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In software engineering, there is a direct correlation between team performance and building robust, stable applications. The data community aims to adopt the rigorous engineering principles commonly used in software development into their own practices, which includes systematic approaches to design, development, testing, and maintenance. This requires carefully combining applications and metrics to provide complete awareness, accuracy, and control. It means evaluating all aspects of a teams performance, with a focus on continuous improvement, and it applies just as much to mainframe as it does to distributed and cloud environmentsmaybe more.

This is achieved through practices like infrastructure as code (IaC) for deployments, automated testing, application observability, and complete application lifecycle ownership. Through years of research, the DevOps Research and Assessment (DORA) team has identified four key metrics that indicate the performance of a software development team:

These metrics provide a quantitative way to measure the effectiveness and efficiency of DevOps practices. Although much of the focus around analysis of DevOps is on distributed and cloud technologies, the mainframe still maintains a unique and powerful position, and it can use the DORA 4 metrics to further its reputation as the engine of commerce.

This blog post discusses how BMC Software addedAWS Generative AIcapabilities to its productBMC AMI zAdviser Enterprise. The zAdviser usesAmazon Bedrockto provide summarization, analysis, and recommendations for improvement based on the DORA metrics data.

Tracking DORA 4 metrics means putting the numbers together and placing them on a dashboard. However, measuring productivity is essentially measuring the performance of individuals, which can make them feel scrutinized. This situation might necessitate a shift in organizational culture to focus on collective achievements and emphasize that automation tools enhance the developer experience.

Its also vital to avoid focusing on irrelevant metrics or excessively tracking data. The essence of DORA metrics is to distill information into a core set of key performance indicators (KPIs) for evaluation. Mean time to restore (MTTR) is often the simplest KPI to trackmost organizations use tools like BMC Helix ITSM or others that record events and issue tracking.

Capturing lead time for changes and change failure rate can be more challenging, especially on mainframes. Lead time for changes and change failure rate KPIs aggregate data from code commits, log files, and automated test results. Using a Git-based SCM pulls these insight together seamlessly. Mainframe teams using BMCs Git-based DevOps platform, AMI DevX ,can collect this data as easily as distributed teams can.

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

BMC AMI zAdviser Enterprise provides a wide range of DevOps KPIs to optimize mainframe development and enable teams to proactvely identify and resolve issues. Using machine learning, AMI zAdviser monitors mainframe build, test and deploy functions across DevOps tool chains and then offers AI-led recommendations for continuous improvement. In addition to capturing and reporting on development KPIs, zAdviser captures data on how the BMC DevX products are adopted and used. This includes the number of programs that were debugged, the outcome of testing efforts using the DevX testing tools, and many other data points. These additional data points can provide deeper insight into the development KPIs, including the DORA metrics, and may be used in future generative AI efforts with Amazon Bedrock.

The following architecture diagram shows the final implementation of zAdviser Enterprise utilizing generative AI to provide summarization, analysis, and recommendations for improvement based on the DORA metrics KPI data.

The solution workflow includes the following steps:

The following screenshot shows the LLM summarization of DORA metrics generated using Amazon Bedrock and sent as an email to the customer, with a PDF attachment that contains the DORA metrics KPI dashboard report by zAdviser.

In this solution, you dont need to worry about your data being exposed on the internet when sent to an AI client. The API call to Amazon Bedrock doesnt contain any personally identifiable information (PII) or any data that could identify a customer. The only data transmitted consists of numerical values in the form of the DORA metric KPIs and instructions for the generative AIs operations. Importantly, the generative AI client does not retain, learn from, or cache this data.

The zAdviser engineering team was successful in rapidly implementing this feature within a short time span. The rapid progress was facilitated by zAdvisers substantial investment in AWS services and, importantly, the ease of using Amazon Bedrock via API calls. This underscores the transformative power of generative AI technology embodied in the Amazon Bedrock API. This API, equipped with the industry-specific knowledge repository zAdviser Enterprise and customized with continuously collected organization-specific DevOps metrics, demonstrates the potential of AI in this field.

Generative AI has the potential to lower the barrier to entry to build AI-driven organizations. Large language models (LLMs) in particular can bring tremendous value to enterprises seeking to explore and use unstructured data. Beyond chatbots, LLMs can be used in a variety of tasks, such as classification, editing, and summarization.

