Today, IT and businesses often find themselves in silos working separately without a shared vision of the customer experience and all the services supporting it. IT must move beyond maintaining up a time to deliver the agility and competitive edge the business needs this means better cohesion between IT and business using predictive tools to prevent service failure meet Splunk IT service intelligence.

 IT service intelligence teams can no longer afford to be reactive especially when it comes to performance and availability. A protective IT strategy is necessary to get ahead of a problem and to protect revenue and your customer experience. Splunk IT service intelligence delivers a predictive IT, running an AIs we have AI scalable data platform that can ingest, search, analyze, and all types of data. ITSI allows you to go from reactive to predictive IT while gaining insights across both IT and business services. Splunk ITSI allows customers to predict service degradation 30 to 40 minutes in advance with the proactive alert for the KPIs most at risk so you can resolve incidents before they start gaining an end-to-end view of both IT and business KPIs on a single screen so you can work on what matters most when it comes to AI ops strategy. ITSI helps you become more proactive and even predictive reduce your incident investigation time business impact outages and event noise ensure your best customer experiences yes and keep the business and IT working better together with Splunk IT service intelligence. Put AI at the heart of your operations and try the free ITSI sandbox from today.

What are the benefits of Splunk IT Service Intelligence?

From the start as we are learning the series of Splunk software tools. In this stack, we discuss that every software has its own performances, capabilities, and security management but how the software works predictive and analytic. This is the reason behind endorsing the system software like how fast service is happening, and how the software is managing by the ITs team. With this effect, there are certain benefits related to Splunk ITSI admin as:-

• Intelligent incident management: Accelerate MTTR using real-time event correlation, automated incident prioritization and integrations with IT service management (ITSM) tools.
• Advance analytics & alerting: It manages a certain function as anomaly detection, adaptive thresholding, and predictive alerts & dashboards.
• Service analyzer: Service monitoring dashboards that display dependent systems and apps. Drill- down into a service’s underlying components to determine what’s impacting its health score.
• End to End visibility: protect business performance by monitoring service health, drill down into underlying infrastructure, all from one dashboard. 

Why we should learn Splunk IT Service Intelligence?

Nowadays, in this trending world. You have met with so many tools in the IT software services. Whenever we talk about Splunk’s advanced services then intelligence plays a prominent role in the IT entities. Now, we are talking about the learning conditions for IT service intelligence. So, what are the reasons to implement a Splunk ITSI? Before we had traditional solutions and those solutions were created as operational challenges like lack of visibility, complex environment, & slow response times. The most important talk is that lack of intelligence service created a significant impact on a business like lost revenue, inefficient productivity, poor customer experiences, & struggle to innovate then it creates technical advance in the field of IT sectors on the basis of Business and IT Service Monitoring, Intelligent Incident Management, Machine Learning, and Predictive Analytics. I wish that these awareness scenarios will give you brain mapping concepts for the Splunk intelligence certified admin.

How our Splunk IT Service Intelligence course/training would help?

This tutorial will help you about understanding the core features of Splunk ITSI like IT event management, service monitoring, and advanced analytics & alerting. It will give you a balancing ideology in the field of KPIs, Data sources, entities, services, incident response, automated remediation, predictive analytics, and event analytics. Through these interactive lectures and hands-on challenges, you will be able to learn to use IT service intelligence, services & glass tables, Notable event & event aggregation policies, and deep dives-investigation with swim lane views. After this, it gives you certification and that makes you qualified for the job opportunities along with the expected high-paid salary and makes your life efficient with future scope.

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Accedian is working with VMware to provide CSPs with near-real-time decisioning and intelligence so they can enable the appropriate quality of experience (QoE) for a variety of applications, announced Accedian on Wednesday.

As a VMware Ready for Telco Cloud partner, Accedian’s Skylight performance analytics solution can be used in conjunction with VMware Telco Cloud Platform. This solution will create an opportunity for CSPs to build out a reliable and high-performing RAN and edge infrastructure to support new, high-bandwidth oriented application workloads that require low latency and assured communications.

CSPs around the world rely on VMware Telco Cloud Platform to deploy and manage virtualized and containerized network functions across their 5G networks. CSP customer can leverage Accedian Skylight to get both continuous and on-demand measurements for the core to RAN, and deliver QoE assurance across all the control plane silos. This is a critical component for data portability and interoperability at hyper scale as it traverses disaggregated RAN infrastructure.

Running on the VMware Telco Cloud Platform, Accedian Skylight component installation is easily orchestrated, tests are easily provisioned, intelligent in-context analysis is easily accomplished, and dashboards are insightful and easily consumed.

In 2011, the Center for Devices and Radiological Health (CDRH) initiated the Case for Quality, which provided guidance on how software validation processes can be improved and streamlined for the medical device, pharmaceutical, and biotech industries. By refocusing on patient and product safety, quality assurance, and data integrity, some suggest that validation documentation can be reduced (between 20% and 40%, in my experience) by applying automated testing and deployment. The benefits are accurate code, faster development processes, and reduced validation documentation.

This article is part one of a three-part series and continues the discussion on computer software assurance (CSA), introduces new technologies and methods to achieve continuous software development, provides insight into new standards (e.g., ISO 9001 and ISO 13485-2016), and concludes with a few good reasons to join the growing number of medical device, pharma, and biotech companies with software specialists (or companies that work with software development organizations) who are creating and/or using test automation tools and applying them throughout the software development life cycle (SDLC) phases: inception, design, implementation, maintenance and monitoring, audit, and disposal.

