Latest news with #ReliabilityEngineering
Business Upturn
30-06-2025
- Business
- Business Upturn
Intellias strengthens cloud transformation capabilities as one of only 19 Google Cloud DevOps specialists worldwide
CHICAGO, June 30, 2025 (GLOBE NEWSWIRE) — Intellias, a global software engineering and digital consulting company, today announced that it has earned the highly coveted Google Cloud DevOps Specialization. Fewer than one percent of all Google Cloud partners—just 19 out of more than 2,300 worldwide—hold this distinction, underscoring the firm's leadership in cloud-native engineering and DevOps delivery. Becoming a Specialized partner elevates the company's status within the Google Cloud Partner Advantage Program, providing the business with a significant endorsement for the work it does across North America, Latin America, Europe, the Middle East, Africa, Japan and Asia-Pacific. For Intellias, the recognition affirms a long-term strategy of investing in advanced engineering talent, rigorous best practices, and deep Google Cloud expertise. 'Achieving the DevOps Specialization proves we can translate advanced engineering into real business value,' said Regina Viadro, SVP Global Head of Digital Technology Services and President, North America at Intellias. 'Our clients trust us to modernize critical infrastructure and reduce time to impact—and this credential validates that trust.' Dmytro Vedetskyi, Head of Cloud and DevOps at Intellias, added: 'Earning the Google Cloud DevOps Specialization is a significant achievement that showcases our team's extensive technical expertise and demonstrated ability to deliver impactful results for clients. This recognition is more than just a successful audit — it stands as a testament to Google Cloud's trust in us as a strategic partner. It underscores our ongoing commitment to innovation, excellence, and the strength of our technology-driven professional team.' The Google Cloud DevOps Specialization is the program's highest technical credential. To qualify, partners must pass an independent technical assessment, present verified customer success stories, and maintain a team of certified engineers. What this means for clients With the specialization in place, Intellias clients can expect: Faster time-to-market: Automated CI/CD pipelines that shorten release cycles and speed new-feature delivery. Higher reliability: Cloud-native architectures and Site Reliability Engineering (SRE) practices that improve uptime and performance. Lower operational overhead: Infrastructure-as-code and automated provisioning that cut manual effort and reduce costs. Future-proof scalability: Modern DevOps toolchains built on Google Cloud that grow seamlessly with business demand. Intellias will continue to expand its DevOps and cloud services portfolio, helping organizations re-architect legacy systems, adopt cloud-first strategies, and innovate at startup speed, all while maintaining enterprise-grade security and governance. Notes to editors About Intellias Intellias is a global software engineering and digital consulting company. Operating as a trusted technology partner to top-tier organizations, the firm helps companies operating in North America, Europe, and the Middle East accelerate their pace of sustainable digitalization and embrace innovation at scale. For more than 20 years, Intellias has been building mission-critical projects and delivering measurable outcomes to ensure lasting change for its clients, such as HERE Technologies, TomTom, ZEEKR, HelloFresh, and Travis Perkins. Olha Kolomiichuk – [email protected] Disclaimer: The above press release comes to you under an arrangement with GlobeNewswire. Business Upturn takes no editorial responsibility for the same. Ahmedabad Plane Crash
Entrepreneur
13-06-2025
- Business
- Entrepreneur
Streaming Without Compromise: Head of Reliability Engineering on SRE, Microservices, and Scalable Architecture
Alexandr Hacicheant, Head of Reliability Engineering, ensures system stability and fault tolerance. He implements practices that minimize risks, let developers sleep peacefully at night, and simultaneously optimize business resources. Opinions expressed by Entrepreneur contributors are their own. You're reading Entrepreneur India, an international franchise of Entrepreneur Media. Mayflower is a global FunTech company taking the entertainment industry to the next level. Its flagship product is a live-streaming platform. Mayflower's CDN processes over 10,000 parallel input streams and distributes approximately 100,000 output streams. Downtime is unacceptable—every delay means losing users. Alexandr Hacicheant, Head of Reliability Engineering, ensures system stability and fault tolerance. He implements practices that minimize risks, let developers sleep peacefully at night, and simultaneously optimize business resources. "My job is to ensure the system doesn't just work—it must withstand peak loads and recover quickly from failures," he explains. Alexandr shares his career journey, key projects, and best practices—SRE, microservices, and minimizing latency in live streaming. From Developer to CTO Before joining Mayflower, Alexandr worked remotely for several years with Russian and international companies as a backend developer. He specialized in solving critical issues—whether implementing urgent features or fixing system failures. "For example, when promo campaigns caused a surge in users, I had to ensure services could handle the traffic spike," he recalls. In 2016, he moved to Cyprus and joined Mayflower. Starting as an engineer on a 15-person team developing and testing new features, he shifted focus to architecture optimization and bottleneck elimination as scaling challenges emerged. "I looked for ways to scale not by buying more servers but by improving our tech stack's efficiency," he says. One of his initiatives was dedicating 30% of team time to technical debt. This improved system stability, reduced incidents, and enhanced engineers' work-life balance. "Before, employees often woke up at night to fix issues. We started addressing root causes—not just symptoms." After several years, Hacicheant became CTO, overseeing technical growth: leading tech leads, coordinating