This post discussed the transformational impact of generative AI technology in the form of Amazon Bedrock APIs equipped with the industry-specific knowledge that BMC zAdviser possesses, tailored with organization-specific DevOps metrics collected on an ongoing basis.

Check out the BMC website to learn more and set up a demo.

Sunil Bemarkar is a Sr. Partner Solutions Architect at Amazon Web Services. He works withvarious Independent Software Vendors (ISVs)and Strategic customers across industries to accelerate their digital transformation journeyand cloud adoption.

Vij Balakrishna is a Senior Partner Development manager at Amazon Web Services. She helps independent software vendors (ISVs) across industries to accelerate their digital transformation journey.

Spencer Hallman is the Lead Product Manager for the BMC AMI zAdviser Enterprise. Previously, he was the Product Manager for BMC AMI Strobe and BMC AMI Ops Automation for Batch Thruput. Prior to Product Management, Spencer was the Subject Matter Expert for Mainframe Performance. His diverse experience over the years has also included programming on multiple platforms and languages as well as working in the Operations Research field. He has a Master of Business Administration with a concentration in Operations Research from Temple University and a Bachelor of Science in Computer Science from the University of Vermont. He lives in Devon, PA and when hes not attending virtual meetings, enjoys walking his dogs, riding his bike and spending time with his family.

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Achieve DevOps maturity with BMC AMI zAdviser Enterprise and Amazon Bedrock | Amazon Web Services - AWS Blog

Participants showcased their talent in machine learning, code generation, and problem-solving, guided by Ask AT&T. The team used Ask AT&T to research industry trends, draft business plans, conduct SWOT analysis, and design PowerPoint templates.

By the end of the competition, Ask AT&T emerged as an indispensable tool for everyday work. Although AI tools like Ask AT&T have potential for improvement, their immense potential was recognized. As AI continues to develop, it will revolutionize our work processes, increasing efficiency and allowing more time for complex tasks. This aligns with our focus on improving internal processes at AT&T.

The TDP's AI Learning & Problem-Solving Challenge was an inclusive event, involving around 700 employees from the corporate systems organization. The competition comprised 16 teams and over 70 participants, from new hires to veterans.

The most innovative teams proposed diverse learning and training tools. Several leaders evaluated the final four contenders, with PLEdge of Progress emerging as the winners. Some of the winning solutions are in the backlog for development.

Participants expressed that AI tools like Ask AT&T, when used effectively, can significantly enhance efficiency and productivity.

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Ask AT&T: Revolutionizing Efficiency and Creativity with AI - AT&T Newsroom

What is a model card in machine learning?

A model card is a type of documentation that is created for, and provided with, machine learning models. A model card functions as a type of data sheet, similar in principle to the consumer safety labels, food nutritional labels, a material safety data sheet or product spec sheets.

There has been a dramatic rise in the development and adoption of machine learning (ML) and artificial intelligence (AI) during recent years. Further advances in generative AI employ large language models (LLMs) as a core component. However, the many models used in those platforms are increasingly complex and difficult to understand. Even model developers sometimes struggle to fully understand and describe the ways a given model behaves. This complexity has created serious questions about core business values such as transparency, ethics and accountability. Common questions include the following:

First proposed by Google in 2018, the model card is a means of documenting vital elements of a ML model so users -- including AI designers, business leaders and ML end users -- can readily understand the intended use cases, characteristics, behaviors, ethical considerations, and the biases and limitations of a particular ML model.

As late as 2024, there are no current legislative or regulatory requirements to produce or provide model card documentation with ML models. Similarly, there are no currently established standards in model card format or content. However, major ML developers have spearheaded the adoption of model card documentation as a way of demonstrating responsible AI development, and adopters can find model cards for major platforms such as Meta Llama, Google face detection and OpenAI GPT-3.

The rise of ML and AI is driving the need for transparency and responsible governance. Businesses must understand what ML models are for, how they work, how they compare to other competitive models, how they're trained and their suitability for intended tasks.

