT Ministry have told India Today that a budget of 300 crores has been sent to the department of expenditure under that Finance Ministry.

Interestingly, even Niti Aayog (government’s think tank) has submitted an expenditure report of approximately Rs 7,000 crore to the department of expenditure.

The department of expenditure is now contemplating the next course of action with regards to the varied budgets it has received.

Prime Minister Narendra Modi has been vocal about his desire to expand India’s reach in artificial intelligence sphere.

On one hand the project is stuck because both MeitY and Niti Aayog are staking a claim on it and on the other hand the government is making tall claims on back of the subject.

Responding to supplementaries during Question Hour, Human Resource Development Minister Ramesh Pokhriyal Nishank said that engineering students are being trained so that they can be a part of the ‘Make in India’ initiative of the government.

The minister highlighted that the All India Council for Technical Education (AICTE) will not allow new conventional disciplines with low employment potential from the academic year 2020-21 and will permit only emerging fields like artificial intelligence and blockchain.

The post Is there a tug of war between Niti Aayog, IT ministry on artificial intelligence project? appeared first on Artificial Intelligence.

OverOps has launched a namesake platform employing machine learning algorithms to capture data from an IT environment that identify potential issues before a DevOps team decides to promote an application into production.

Company CTO Tal Weiss said the OverOps Platform is unique in that, rather than relying on log data, it combines static and dynamic analysis of code as it executes to detect issue. That data then can be accessed either via dashboards or shared with other tools via an open application programming interface (API). The dashboards included with the OverOps Platform are based on open source project Grafana software.

That approach makes it possible to advance usage of artificial intelligence (AI) within IT operations without necessarily requiring that every tool in a DevOps pipeline be upgraded to include support for machine learning algorithms, Weiss said.

OverOps also includes in the platform access to an AWS Lambda-based framework or separate on-premises serverless computing framework to enable DevOps teams to also create their own custom functions and workflows.

Weiss said OverOps is designed to capture machine data about every error and exception at the moment they occur, including details such as the value of all variables across the execution stack, the frequency and failure rate of each error, the classification of new and reintroduced errors and the associated release numbers for each event. Log data is, by comparison, relatively shallow in that it is challenging to determine precise root cause analysis when trying to troubleshoot an issue, he said, noting the OverOps Platform offers visibility into the uncaught and swallowed exceptions that would otherwise be unavailable in log files.

DevOps teams spend an inordinate amount of time analyzing log files in the hopes of discovering an anomaly. But as IT environments continue to scale out, the practicality of analyzing millions, possibly even billions, of log files becomes impractical. OverOps is making the case for employing machine learning algorithms to analyze events before the log file is even created, which eliminates the need to find some way to store log files before they can be analyzed.

There’s naturally a lot of trepidation when it comes to anything to do with machine learning algorithms and other form of AI to manage IT. But as the complexity of IT environments continues to increase, it’s clear DevOps teams will need to rely more on AI to mange IT at levels of scale that were once considered unimaginable. For example, while microservices based on containers may accelerate the rate at which applications can be developed and updated, they also can introduce a phenomenal amount of operational complexity. Most DevOps professionals would rather automate as much as possible the manual labor associated with operations, especially if that leads to more certainty about the quality of the software being promoted into a production environment.

Of course, while making use of machine learning algorithms to analyze code represents a step forward in terms of automation, it’s still a very long way from eliminating the need for DevOps teams altogether.

The post OverOps Brings Machine Learning to DevOps appeared first on Artificial Intelligence.

When asked if AI would eventually replace radiologists, Dr. Curtis Langoltz of Stanford University pithily replied “No, but radiologists who use AI will replace those who do not use it.”

This great insight is highly applicable to IT. Yes, there are many ways we can use AI methods to fully automate a broad range of DevOps tasks. Companies, including CA Technologies, are actively involved in the development of solutions that enable our customers to do exactly that.

Algorithmic machine learning, however, doesn’t just empower systems to perform tasks and solve problems autonomously. It also makes them great active partners with human beings. In fact, much of what machines learn they also wind up teaching.