As we await FDA’s communication on the CSA guidance, we have an opportunity to reevaluate the phases of the SDLC and start to think about using new software development methods, such as a combination of artificial intelligence (AI) and adaptive machine language (ML) to provide human-like instructions for automated testing. Challenges include how to evaluate your SDLC phases and make the transition to continuous test automation. What is most puzzling to many is the impact of making this transition from traditional computer system validation (CSV), effectively and efficiently, to a continuous validation life cycle, while applying the tenets of CSA: critical thinking, risk management, patient and product safety, quality assurance, and data integrity.

ISO Standards Address Risk Management
For those in the medical device industry, your software development team (or your software supplier that you work with) are required to comply with medical device regulations and standards, such as ISPE GAMP 5, ISO 9001, and ISO 13485-2016 – Medical Devices. The regulations and standards include a risk-based assessment (RBA) for all computerized software systems. ISO 9001 requires this assessment during the initial phase of the SDLC; ISO 13485-2016, however, requires that a risk-based assessment be updated at every stage of the SDLC to determine the level of risk and the validation and/or change control actions required to be compliant.

Applying the Agile methodology enables you to find opportunities to improve, implement, and comply with the regulations and standards since it uses an iterative approach to ensure quality: continuous code reviews, continuous testing, continuous deployment, continuous monitoring and maintenance, and continuous audit and disposal. Many software development organizations are already performing automated testing and updating their SDLC. Agile testing methods, such as behavior-driven development (BDD), test-driven development (TDD), and acceptance test-driven development (ATDD), are all different approaches to validating code and may be used for automated testing. With the Agile method and progress in development of intelligent technologies, manual testing and/or unscripted testing for low-risk features may become obsolete.

Information Technology Alignment With Software Engineering
Intelligent systems require IT and DevOps personnel, along with software developers, programmers, and software engineers, to acquire new skills and knowledge while applying the principles of software engineering. The ability to get back to basics and skills in software engineering principles, mathematics, and machine language will continue to be highly desired. Making the transition to continuous validation and continuous testing will not be an easy task, especially for mature companies that have been performing traditional validation since 2003 or earlier. Typically, the quality organization has been responsible for ensuring that the software application(s) are compliant and meet all required regulations for software validation. The process seemed straightforward: inception, design (analyze) implementation (build, test, deploy), maintenance and monitoring, audit, and disposal.

Quality assurance’s (QA) role was to oversee the validation effort by reviewing all computer system validation processes and documentation to ensure that they followed the company’s policies and standard operating procedures (SOPs) for compliance and met the associated regulations and standards. Deviations were documented, errors were corrected, and final documentation was reviewed, approved, and stored following 21 CFR Part 11 regulations. With continuous life cycle management for software development, QA’s focus will be on patient and product safety rather than manual testing and documentation, which will be replaced by test automation to enable continuous validation.

Opportunities For Continuous Life Cycle Management
By applying the concept of continuous life cycle management for software development, advantages such as the ability to address risk (critical, high, medium, etc.) during each stage of the development process are feasible. Performing iterative code reviews and automated testing enables early detection of errors, resulting in error-free code that can be shared with other software developers and programmers, minimizing change control actions after software is released. Organizations that will need to reinvent themselves include, but are not limited to, the following:

DevOps groups have developed a set of practices for software development (Dev) and IT operations (Ops). The purpose is to streamline the SDLC, applying validation methodologies, such as Agile, to software development, and providing continuous delivery.
Quality groups will need to change as intelligent technologies replace repetitive and mundane validation tasks by providing automated assurance that the software is performing as it was intended and according to the user requirements specifications.
Intelligent technologies and methods (e.g., AI and ML) will replace traditional/legacy technologies and methods that will over time become obsolete.
SDLC needs to be modernized and get back to basics by applying software engineering discipline and principles to new applications that enable continuous life cycle management.
AI And Advanced Technology And Research
AI presents a modern way for quality organizations to be compliant using test automation. AI is also a method used to advance technology and research by directing computers to perform automated tasks (using algorithms) through the application and translation of machine language. AI can teach machines to learn for themselves and find new avenues for learning.

As the life sciences industry starts taking advantage of AI and ML, its leaders will be known as future thought leaders, visionaries, and influencers. When we think of AI, some examples that come to mind are: adaptive (i.e., robust, efficient, and agile), prescriptive, and actionable data analytics; machine learning, automated administrative tasks (i.e., for patients, doctors, and healthcare personnel), as well as intelligent medical devices; and machines that can be designed to help and assist humans. Some of the top uses include, but are not limited to, diagnostic imaging, surgical robots, and faster, smarter, and secure tools. In fact, when looking at the world of robotics, machines can be designed in such a way as to imitate humans. By integrating engineering fields (i.e., mechanical engineering with computer engineering), software engineers can now develop programs in human-like language.

Some new challenges for software developers and software validation leads may include, but are not limited to, how do you test code when the outcome of the prescribed code potentially provides different results each time you test it? How do you test code when the inputs vary and the outputs are based upon other factors? How do you leverage the extensive work of the software developer/software engineer without duplicating the code? Answers to these and other questions will be discussed as we do a deeper dive into AI and ML.