Model cards are a tool that can address such concerns which readily impact governance and regulatory issues for the business. Model cards can provide a range of important benefits to ML and AI projects including the following:

Labels and other informational summaries are generally most effective when they allow comparing similar products side by side using comparable content and formats. However, the information presented on an ML model card can vary. Unlike highly regulated informational displays -- such as food nutritional labeling -- there are no current standards to govern the information or formatting included on ML model cards.

ML models can vary dramatically in their scope, purpose and capabilities, so this makes it hard to regulate. For example, an ML model developed to aid in medical diagnosis can be distinctly different than an ML model created to run analytics on retail sales operations, or a complex LLM used in an AI construct. Consequently, ML model developers largely use their own discretion to determine what information to include, and how that information should be presented. Yet, as leading technology firms develop ML/AI platforms and document those offerings through model cards, some de facto documentation standards are taking shape. Model cards should include the following:

This first section of a model card is typically the introduction to the model which can outline the model's essential details including the model's name, version, revision list, a brief general description of the model, business or developer details and contact information, and licensing details or limits.

This section describes the intended uses, use cases and users for the model. For example, a section on use cases may describe uses in object detection, facial detection or medical diagnoses. This section may also include caveats, use limitations or uses deemed out of scope. For example, a model intended for object detection may detail input from photos or video; output including detection of a specified number of object classes; and other output data such as object bounding box coordinates, knowledge graph ID, object description and confidence score.

This section describes the overall design of the model and any underlying hardware back end that runs the model and hosts related data. Readers can refer to the model card to understand the design elements or underlying technologies that make the model work. For the object detection model example, the model card may describe an architecture including a single image detector model with a Resnet 101 backbone and a feature pyramid network feature map.

This section outlines, describes or summarizes the data used in model training; where and when the data was obtained; and any statistical distribution of key factors in the data which may allow for inadvertent bias. Since training data may be proprietary to the model's developers, training details may be deliberately limited or protected by a separate confidentiality agreement. Training details may also describe training methodologies employed with the model.

This section outlines details related to the model's performance measured against a test data set, not a training data set, as well as details about the test data set itself. For the object detection model example, performance metrics included on the model card may note the use of both Google's internal image data set as well as an open source image set as test data and the number of object classes the model can detect in each data set. Additionally, performance details may outline reported metrics including the precision and accuracy of the object detection. More sophisticated models may utilize other detailed metrics to measure performance.

A key segment of any model card is the section describing limitations, possible biases or variable factors that might affect the model's performance or output. For the object detection model example, known limitations may include factors such as object size, clutter, lighting, blur, resolution and object type since the model can't recognize everything.

This final segment of a model card is often dedicated to business-related details including information about the model's developers, detailed contact, support and licensing information, fairness/privacy and usage information, suggestions for model monitoring, any relevant assessment of impacts to individuals or society, and other ethical or potential legal concerns related to the model's usage.

As leading technology organizations build ML and AI platforms, their work on model cards and other documentation has provided a standard for other ML firms to follow. Today there are many examples of ML model cards to review including the following major examples:

There are also more standardized tools for model card creation, as well as model card repositories, such as these examples:

Both GitHub and Hugging Face provide a repository of model cards which are available for review and study, offering model card examples across many different model types, purposes and industry segments.

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What is a model card in machine learning and what is its purpose? - TechTarget

In an exclusive interviewwith Asia Business Outlook, BN Shukla, Operations Director, Jabil, India, shares his views on the challenges of interpreting and understanding the decision-making processes, strategies to optimize the implementation of AI & ML, robust measures to protect sensitive production data, how manufacturers ensure compliance with industry standards and regulations and more. BN Shukla, Operations Director for Jabil in India, has a career spanning more than 28 years with a specialization in operational excellence and business management.

Considering the complexity of AI and machine learning algorithms, especially within critical manufacturing processes, how can manufacturers navigate the challenge of interpreting and understanding the decision-making processes of these systems?

Some of the common challenges in increasing the adoption of digital technology in manufacturing include:

Capital investment: Varying degrees of costs from IoT sensors used on existing machines to purchasing large machinery with integrated machine learning solutionsto enterprise-wide infrastructure adaptations, particularly in large-scale projects.

Effective change management: AI/ML is changing the way we do things as we merge the physical and digital together. Strategies must be accompanied by a support structure for employees, empowering them with the right tools and skills, thus creating a culture ripe for a successful transition.