The synergy between AI and human radiologists, for example, stems in part from the fact that digital systems can differentiate about 200 levels of gray in a diagnostic image—compared to only about 16-20 that are discernable by the human eye. Train an AI system with enough images, and that precise cognitive power can more effectively detect that something is going on.

But for the most effective diagnostic process, you don’t just depend on that detection alone. You use that detection to empower a human diagnostician who can apply a broad understanding of pathologies and deep experience with the complexities of individual patients to deliver the highest quality care.

In DevOps, we can do the same. We can use AI to capture insights that teach us how to continuously optimize our workflows and processes. We can also use our AI learnings to push our work up higher on the value chain.

More specifically, the synergy between AI and human intellect can:

Make development smarter. The speed, quality, and efficiency of development pipelines can be affected by all kinds of subtle factors. A less-than-optimally designed API, for example, can be a small but chronic stumbling block to everyone who has to use it. Scrum outcomes can be undermined by anything from a particular type of technical challenge to a nascent personality conflict.

By capturing a rich set of DevOps metrics and applying machine learning to those metrics, development leaders can discover process bottlenecks and skilling shortfalls. They can better coach individuals and promote team collaboration. The result: a better working environment that facilitates digital agility for the enterprise and higher satisfaction/retention for valuable employees.

Make ops smarter. Enterprises are running increasingly volatile and complex workloads on increasingly hybridized infrastructure. At the same time, the tolerance of internal and external users for latency and outages continues to approach zero. There are also real costs associated with performance problems.

The elastic capacity of public and private cloud does much to help with workload volatility. But adding cloud capacity also has its costs—and end-to-end application performance often depends on back-end systems that are not cloud-based. So not every performance issue can be solved by simply throwing more capacity at it. Nor should it be, if a rearchitecting can fix a bottleneck less expensively.

Here again, AI can teach us a lot. We can uncover opaque interdependencies in processing load and data throughput. We can spot conditions when it may make business sense to throttle cloud costs that aren’t cost-justified. We can even better understand the real-world conditions—whether patterns in customer behaviors or our own marketing programs—that are driving our demand spikes and troughs. All of this helps us deliver consistently responsive digital experiences at a cost that makes good business sense.

Make security smarter. AI is already being broadly implemented in security solutions such as endpoint protection and threat response to automate the detection and neutralization of anomalous activities in the enterprise environment. But effective multi-layer security isn’t just about finding and stopping exploits. It’s also about building applications that are themselves inherently less vulnerable to hacking. This is the essence of DevSecOps.

AI has huge potential value here. We are writing a rapidly growing volume of increasingly sophisticated code. It is very easy for subtle vulnerabilities to hide in that code. As our development practices become more complex—often including multiple contractors—it becomes more difficult to understand exactly where and why these vulnerabilities were introduced into our code. Machine learning can teach us the answers to these questions, so we can more proactively secure our data and our businesses.

It’s especially interesting to consider what may happen as we start to apply AI to DevSecOps across our organizations, as well as within them. More diverse inputs enable machine learning to discover more factors that impact code pipeline performance. By aggregating our knowledge about how we build, deliver and secure our code, we are all likely to benefit with better practices and stronger guardrails.

The post When AI Met DevOps: Machine Teaching appeared first on Artificial Intelligence.

While AI and machine learning are often used interchangeably, machine learning is simply a way of achieving AI. Machine learning at its core is the ability of a machine or system to automatically learn and improve its operation or functions without human input, which is an essential element of any AI.

Now, where all this intelligent learning and processing happens is another story. As the demand for data brought on by technologies like machine learning and AI is growing exponentially, it is pushing IT environments toward a decentralized hybrid computing ecosystem.

Use Cases for the Intelligent Edge

While the majority of machine learning technologies are being hosted in remote cloud data centers, there is a shift happening toward the edge. Businesses are finding that with certain applications, it makes more sense to apply machine learning at the network edge rather than connect back to the cloud. The edge is advantageous for machine learning for a number of reasons, but a key benefit is minimized latency, which leads to faster data processing and real time, automated decision-making.

For example, a content provider may use machine learning applications to understand what viewers in a specific city are currently watching so they can cache the content locally at the edge to improve the viewing experience and lower their operating cost. By running the machine algorithm locally, it minimizes incurred latency and enables the algorithm to learn in real-time as it was designed to do.