Concepts For Intelligent Technologies
Concepts that enable intelligent technologies include, but are not limited to, the following:

AML: ACPI Machine Language (AML) is platform independent code.
Machine learning: Can machines learn by themselves? Maybe, if machines were coded to think like humans, learn to read text, and figure out the purpose of the text and/or understand music.
Neural networks are computer systems that simulate the human brain and can be taught to understand things based on their structure. Applied intelligence is AI services coupled with analytics and automation to create a strategy for your organization.
Predictive analytics uses data, statistical algorithms, and ML techniques to predict trends, human behaviors, and patterns based on current and historical data.
Conclusion

Biopharma, medical device, and pharmaceutical companies understand the benefits of test automation and many have already included continuous integration (CI) and continuous development and deployment (CDD) into their SDLC, while others are waiting for the new CSA guidance from FDA on how continuous testing will impact current validation regulations. Either way, intelligent, automated systems (e.g., AI and ML) are already here and will continue to be the future direction of technology development. The impact on these industries’ software and on the validation life cycle will be huge. The next article in this three-part series will continue to expand on validation challenges and next-generation intelligent technologies.

Observability is a staple of high-performing software and DevOps teams. Research shows that a comprehensive observability solution, along with a number of other technical practices, positively contributes to continuous delivery and service uptime.

Observability is sometimes confused with monitoring, but there is a clear difference between the two; it’s important to understand the distinction. Observability refers to a technical solution that enables teams to actively debug a system. It is based on exploring activities, properties and patterns that are not defined in advance. Monitoring, on the other hand, is a technical solution that enables teams to watch and understand the state of their systems, and is based on gathering predefined sets of metrics or logs.

What Makes Kubernetes Observability Different?
Conventional observability and monitoring tools were designed for monolithic systems; observing the health and behavior of a single application instance. Complex distributed microservices architectures, like Kubernetes, are constantly changing, with hundreds and even thousands of pods being created and destroyed within minutes. Because this environment is so dynamic, pre-defined metrics and logs aren’t effective for troubleshooting issues. Conventional observability approaches, which work well in traditional, monolithic environments, are inadequate for Kubernetes. So, an observability solution that is purpose-built for a distributed microservices architecture is needed to match the unpredictable nature of Kubernetes cluster behavior, and to capture the data required for teams to identify and troubleshoot issues in real time.

Kubernetes Observability Challenges
Kubernetes provides abstraction and simplicity with a declarative model to program complex deployments. However, when it comes to debugging microservices, this abstraction and simplicity actually creates complexity. There are several reasons why.

Kubernetes microservices architecture itself is complex, involving tens to hundreds of microservices that are communicating. Debugging this architecture requires specialized tools.
Kubernetes clusters run on a distributed infrastructure that is spread across on-premises, hybrid and cloud environments.
Kubernetes infrastructure is dynamic. The platform spins up required resources and provides ephemeral infrastructure to scale the application based on demand.
Kubernetes deployments need fine-grained security and an observability model that complements a defense-in-depth approach. Some DNS issues, for example, can indicate a compromised resource in the cluster.
Given the complex nature of Kubernetes microservices deployments and the overwhelming amount of data generated, it’s not humanly possible to diagnose and troubleshoot this kind of environment. This becomes even more problematic when mission-critical applications are involved. Given the density of applications and the dynamic nature of the computing environment, the problem is worsening each day.

Enabling Kubernetes Observability with Artificial Intelligence
Existing tools are inadequate. It’s time to reimagine a solution for this critical observability problem. We can start by applying machine learning and artificial intelligence (AI) to observability; in effect, deploying machines to debug machines. By automating dynamic monitoring processes, for example, we can create intelligent observability that converts telemetry data into actionable insights. We can use AI to analyze this data and identify problem patterns, and create unique observability “snapshots” which can be used to build reference templates that can be catalogued and accessed by troubleshooting teams when issues arise.

Kubernetes introduces dynamism to distributed infrastructure, and a high level of complexity for troubleshooting teams. By applying machine learning and AI to observability, we can open exciting new avenues to make Kubernetes more consistently reliable and secure, speed time to root cause analysis and resolution, and make it easier for new SREs, DevOps engineers and service owners to effectively debug dynamic, distributed Kubernetes environments.

Technology is undoubtedly ruling over us lately. Owing to the COVID-19 pandemic, technology is our best bet today. At present, no industries are left untouched from adopting cutting-edge technologies like Artificial Intelligence (AI) and Machine Learning (ML). Some industries that adopted AI and ML include automotive industries, health, logistics, etc. The hospitality sector in India has also not been exempted from adopting such world-class technology to ensure profit maximization in the competitive market.

The hospitality industry encompasses a broad category of fields like tourism, lodging, transport, event planning, theme parks, and others. It is undoubtedly a wide sector, but notably, the leaders in the field seem to be the hotels and accommodation along with restaurants and bars. The hospitality sector in India strongly requires the much-needed application of AI and ML. It is not only for surviving in the pandemic scenario but also for maximizing their profit margins. This industry mostly caters to the wants of the customers more than their needs. Therefore, companies in the hospitality sector must adopt AI and ML for ensuring continuous delivery of their best services at a competitive price to their customers. Here’s how AI and ML help the hospitality sector in ensuring survival and profits.

Artificial intelligence enhances hotel operations

Answering customer calls, attending front desk guests while responding to online guest queries simultaneously is no more a troublesome issue that existed for so long. With Artificial Intelligence (AI), staff does not need to work under high-quality performance pressure and perform like robots which leads to poor service quality. Chatbots emerged as a saviour and a boon to assist a multitude of guests effortlessly. Chatbots take care of all the online queries from potential customers while the front desk staff can serve the guests in a hassle-free manner, thereby enhancing hotel operations without compromising on any customer.