Technical skill gaps: Fuelled by digitalization, the roles and expectations of the workforce on and off the shop floor are evolving. Talents that have digital dexterity and are ready to adapt and innovate in manufacturing processes and adopt digital tools that support those processes will successfully implement new technology and maintain operations.

Data growth, sensitivity, and security: The physical and digital systems in smart factories make real-time interoperability possible. While large volumes of data are generated, challenges remain in data quality and management for decision-making, looming concerns over data and IP privacy, ownership, governance, and an increased risk of an expanded attack surface as numerous machines and devices are connected to networks.

To ensure the quality and reliability of the data used to train and optimize these systems, we have put in place several guard rails:

Datafication: We progress from digitization to digitalization to datafication, where we investigate business processes and transform the process into quantifiable data to track, monitor, and analyze. To do this effectively, we have set up an enterprise-wide Data & AI council, which involves senior members from all the functions to help identify key processes critical to the business and have the process owners work on critical data definitions, data lineage, and data sources. Although this is not technology-specific, it helps to set a good foundation for the organization moving forward. Teams across Jabil are learning how to use data effectively to enable Data to speak, data to act.

AI/ML: Collected data that is not used effectively is a waste of resources. We leverage AI/ML/deep learning to extract value out of the swarm of data we collect each day from our factories and work processes to help deliver business insights, automate tasks, and advance system capabilities. Our AI/ML strategy spans from using AI algorithms to improving our inspection process in the factories. By using advanced data analytics to derive algorithms or new business models, we can gain new insights and intelligence for our business. Were also developing our knowledge database and combining it with Generative AI technology to merge the insights from our self-healing manufacturing line (ready in April 2024) with the know-how of our workforce to continuously train our AI models and guide our technicians to take action.

SAP S4: When Jabil migrated from SAP ECC to SAP S4 Hana in January 2022, we were able to offload the technical debt that came from 20-plus years of over-customization and subsystems that were peripheral to the legacy SAP system. The Hana database also brought about greater speed in data processing and a simplified data structure. Nevertheless, the benefits of SAP S4 migration should not be just about solving technical issues but about bringing new value for the users through new ways of report creation, enhanced user experience, increased productivity, and the ability to leverage new functionality to transform the processes. We are still in the continuous improvement process to better leverage these functionalities and are bringing the users along in the transformation journey.

Automation: Process automation through the use of robotic process automation (RPA) tools has helped us automate many back-end office and repetitive tasks in various functions. Many functional teams who are using the RPA bots also try to humanize the bots and treat them as part of the (digital) workforce, measuring the performance of these bots to ensure we obtain the maximum ROI.

To navigate the challenge of interpreting the complexity in the decision-making process, it is crucial that organizations first have a clear strategy of how they plan to leverage AI/ML in the company. At Jabil, we took a customer-first approach, and we deliberated from the onset. How we leverage AI/ML is about solving a business problem or providing deep insights to realize a step-change improvement in safety, quality, delivery, and cost.

Interpreting and understanding such systems, communications, and engagement with stakeholders is critical to ensure we are working on what matters to the business most.

In light of the challenge of ensuring scalability and adaptability across diverse manufacturing operations, what strategies can manufacturers employ to optimize the implementation of AI and machine learning solutions?

Once we knew what our North Star looked like, we were able to clearly break down obstacles within the People, Process, and Systems categories and develop solutions to take us to the next level. Some of these were:

Focus on people at the heart of transformations by taking an employee-first digitalization approach. Many people are familiar with the saying, AI will not replace people, but the people who can use it will. With that in mind, the human factor is a major lever for transitioning and tapping into opportunities that come with AI/ML.

Our industry-certified internal courses, in partnership with industry experts and local universities, have allowed us to grow our pool of subject matter experts by ensuring that technological know-how is retained and expanded through customized application-based upskilling. Additionally, as engineers and technicians take business-related modules, they promote diversity in the workplace in the form of business differentiation and innovative decision-making.

Enhance industry ecosystem through public-private initiatives: In many of our locations, we partner with leading equipment providers and government agencies to build a strong manufacturing ecosystem. We must continue to actively partner with academia to create the next generation of talented professionals.