In the case of self-driving cars, machine learning applications are being trained both locally in the car itself and at the edge to cut back on bandwidth and latency to process data, which can rack up to about 4,000 GB a day – equivalent to 3 billion peoples’ worth of data, in real-time. Not to mention the life-safety factor required; the ability for these vehicles to process data instantly is critical and can be life-saving as automated decision-making based on road conditions or unexpected instances can keep passengers out of harm’s way.

With data processing, proximity to the network matters, so it’s natural to see a progression toward the edge in order to capture and analyze data on the spot.

Machine Learning at the Edge: Best Practices

As businesses look to deploy machine learning at the edge, there are few key factors to consider.

• Establish objectives. It’s important to first establish the business’s objectives for leveraging machine learning in order to determine what data sources and edge technology solutions are required to support those goals.
• Identify “the question.” In order to establish business objectives for machine learning, businesses need to identify the question they are trying to answer. For example, “What three drivers are negatively impacting customer satisfaction?” It’s important that the question be pointed, identifiable and can ultimately translate into some statistical process in order to derive meaningful answers from the massive amount of data that’s being collected.
• Gain compute, network and storage baselines. Businesses also need to consider that because machine learning algorithms are designed to ingest copious realms of data as they consume an enormous amount of computational power. As such, understanding what the compute requirements are and then building out edge technology from a network, cooling and storage capacity will be critical in order to adequately handle the workload demand.

Machine learning will continue to enhance business decisions as the algorithms improve exponentially as time goes on. Processing and analyzing will increasingly occur wherever it is best suited for any given application – and in many cases that will be at the edge. As the fusion of machine learning and edge continues to evolve, we’ll see it drive business efficiency, automation, predictive capabilities and decision-making on a greater scale.

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When U.S. Special Forces entered Afghanistan in 2001, Facebook didn’t exist, the iPhone had yet to be invented, and “A.I.” often referred to an NBA star. Seventeen years later, American special operations forces continue to ride horseback in rural Afghanistan, but information technology has advanced rapidly. Recent breakthroughs in robotics and artificial intelligence (AI) have captured the popular imagination and prompted sober talk of an impending AI revolution. Yet surprisingly little of that talk has touched on the small wars and insurgencies that have dominated U.S. foreign policy in the 21st century.

The definitive work on emerging technology and insurgency has yet be written, but two recent books offer suggestions for how the era of big data and AI will affect the United States’ modern conflicts. Small Wars, Big Data: The Information Revolution in Modern Conflict, by Eli Berman, Joseph Felter, and Jacob Shapiro, offers few musings about the future of insurgency, but lays out a compelling theory about the ways in which information shapes insurgent violence. By contrast, Paul Scharre’s excellent new book, Army of None: Autonomous Weapons and the Future of War, offers little in the way of counterinsurgency strategy, but is wholly concerned with how artificial intelligence will reshape armed conflict. Taken together, they begin to sketch out a vision for how AI and big data might alter insurgent dynamics.

The core insight of Small Wars, Big Data is that insurgencies are ultimately competitions over information rather than territory or ideology. Since insurgents can readily blend in with their surrounding populations, regime forces cannot defeat an insurgency unless the local population identifies who and where the insurgents are. The challenge for the state is thus to convince local civilians to provide that information, while the challenge for insurgents is to persuade them not to. In Berman, Felter, and Shapiro’s telling, just about everything that happens in an insurgency—from building schools and hospitals on the one hand, to the indiscriminate slaughter of civilians on the other—can be read as an attempt to coax or intimidate civilians into divulging or withholding what they know.

Small Wars, Big Data is by no means the first to offer that argument. But it is unique in terms of the breadth and depth of the empirical evidence it marshals. From the pioneering research of Stathis Kalyvas in the early 2000s on, political scientists from Lisa Hultman to Laia Balcells have compiled an extraordinary body of empirical workon the “micro-foundations” of insurgent and civil war violence. As leading contributors to that literature themselves, the authors do an admirable job of surveying its findings.