AI boosts personalized services

AI personalization synthesizes diverse customer data sets to extract relevant insights. By leveraging the priceless insights, the hotels can target great marketing efforts and deliver outstanding services. This helps in offering a seamless customer experience. With AI, hotels can properly understand the preferences of each customer. AI chatbots also offer a personalized conversation that effectively addresses the individual needs of every customer more conveniently and helpfully. Customers prefer a fast response to all their queries, especially online. AI chatbots complement it by facilitating immediate response and managing multiple customer needs seamlessly.

Competitive pricing via ML

What’s important for every business to survive in the marketplace is to use a competitive pricing strategy. The hospitality sector leverages competitive pricing strategies that are considered to be one of the most significant strategies to attract customers and enhance sales without compromising on profits. What helps in achieving this is Machine Learning. It helps to achieve the best possible pricing for services based on hotel history, seasonability, local competition, external real-time events, local events, third-party promotions, etc. Basis such factors, the data is run via predictive models and can be analyzed to offer competitive pricing for companies that help them achieve an edge in the broad marketplace.

ML improves customer service and satisfaction

Customers belong from varied backgrounds so their expectations, needs, and demands differ from each other. A process known as Market Segmentation is employed in every sector, including the hospitality sector, to ensure that each customer’s wish is fulfilled according to their tastes and preferences. The entire customer spectrum is segregated into various segments with more or less similar expectations and demands. With Machine Learning (ML), the customer spectrum becomes more diverse into extremely smaller groups and sub-groups that were not witnessed ever before. This process improves the quality of customer service and satisfaction by ensuring a more profitable business.

Summing up

Both Artificial Intelligence and Machine Learning are leading the way to innovation in every sector. They continue to be explored and applied to new sectors every day. The hospitality sector should also leverage these technologies to tackle the unseen problems and challenges to satisfy the customers. Therefore, from enhancing a hotel’s operation, offering personalized services & competitive pricing to improving customer satisfaction via phenomenal service, the role of AI and ML to maximize profits in the hospitality sector is unparalleled. It shows us the untapped potential of the technologies that own the ability to change the face of the hospitality sector.

US-based tech majors Nvidia and VMware on Tuesday announced a collaboration to virtualise Artificial Intelligence (AI) workloads on VMware vSphere with Nvidia AI Enterprise.

Nvidia unveiled AI Enterprise, a comprehensive software suite of enterprise-grade AI tools and frameworks, exclusively with VMware.

The offering gives enterprises the software required to develop a broad range of AI solutions, such as advanced diagnostics in healthcare, smart factories for manufacturing, and fraud detection in financial services.

“Nvidia AI Enterprise enables customers to reduce AI model development time from 80 weeks to just eight weeks, and allows them to deploy and manage advanced AI applications on VMware vSphere with the same scale-out, record-breaking Nvidia accelerated computing performance that’s possible on bare metal,” Justin Boitano, VP and General Manager of Enterprise and Edge Computing at Nvidia, said in a statement.

With the Nvidia AI Enterprise software suite, IT professionals at the hundreds of thousands of enterprises that use vSphere for compute virtualization can now support AI with the same tools they use to manage large-scale data centers and hybrid cloud environments.

The Nvidia software suite provides scale-out, multi-node, AI application performance on vSphere that is indistinguishable from bare-metal servers.

The AI Enterprise provides compatibility for a broad set of accelerated CUDA applications, AI frameworks, pre-trained models and software development kits running in the hybrid cloud.

Nvidia has also certified VMware vSphere as the only compute virtualization software to provide hypervisor support for live migration with Nvidia Multi-Instance GPU technology, which allows each A100 GPU to be partitioned into up to seven instances at the hardware level to maximize efficiency for workloads of all sizes.

Ori Keren, LinearB CEO, said rather than relying on antiquated processes that require software development teams to track progress by reentering data into a project management application, it’s now possible to automatically collect all the relevant data required from the tools and platforms being employed to build software.

The LinearB platform correlates DevOps pipeline data gathered from code, Git, projects and releases to provide visibility and context to every member of the development team. LinearB employs machine learning algorithms to construct and correlate that data, in real-time, to provide accurate insights into project status. That’s critical, because it empowers every individual on that team to make informed decisions about which tasks to prioritize without waiting for an explicit directive, Keren said.

LinearB will, in part, use the funding to build integrations with a wider range of DevOps platforms and tools, Keren added.

Software delivery intelligence differs from value stream management (VSM) platforms; though they provide similar insights, those surfaced by LinearB are aimed at developers rather than C-level executives trying to optimize deployment of limited resources across multiple teams, said Keren. In many ways, the data collected by LinearB could be employed to better inform a VSM platform, Keren added.

LinearB, in contrast, makes it easier to identify which teams and projects need additional help in near-real-time using metrics surfaced in a dashboard that track, for example, cycle times. The goal is to provide insights that make it easier to predict and eliminate project delays, remediate high-risk code before it is included in a production environment, eliminate bottlenecks and make certain the focus of the DevOps team remains on tasks of the highest priority.

More than 1,500 software development teams are already using LinearB as an alternative to traditional project management applications. Most of those customers are building commercial applications, so the goal now is to extend the reach of LinearB into the enterprise, said Keren.

LinearB, of course, doesn’t eliminate the need for project managers. However, it does eliminate the need for project managers to nag developers to update a project management application. Given the rate at which application development projects are now updated, much of the data in a project management application is outdated shortly after it is entered.

