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.

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.