Of particular note is chapter four, which offers a thorough overview of the debate regarding the effects of developments in information technology on insurgent violence. As the authors note, on the one hand, information technology may reduce insurgent attacks by making it easier for states to gather intelligence about the insurgents. On the other, it may instead increase attacks by enabling insurgents to better coordinate with one another. By comparing the rollout of cell phone networks with insurgent violence in Iraq, the authors show that cell phones—by offering low cost, anonymous ways of supplying information about insurgents at little risk to the informant—do appear to reduce and disrupt insurgent activity. That the Taliban in Afghanistan and Boko Haram in Nigeria have both repeatedly targeted cell phone towers, despite the improved communication they enable, suggests that even insurgents themselves fear that information technology has tilted the balance of power to the state.

For all its strengths, Small Wars, Big Data is not without its flaws. One is that it gives short shrift to the kind of “brute force” tactics that Jacqueline Hazelton discussed in a controversial article last summer. The book would have been stronger if it had discussed at greater length why exploiting information technology is more effective at defeating insurgencies than draconian policies like mass incarceration, mass resettlement, or even mass killing. But the other critique is that the book’s title hints at a topic it never addresses: It is primarily a book about the role of information in insurgency, rather than information technology. The title makes for great marketing, but it’s a bit of misnomer.

Army of None, by contrast, more than lives up to its billing. Scharre has spent nearly a decade framing the early debate over autonomous weapons in D.C. and the Pentagon, and the experience shows. The book plainly and masterfully lays out the major questions that AI and autonomous weapons raise for the future of armed conflict. Although written for a popular audience, even well-informed academics will find it worthwhile as an introduction to the technical, ethical, and strategic issues that AI-infused weapons systems will introduce.

Scharre’s argument about the coming ubiquity of big data and AI has profound implications for the future of insurgency. Very roughly, two futures are possible. In one, AI and autonomous weapons are both distributed and commoditized, such that insurgents can afford weapons systems that are nearly as capable as those of any given regime. Think of the commercial off-the-shelf drones that the Islamic State has deployed in Iraq and Syria, but with low-level intelligence and object-detection baked in via Tensorflow. Since many of the cutting-edge AI projects are open source and publicly available, it may well be possible to build makeshift lethal autonomous weapons systems that are nearly as good as state-of-the-art systems but at a fraction of the cost. In such a scenario, the balance may shift slightly to insurgents; one shudders to imagine the carnage a Mumbai-style attack could produce if the attackers had lethal drone swarms at their disposal.

However, if the future of artificial intelligence is one that favors scale and centralization, then AI may give the upper hand to regime forces. In this world, regimes with access to the products and infrastructure of a great power, like the United States or China, may rapidly unravel insurgent networks. We might think of this as the “wars of none” scenario, since the regime’s overwhelming informational advantage may come to limit the need for violence altogether.

Recall that the central point of Small Wars, Big Data is that insurgencies are primarily contests for information about the identity and location of insurgents, and that even relatively simple technology—such as a text-based tipline—appears to make it much easier for states to gain that information. What happens when tiplines are replaced with real-time surveillance systems equipped with facial recognition? As the cost of ubiquitous surveillance drops, we may see the emergence of a new counterinsurgency strategy: Whereas the “hearts-and-minds” approach tries to coax information out of civilians and the “brute force” approach attempts to coerce it, the “big brother” approach may bypass civilians altogether. There’s little need for informants if you have enough sensors, cameras, and processing power to recognize and track everyone, everywhere.

Although such a scenario may seem far-fetched, early versions are already feasible. In the United States, Anduril Industries, the latest security startup funded by Peter Thiel, is fast at work building an “electronic” wall that has already proven remarkably effective at detecting and monitoring unauthorized border crossings in Texas. In China, meanwhile, authorities have responded to deadly attacks in Xinjiang by widely deploying facial recognition and mass surveillance technology there. And the capabilities of Chinese authorities are only set to grow: Beijing recently announced a massive new investment in SenseTime, an AI startup whose next generation product aims to identify objects and individuals across 100,000 live camera feedssimultaneously.

Whether AI and information technology will empower states or insurgents remains unclear. Most likely it will do both, with AI and information technology supercharging insurgencies in failing or weak countries while constraining the space in which insurgency can occur everywhere else. What seems certain is that the information revolution is poised to revolutionize insurgency soon too.

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