There’s a lot of pressure on application development teams these days. Many projects have been accelerated as part of an effort to more rapidly embrace digital business transformation in the wake of the economic downturn brought on by the COVID-19 pandemic. The challenge for many developers is they, too, are working from home to help combat the spread of the virus. That often results in interactions with managers over video conferencing calls that lack context. Having access to a software development intelligence platform should enable everyone with a vested interest in application deployment speed to make more informed decisions faster.

The Australian Institute of ICT (AIICT) has introduced a new ‘Certified Artificial Intelligence Professional’course, jointly developed with Amazon Web Services (AWS)for aspiring Artificial Intelligence (AI) developers and Machine Learning (ML) professionals.

The course enables novice practitioners who are interested in entering the field of AI to become job-ready engineers in just six months. There will be a skill assessment to evaluate the student’s current skills and experience, but the only prerequisites are a basic understanding of mathematics and statistics and, or alternatively, relevant experience in the industry, such as an internship.

According to the 2020 IT Skills and Salary Report, ‘AI, cognitive computing and machine learning’ was reported as the second weakest IT skillset in organisations across the world, presenting serious hiring challenges for IT managers.[1] The lack of skilled AI professionals forms part of a broader technology skills shortage in Australia, with new research commissioned by AWS revealing that Australia will require an additional 6.5 million newly-skilled and reskilled digital workers by 2026 to meet future demand for technology skills (79 per cent more than Australia currently has).

Jon Lang, CEO of AIICT said, “We’re seeing a severe shortage of skilled AI professionals and in many instances, organisations are simply unable to fill these roles. With AI and ML becoming a critical part of the digital transformation process for many organisations, the demand is on the rise for engineers with these specific skills across a wide range of industries.”

“At AIICT, we are committed to providing all our students with the most contemporary, relevant and future-focused skills. Together with AWS we hope to address the skyrocketing demand in the AI field and support those looking to start their career as an AI engineer,” added Lang.

An ‘Artificial Intelligence Specialist’ is currently the top emerging job role in both Australia and the United States, according to the LinkedIn 2020 Report, with hiring growth surging 74% annually in the last four years.[2]

Moreover, as of January 2021, the average salary for a Machine Learning Engineer in Australia is estimated at $94,255 ($1,812 weekly), according to Indeed.[3] This is 34% above the average annual earnings for all employees in Australia, which is $66,575 ($1,280 weekly), according to the ABS[4].

“There have been rapid technological advances in cloud computing, machine learning and artificial intelligence, however we still face an ongoing skills shortage in these areas,” said Melanie Botha, Head of Training and Certification, Australia and New Zealand. “AWS is passionate about continual education, and the need to build new digital skills to drive economic growth and job creation in Australia and New Zealand. Together with AIICT, we want to close the digital skills gap, equip individuals with job-ready skills that will boost their employability, and support the upskilling of our nation.”

AIICT is a start-up launched in 2019 by DDLS, one of Australia’s largest providers of corporate IT and cybersecurity training, and is one of only two authorised AWS Training Partners in the country.

The Certified Artificial Intelligence Professional course prepares individuals for a role as an entry-level AI developer or Machine Learning Engineer. Through its unique Industry Partner Program, AIICT connects recent graduates with leading employers to help them secure a front-line role, while simultaneously tackling skills shortages in the field.

The course incorporates three globally recognised AWS certifications, including AWS Certified Cloud Practitioner, AWS Certified Data Analytics and AWS Certified Machine Learning, as well as the TensorFlow Developer Certificate.

The hands-on course places a high focus on the development of real-world skills, and provides students access to unique ‘live labs’, which enable them to practice developing and deploying machine learning models in a simulated work environment.

The course platform also includes video content, quizzes, expert led webinars and direct access to online mentorship from industry experts, who are on hand to guide students through the course material.

AIICT operates using a unique self-paced course schedule, which is not bound by the traditional academic structures of terms and semesters. The flexible learning options allow students to build an online learning schedule around their life and encourages people who may not have considered a career in AI as an option previously, to undertake training.

The AIICT platform also allows students to join a community of like-minded individuals for communication and collaboration as they progress through the course.

Some of the course’s key learning outcomes include:

Understanding the fundamentals of AI/ML processes and concepts, and elements of data science.
Learning the key concepts of cloud computing with a deep dive into AWS.
Gaining skills in Python, SQL JavaScript, and Deep Learning.
Learning how to build scalable AI-powered algorithms with TensorFlow.
Exploring ML toolsets and processes to build models with Amazon SageMaker and computer visions based on smart applications.
Understanding data analytics and its application to AWS

AIICT

The Australian Institute of ICT (AIICT) is a start-up launched by DDLS, Australia’s largest provider of vendor-certified cybersecurity and ICT training in Australia. Launched in 2019, AIICT was designed to provide those in the ICT and cybersecurity industries, or those looking to enter the industries with the most contemporary, relevant, and future-focused skills needed to succeed. As an operational division of DDLS, AIICT aims to provide online, self-paced, and nationally accredited cybersecurity and ICT training, leveraging the experience, skills and knowledge DDLS has accumulated as leaders in this industry for more than 30 years. The training is delivered via the Moodle LMS platform and leverages world renowned content from CompTIA, Skillsoft, Microsoft and LinkedIn Learning.

Microsoft, Google, and AWS are collaborating with pharma firms to develop drugs fast & cheaply with AI and Cloud Computing.
Hospitals and health care providers are using cloud service and AI for digital transformation. COVID-19 pandemic has seen the rapid adoption of telemedicine and healthcare chatbots that use artificial intelligence and machine learning. To further the course of digital transformation, pharmaceutical companies are employing the cloud to polish their business operations in fundamental ways.