Amidst concerns regarding workforce reskilling and upskilling, how can manufacturers effectively foster collaboration between human workers and intelligent machines in manufacturing processes?

There are seismic shifts with the convergence of technologies across operations, information technology (IT), and supply chains, creating a data-driven environment that enables us to deliver the future of Jabils manufacturing.

We need to embrace a work environment that is expected to blend advanced technology and digital skills with uniquely human skills to yield the highest level of productivity. The rise of advanced technology can replace the manual or repetitive tasks many jobs entail. This frees up space for skills that are uniquely and essentially human, or so-called soft skills, including critical thinking, people management, creativity, and effective communication. Companies need workers who can exhibit these skills, as well as digital skills, to work alongside robots and technologies.

The broader aim of digital transformation is not just to eliminate tasks and cut costs but to create value, safer workplaces, and meaningful work for people. Industry leaders need to put humans in the loop when preparing their workforce through rethinking work structure, retraining and reskilling talents, and structuring the organization to leverage technology and transform its business.

Theres no one-size-fits-all answer to this other than saying that digitalization, digital transformations, and digital readiness of ones operations and workforce are imperative to remain relevant and competitive in the long term.

We believe that the best way to make our organization more data-centric and digital is to invest in those who are adaptable, curious, and flexible. We look to our existing and future talents with the logic that digital transformation is changing everyones role, from the factory floor to our executives.

This marriage of multi-generational talents not only propels the industry forward but also heralds change within the industry itself, making manufacturing a destination for innovative jobs and being the continued change maker in Indias socio-economic landscape.

Given the cybersecurity risks associated with the adoption of AI and machine learning technologies in manufacturing, how can manufacturers implement robust measures to protect sensitive production data and mitigate potential cyber threats and data breaches?

At Jabil, we take cybersecurity seriously. Through our industry expertise and enterprise education and awareness efforts, we are building a data protection culture at Jabil, where our employees are empowered, and our customers and partners are confident in our ability to conduct business safely in todays evolving digital world.

Jabil delivers a three-pronged risk management methodology as part of our Defense-in-Depth approach:

This layered system provides several levels of protection for data, does not rely on any single tool or policy, and enables redundancy in our systems and processes.

We manage digital security guided by the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF), coupled with best-of-class solutions for data protection, threat detection and continuous monitoring within Jabils Security Operations Center (SOC).

Backed by policies and procedures to ensure enterprise resiliency, we provide robust and holistic risk-based guidance and high-quality shared cybersecurity services and solutions. To ensure that our capabilities are always relevant and updated, we use a leading third-party assessor to rate our security program as a whole and conduct penetration tests (PenTests) to monitor compliance with policy and identify gaps for remediation.

We have also built an effective security program that centers around technical controls and empowering our people to be our best line of defense by equipping them with top-tier cybersecurity education and awareness programs that provide them with the information they need to stay safe online in their personal and professional lives.

A great example is educating our employees before they click and providing best practices and tools to analyze site URLs after an individual click. We ensure our readiness to act if cyberattacks breach our defenses through continuous improvement programs such as tabletop exercises, a ransomware playbook, and incident response.

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The impact of AI and machine learning technology in revolutionizing manufacturing practices - Asia Business Outlook

Theres a new startup in San Francisco, and theyre into marketing. Alone, that wouldnt be so interesting, but this company has made its business about algorithmic marketing. This is 2006, in the days when Facebook was still new and generative artificial intelligence was a conversation among science fiction fans. But this startup is sure that machine learning is the future.

The startup secures a major client, a local tourist board, and is eager to turn this into an early win. So they put their heads together and burned the midnight oil. Why would you come to San Francisco? Who should they target, and how? The team came up with the old, familiar answers: Alcatraz, Golden Gate Bridge, and wine country. But then they turned to their algorithm to measure the decision-making behaviors of potential San Francisco visitors.

Their systems buzzed and whirred and they came back with dentistry. At first, the CEO thought it was a mistake. Dentistry? San Franciscans have nice teeth, no doubt, but they arent famous for them. Then the team realized something: the annual Dentistry Association conference was about to be held in San Francisco. No one had known. No one had even thought of that. But the machine did. The team whipped up a presentation for their client, and everyone thought it was great.