backend/client development, ML teams, and DevOps. Under his guidance, security improved—including forming a dedicated infosec team (previously handled by infrastructure teams). Alexandr and his team implemented automated vulnerability detection pipelines (SAST and SCA solutions) to scan project source code before production deployment, while establishing streamlined remediation processes. Additionally, they deployed a centralized access management system for company resources. Furthermore, Hacicheant spearheaded a company-wide security awareness initiative through interactive training sessions and meetups. Under Alexandr's leadership, the development and operations teams also dedicated significant efforts to building a cloud platform and migrating applications to cloud infrastructure. This transition delivered substantial advantages in computational resource management and allocation, automated scaling and failure recovery, along with accelerated application and service deployment speeds compared to traditional physical server or virtual machine environments. Resilience at the Architectural Level After approximately three years as CTO, the expert took over leadership of Reliability Engineering. Currently, Alexandr's primary goal is to ensure service fault tolerance while establishing robust failure recovery and analysis processes. "Ideally, outages shouldn't occur. But when they do, we need to identify the issue and recover quickly," he explains. System failures can stem from various causes, often due to suboptimal code or hastily chosen architectures. Alexandr's team identifies failure root causes during profiling and analysis, documents best practices, shares them company-wide, and automates detection of similar future issues. For example, they've implemented load-testing pipelines to evaluate code performance under multi-user loads and assess service readiness for peak traffic. Under Alexandr's guidance, the team established a three-tier technical support system: - First line: 24/7 monitoring team - Second line: SRE team comprising developers and DevOps engineers for specific services - Third line: Team leads and technical leads with broad expertise "Initially, incidents frequently escalated to the third line. But as the first and second lines gained experience—writing postmortems (documents detailing timelines, conclusions, and preventive measures) and action items—escalations dropped dramatically," Hacicheant emphasizes. Collectively, these innovations reduced major incidents from weekly occurrences to no more than monthly. A current priority for Alexandr is decomposing the monolith into microservices. Monolithic architecture—a single, tightly integrated system—simplifies development but severely hinders scaling and partial updates. Microservices, conversely, break applications into independent modules, each handling specific functions and deployable separately, communicating via APIs. "Monoliths work initially when validating hypotheses quickly. But they eventually impede scaling, and a single failure can disrupt half your business processes," Alexandr explains. For this transition, he oversees technology selection and architectural decisions. The expert emphasizes that such decisions must always strike a balance between business requirements, technical considerations, and available resources - both human and temporal. Chasing after new technologies and ideas isn't always the optimal approach. "When testing a business hypothesis, it's better to leverage existing solutions and quickly assemble a makeshift system that meets requirements using the tech stack your team already knows. There will be time to refine architectural solutions once the service proves its business value. Otherwise, all resources that could have been invested in validating other hypotheses will be wasted," Hacicheant stresses. According to the expert, one of the key recommendations before implementing microservice architecture is to thoroughly understand the system's business processes. This enables more precise definition of service boundaries and their decomposition. Additionally, services should maintain loose coupling; issues with one service shouldn't cause the entire system to degrade. This can be achieved through approaches like asynchronous communication and Event-Driven Architectures. Simultaneously, Hacicheant is enhancing monitoring and improving service observability so the system can automatically identify where failures occur and alert the appropriate personnel. "The ultimate goal isn't manual log collection, but building an intelligent alert system that can independently diagnose what went wrong and where, then precisely notify the responsible team." SRE: A Systemic Approach to Failures Under Alexandr's leadership, Mayflower adopted Site Reliability Engineering (SRE) practices—an engineering methodology balancing feature velocity with service stability. Core principles acknowledge that failures are inevitable; teams must minimize impact, automate responses, and prevent recurrences. At the core of SRE are three key concepts: SLO (Service Level Objective) — the target levels of service availability or performance; SLI (Service Level Indicator) — the metrics used to measure these targets; and the error budget — the acceptable threshold of failure. If this threshold is exceeded, the system automatically triggers an alert, and the CI/CD pipeline may suspend further deployments. SRE practice includes the preparation of runbooks — step-by-step guides for resolving common issues. After an incident, a postmortem is created. In addition, SRE promotes gradual rollouts: updates are initially delivered to 5–10% of users, and only if the system remains stable are they rolled out more broadly. If issues are detected, tools like Spinnaker can automatically roll back the changes. This approach helps companies reduce the number of outages, accelerate recovery times, and improve user satisfaction. Instead of chaotic late-night firefighting and stress, SRE brings structure, transparency, and predictability. As Alexandr emphasizes, implementing SRE provides not only technical but also cultural benefits: it enhances collaboration across teams and reduces burnout.