This is called computational drug discovery.
It uses a combination of cloud computing and AI to make the process of drug development faster and cheaper. Big drug companies are taking complete advantage of the advancements in artificial intelligence and the computational capabilities of the cloud to test this new method,

Many pharma companies are using this tech-driven approach to develop traditional medications along with new categories of drugs that work at the DNA and RNA level to stop diseases. Hyperscale cloud providers like AWS, Google, and Microsoft are vital for this approach.

Moderna’s Biological Software
Cloud provider AWS is a significant element in the drug research, development, and production processes at Moderna. The company is developing a new class of treatments using mRNA that uses instructions in DNA to build the proteins in human cells. mRNA medicines can inform the human body to build certain proteins that treat or prevent an illness. Moderna’s mRNA platform uses the cloud-based computational capacity to run a variety of algorithms that design individual mRNA sequences. Moderna uses Amazon Elastic Compute Cloud with Auto Scaling, Amazon EC2 Spot Instances, the Amazon Relational Database Service, and Amazon Simple Queue Service.

With AWS, the company is able to shorten the time required to advance drug candidates to clinical studies and create treatments that wouldn’t have been possible until a few years ago.

Microsoft’s Role In Drug Development
In 2017, Microsoft announced a technology development alliance with a global biopharmaceutical service organization, Parexel. The biopharma company combined Microsoft’s global cloud infrastructure with its clinical and regulatory technology domain expertise.

Prior to this stint, Microsoft was an early-stage funder of Cloud Pharmaceuticals, a drug company that was started in 2011 with technology developed by Duke University. Their tech platform, Quantum Molecular Design, uses a combination of AI, advanced quantum chemistry, and cloud computing to find out the best candidates for new small-molecule treatments.

Using Artificial Intelligence To Understand Human Protein-Folding
One of the oldest companies in the industry, Alphabet, is also using artificial intelligence to accelerate its drug development process. After acquiring the London-based startup DeepMind, Alphabet aims to tackle some of the most pressing real-world challenges that include slow drug development.

The DeepMind team uses artificial intelligence to understand protein folding, which guides almost every function of our bodies. When proteins become tangled and deformed, it has the potential to cause various diseases like diabetes, Parkinson’s, and Alzheimer’s. With accurate protein structure models, researchers can use practical applications of AI to speed up the processes to develop drugs for many such diseases.

In 2018, one of DeepMind’s products, AlphaFold, entered into a protein-folding competition. Researchers from all over the world were in the audience where teams predicted the structures of proteins from lists of specific amino acids, which were sent to the teams every few days over several months. AlphaFold defeated 97 other participants. AphaFold’s algorithm predicted the most accurate structure for 25 out of 43 proteins.

Predicting protein-folding is a complex process because the human body can produce billions of different proteins. Cloud computing’s capabilities are evident as it helps researchers and pharmaceutical companies to analyze and understand vast volumes of data to come up with new treatments.

WASHINGTON, Oct. 20, 2020 /PRNewswire/ — In order to provide rapid innovation in the areas of DevSecOps and Agile software engineering, Hunter Strategy, a premier DevSecOps consultancy, has partnered with leading single application for the entire DevSecOps lifecycle provider, GitLab. Hunter Strategy joins the GitLab Partner Program as a reseller and strategic consulting partner to address government clients’ diverse DevSecOps implementation needs. Hunter will be migrating its DevSecOps acceleration framework, HunterOps, to be fully implemented on the GitLab Enterprise platform. Rather than managing multiple threads across disparate tools, GitLab helps software development teams collaborate and streamline from a single conversation on a single application. GitLab provides teams a single data store, one user interface and one permission model across the DevOps lifecycle, enabling government IT teams to collaborate, reduce cycle time, and focus exclusively on building software that empowers their missions.

“GitLab is one of the rare companies that builds quality software and manages to be fundamentally different in an overcrowded marketplace,” said Matthew Triner, Founder of Hunter Strategy. “As federal enterprises start to move to DevSecOps, it will only make sense that they look to streamline their tool bloat with best in breed suites like GitLab.”

“GitLab is excited to build a strategic relationship with Hunter Strategy,” said Michelle Hodges, vice president of global channels at GitLab. “Hunter Strategy’s expertise across DevSecOps and software factory engineering makes them a great fit for the GitLab Partner Program and enables us to work together to create high-value services for mutual customers who need to deliver secure applications quickly.”

About HunterOps

HunterOps is Hunter Strategy’s DevSecOps acceleration framework. Built upon a set of secure modular containers from known repositories such as DoD’s Iron Bank. HunterOps is a quick start framework which enables our customers to set up secure Software Factory Environments within a matter of hours or days to rapidly begin DevSecOps transformation.

About Hunter Strategy

Headquartered in Washington DC’s Cathedral Heights neighborhood, Hunter Strategy is a Small Business Administration (SBA) HUBZone certified small business that offers strategic DevSecOps solutions to federal agencies, Fortune 1000 companies, and emerging technology firms. In addition to DevSecOps, Hunter Strategy’s core capabilities include cloud software factory engineering, cyber risk management, and agile software development.

NEW YORK, Oct. 6, 2020 /PRNewswire/ — IntSights, the threat intelligence company focused on enabling enterprises to Defend Forward™, announced today the availability of the new certified IntSights Application for Splunk. This partnership between the market’s only all-in-one External Threat Protection Suite, IntSights, and Splunk, provider of the Data-to-Everything® Platform, empowers joint customers to seamlessly access enterprise-specific threat intelligence from IntSights directly within existing Splunk deployments.