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Thats the true story of one of Quantcasts earliest successes. Today, Quantcast is one of the biggest AI-driven advertising technology companies in the world. They have offices in ten countries around the world with hundreds of employees. They were one of the few to recognize the power of AI before the rest of the world did. Last month, Big Think talked with Quantcast founder and CEO, Konrad Feldman, to make sense of how AI has changed marketing and will continue to revolutionize business.

Almost everyone in the world uses some kind of bank. There are billions of transactions occurring every second, and each of those transactions represents a behavior. A boy is buying a chocolate bar from a vending machine. A pensioner is moving money between pension accounts. A young couple is putting down a deposit on a house. A businessman is giving a generous tip. Money moves, and it tells us a story. We can often tell more about peoples behavior from their spending habits than from any self-recorded account.

The problem, then, is how to make sense of those stories. How can anyone hope to read the habits of billions of microtransactions? I started my career in research in neural networks at UCL, Feldman tells Big Think. I started a business using machine learning techniques to find patterns predominantly in the financial service industry. I ended up working in lots of different business domains where it was really hard for people to interpret all of the data and find the patterns of activity that they needed to address particular business problems. We ended up building a lot of systems in the banking sector to help the big banks detect things like money laundering and terrorist financing.

Having fine-tuned various algorithms in the financial sector, Feldman looked to move into the online space; Quantcast was focused on dot-com entrepreneurs just starting out. But now there was a problem. In the days before cloud computing and the internet of everything, the internet was a web of tiny pockets and gated communities. Quantcast Measure is the product we launched in 2006 to help websites of any size, explains Feldman. It was a classic sort of disruptive innovation initially. These algorithms have the ability to provide much broader visibility to better understand audiences. Over time, larger and larger sites realized the richness of audience information they could derive, and participate in machine learning. That was the first use of Quantcast Measure.

Marketers need data. But everyone has their own data sets, often very small. Quantcast offered the opportunity to combine them and analyze them. We had to solve some really thorny technical issues, says Feldman, both in terms of the sort of data processing architectures we needed and because the data volumes involved are just absolutely immense.

Its often misunderstood that machine learning and AI are some kind of data-processing panacea. A computer can do billions of calculations per nanosecond, so surely they can make light work of all that data? But an algorithm is only as good as the humans who engineer it. As Feldman puts it, A lot of people, when they think about AI or machine learning, think of it as some sort of holistic algorithm that does everything. In fact, even though we have algorithms now in large language models (LLMs) that can actually do a number of different things, fundamentally, most specific problems require specific algorithms and specific tuning and application of those algorithms.

The various explosions of GPTs are a good example of this. OpenAI has produced one of the most powerful AIs in the history of civilization, but after a few months, its usage slipped, and people started to see it as a time waste. Then, at the end of 2023, OpenAI introduced custom-made GPTs. Essentially, anyone could fine-tune and create an algorithm of their own using OpenAIs LLMs. Users started to craft prompts in an algorithmic style: Imagine youre a movie critic, In the style of a romantic poet, You are Albert Einstein, Explain like Im five years old, and so on. This is an example of the fine-tuning Feldman talks about to turn machine learning into something practical because machine learning varies.

We believe it will be possible to build machine learning models that could actually infer audience characteristics.

The machine learning we use to measure audiences is different from the machine learning were using to build predictive audience models for advertising delivery, explains Feldman. Its different from the machine learning thats been used to ensure brand safety or to price individual impressions. Weve developed different types of machine learning approaches to solve some of these fundamental problems. Quantcasts marketing algorithms will capture information in real time [and] then make adjustments as they go along. They are interactive tools that will optimize for some specified goal as theyre working.

The clear implication here is that the next stage of AI will be more flexible, fine-grained and fine-tuned. For AI to become really useful and for the conversation to become really exciting AI has to become personalized AI.

We believe it will be possible to build machine learning models that could actually infer audience characteristics, says Feldman. Humans are really good [at] pattern matching thats how weve evolved. But machines also have flexibility because they can deal with so many dimensions of data. If youre going adjust and tailor the content, if youre going to modify content for each individual recipient, well then, people cant do that. You have to use a computer to do it.

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Why the CEO of Quantcast is betting on "personalized AI" - Big Think