Effective cyber defense extends beyond the corporate perimeter. Understanding how, when, and where attacks are likely to strike is critical. When organizations augment SIEM solutions with integrated threat intelligence, they can proactively defend and neutralize threats at the source.

The new IntSights App for Splunk introduces a revolutionary approach for connecting threat intelligence from multiple sources across the clear, deep, and dark web with a customer’s Splunk environment. The IntSights app is a unique bidirectional integration that correlates, enriches, and manages organization-specific vulnerabilities, and offers the ability to easily conduct comprehensive IntSights-driven investigations… all within Splunk.

“With this robust app, security teams can directly access IntSights-enriched IOCs from within their Splunk environment. This gives those on the front line vital real-time context for informed and prioritized risk management,” noted Yaron Paryanty, VP of Product Management at IntSights.

IntSights for Splunk enables SOC teams to:

Expand real-time visibility of threat actors and malware targeting the enterprise’s digital assets
Generate automatic alerts for relevant active indicators in an organization’s network environment
Seamlessly conduct advanced IntSights investigations from within the app
Accelerate informed decision-making and automate incident response
Instantly identify and rapidly remediate critical threats before business impact

Stay tuned for additional enhancements to the IntSights App for Splunk. In the near future, mutual customers will be able to continuously access IntSights organization-specific threat alerts and risk-scored CVEs from within their Splunk environments.

About IntSights

IntSights is revolutionizing cybersecurity operations with the industry’s only all-in-one external threat protection platform designed to neutralize cyberattacks outside the wire. Our unique cyber reconnaissance capabilities enable continuous monitoring of an enterprise’s external digital profile across the clear, deep, and dark web to identify emerging threats and orchestrate proactive response. Tailored threat intelligence that seamlessly integrates with security infrastructure for dynamic defense has made IntSights one of the fastest-growing cybersecurity companies in the world. IntSights has offices in Amsterdam, Boston, Dallas, New York, Singapore, Tel Aviv, and Tokyo. To learn more

What Is Cloudscene’s Cloud Pathfinder?

Cloudscene launched the Cloud Pathfinder app to help enterprises quickly identify service providers that can deliver connectivity to Google Cloud from any data center or on-net building anywhere in the world. The tool is designed to remove one of the most common friction points in enterprise cloud adoption: knowing exactly which carriers and network providers can connect a specific physical location to Google Cloud’s global infrastructure.

How Cloud Pathfinder Works

The workflow is straightforward:

1. Address search: A user enters a physical address or selects a data center location.
2. Provider discovery: The app queries Cloudscene’s database and surfaces service providers capable of delivering connectivity from that location to a Google Cloud node.
3. Shortlisting: Users can shortlist relevant providers directly within the app.
4. Outreach: Enterprises contact shortlisted connectivity partners directly to request a quote.

The data underpinning Cloud Pathfinder comes from the connectivity information of more than 6,500 service providers listed on Cloudscene’s platform. Importantly, service providers can log in and manage their own data directly, which is intended to keep the information current and accurate over time.

The Cloudscene Platform Behind It

Cloudscene operates an independent market intelligence platform that aggregates data on data centers, service providers, network fabrics, and cloud on-ramps. Cloud Pathfinder is integrated with this broader dataset, giving enterprises visibility into connectivity options for both colocation data centers and on-net buildings — locations already served by a provider’s fiber or network infrastructure.

Why Google Cloud Partnered with Cloudscene

According to Google Cloud product manager Kerry Takenaka at the time of the announcement:

“Google Cloud has chosen Cloudscene to support our strategy to bring the cloud one step closer to the enterprise. By enabling our customers with an app like Cloud Pathfinder, we’re empowering the enterprise in their cloud adoption journey.”

Kerry Takenaka, Product Manager, Google Cloud

The partnership reflects a broader industry trend: cloud providers working with third-party marketplace and intelligence platforms to reduce the complexity of procuring last-mile or dedicated network connectivity.

What This Means for Service Providers and Data Center Operators

Cloudscene explicitly encourages data center operators, network fabrics, and service providers to claim and keep their profiles updated on the platform. Providers with on-net buildings are also urged to submit lists of connected locations — via spreadsheet, JSON file, or API — so those locations appear in Cloud Pathfinder search results.

For a service provider, visibility in Cloud Pathfinder is effectively free inbound lead generation: enterprises searching for Google Cloud connectivity can find and shortlist that provider without the provider needing to run targeted outreach campaigns.

Roadmap: Latency Metrics via ThousandEyes

At launch, Cloud Pathfinder focused on provider discovery. The announced next phase planned to incorporate latency metrics provided by ThousandEyes, surfacing network performance data within search results to help enterprises make more informed shortlisting decisions — not just based on which providers are available, but on how well their networks actually perform to the relevant Google Cloud nodes.

Broader Context: Streamlining Telecom Procurement

Cloudscene CEO Belle Lajoie positioned Cloud Pathfinder as the first phase of a wider procurement vision:

“Cloudscene’s Cloud Pathfinder app is the first phase of our wider procurement solution that will streamline the way the telecommunications industry communicates and transacts. Cloudscene’s goal is to revolutionize the way enterprises buy cloud and connectivity services and providing the industry with trusted data is critical to this strategy.”

Belle Lajoie, CEO, Cloudscene

For DevOps and infrastructure teams, tools like Cloud Pathfinder matter because network connectivity decisions — particularly around dedicated interconnects and private peering — directly affect latency, reliability, and egress cost for cloud workloads. Having a neutral, searchable database of provider options reduces the research time typically involved in evaluating cloud networking paths.

Key Takeaways

• Cloud Pathfinder is a Cloudscene app that maps Google Cloud connectivity options from any address or data center globally.
• Data is sourced from 6,500+ service providers and is kept current by providers managing their own listings.
• Enterprises can discover, shortlist, and contact connectivity partners directly from the app.
• A planned second phase will add ThousandEyes latency data to search results.
• Service providers should claim and update their Cloudscene profiles to appear in results.

What Is DevOps Intelligence?

Most DevOps transformations focus on tooling and process: CI/CD pipelines, infrastructure-as-code, automated testing. But a critical next step is often overlooked—using the data those pipelines generate to make smarter decisions. That practice is called DevOps Intelligence.

DevOps Intelligence means collecting, analyzing, and acting on data from across your entire software delivery lifecycle—from code commit to production deployment. The goal is to deliver software more efficiently, with less risk, and with measurable business value. As both the demand for faster software and the complexity of modern applications continue to grow, building intelligence into your delivery process is no longer optional—it is a competitive requirement.

Below are seven concrete benefits of making DevOps Intelligence a priority.

1. Faster Release Cycles

End-to-end visibility into your delivery pipeline makes it possible to identify and eliminate waste. With real-time metrics, teams can pinpoint bottlenecks—whether that is a slow test suite, a manual approval gate, or an environment that is consistently unavailable—and take targeted action to remove them.

Key metrics to track include:

• Deployment frequency – how often you ship to production
• Lead time for changes – from commit to live deployment
• Deployment success rate – the percentage of deployments that complete without rollback
• Cycle time per team – where handoffs are slowing overall throughput

When this data is visible in real time, teams stop guessing and start optimizing with evidence.

2. Higher Quality Software

DevOps Intelligence enables tighter, faster feedback loops—the foundation of iterative development. When teams can see immediately how a change affects test pass rates, error budgets, or user-facing error rates, they can course-correct before problems compound.

Feedback loops are especially valuable for experiments: A/B tests on new UI patterns, feature flags, or canary deployments. Because failures are fast, small, and contained, the cost of learning is low and the speed of improvement is high. DevOps Intelligence provides the signal that tells you whether an experiment succeeded or should be rolled back.

3. Increased Business Value of Software

Shipping faster is only valuable if what you ship matters to customers. DevOps Intelligence closes the loop between delivery and outcome by surfacing data such as:

• Which features customers actually use
• Which flows they abandon
• How behavior changes after a release

This post-release intelligence supports impact analysis—did the work you delivered actually move the needle for users? Those answers feed directly into prioritization decisions for the next iteration, reducing the risk of building the wrong thing.

4. Greater Transparency Across the Organization

A common friction point between IT and the rest of the business is the perceived black box of software delivery. Stakeholders ask when a feature will ship; engineers struggle to give a reliable answer. DevOps Intelligence replaces that uncertainty with shared, real-time dashboards that anyone in the organization can read.

This transparency has practical benefits:

• It is easier to justify requests for additional time or resources when you can point to data.
• Release dates can be driven by measurable readiness criteria rather than arbitrary calendar commitments.
• Non-technical stakeholders can track progress independently, reducing the overhead of status meetings and one-off requests to IT.

5. Proactive and Predictive Delivery Management

Reactive management—fixing problems after they appear in production—is expensive and stressful. DevOps Intelligence shifts the posture toward proactive management by combining real-time monitoring with historical trend analysis.

Real-time signals provide early warnings: a deployment that is taking longer than usual, a spike in test failures after a particular commit, or an environment showing resource pressure before it causes an outage.

Historical data enables prediction: which types of changes historically correlate with incidents, how long certain release tasks typically take, and where capacity constraints tend to emerge at scale. Together, these inputs support smarter planning and fewer production surprises.

6. Simplified Compliance and Audit Readiness

In regulated industries—finance, healthcare, government—audit readiness is non-negotiable. DevOps Intelligence automatically captures an auditable record of your delivery process: who did what, when, whether defined processes were followed, what failed, and what remediation steps were taken.

Instead of reconstructing this trail manually from scattered logs and emails during an audit, teams can query their delivery data directly. This reduces compliance overhead and provides early warning when process gaps might put compliance status at risk.

7. A Stronger, More Empowered DevOps Culture

Culture change is harder to measure than pipeline metrics, but DevOps Intelligence contributes to it in a concrete way. When data about delivery performance is visible across the organization, it does several things:

• It surfaces team and individual contributions that might otherwise go unrecognized.
• It gives engineers objective evidence to support process improvement proposals.
• It invites people outside IT—product managers, support teams, business analysts—to participate meaningfully in decisions about how software is built and shipped.

Shared data creates shared ownership. That is a prerequisite for a genuinely collaborative DevOps culture rather than a superficial one.

Building the Infrastructure for DevOps Intelligence

None of these benefits are automatic. Realizing them requires building the infrastructure to capture, centralize, and analyze data from across your toolchain—version control, CI servers, test frameworks, deployment platforms, monitoring systems, and ticketing tools.

As organizations mature their DevOps practices and adopt release orchestration platforms, they gain the ability to aggregate this data automatically and surface it in actionable dashboards. The result is a delivery organization that can measure its own performance, improve continuously, and demonstrate its value to the broader business in terms that non-technical stakeholders understand.

DevOps Intelligence is not a single tool. It is a discipline—and investing in it is what separates teams that ship fast from teams that ship well.