International Business Times
22-05-2025
- Health
- International Business Times
Building Trustworthy AI in Healthcare: Why Fairness and Accountability Are No Longer Optional
As artificial intelligence (AI) becomes increasingly embedded into clinical workflows, the healthcare industry is facing a new imperative not just to innovate, but to innovate ethically. From diagnosis and treatment recommendations to hospital resource allocation and insurance eligibility models, AI now plays a role in decisions that affect millions of lives. Yet as adoption rises, so too do concerns around bias, transparency, and regulatory compliance. One of the researchers leading the charge in solving this challenge is Vijaybhasker Pagidoju, a U.S.-based AI infrastructure specialist and healthcare systems engineer with extensive experience designing scalable, reliable, and audit-ready solutions for clinical settings. His recent research, "Fair and Accountable AI in Healthcare: Building Trustworthy Models for Decision-Making and Regulatory Compliance," sheds light on how AI systems can be both technically advanced and ethically responsible without compromising on performance. "Trust in healthcare AI isn't just a technical milestone it's a human requirement," says Vijaybhasker. "The goal is not only to make algorithms smarter, but to make their impact more equitable, explainable, and compliant." The Hidden Risks of "Black Box" AI in Medicine Despite the promise of AI-driven efficiencies in diagnostics and clinical support, many systems still operate as opaque "black boxes." These models may be highly accurate in aggregate, yet produce unequal outcomes across demographic groups a phenomenon that can lead to serious consequences in high-stakes environments like ICU triage or sepsis detection. Vijaybhasker's study evaluated real-world deployments of AI systems in hospitals across six countries, revealing performance disparities in predictive models when applied to women, publicly insured patients, and African American populations. "Bias was often invisible until broken down by race, gender, or insurance type," noted one ML engineer interviewed in the study. By integrating fairness-enhancing techniques such as Federated Learning with adversarial debiasing, the research demonstrated that it's possible to significantly improve fairness metrics (like Equal Opportunity Difference and Demographic Parity) without sacrificing accuracy. In one case, fairness gaps were reduced by over 80% with less than a 1% drop in predictive accuracy. Accountability Through Infrastructure and SRE Principles Beyond bias mitigation, the study makes a compelling case for embedding Site Reliability Engineering (SRE) and MLOps principles into the healthcare AI lifecycle a novel but increasingly necessary fusion. "AI systems need the same robustness, observability, and fault tolerance that we expect from mission-critical infrastructure," says Vijaybhasker, who brings years of real-world experience in AI-driven SRE for U.S. healthcare environments. His work outlines how practices like drift detection, incident logging, and real-time monitoring staples in modern SRE can be used not just to improve uptime, but to ensure regulatory traceability and ethical accountability. In fact, institutions that adopted such practices showed 40% faster response times to model failures, and were better prepared for external audits by organizations like the FDA and NHS. Clinical Trust Through Explainability A key insight from the study is that statistical fairness alone is not enough. Clinicians surveyed overwhelmingly said they were more likely to use and trust an AI system when they could understand how and why a decision was made. Tools like SHAP and LIME were integrated into dashboards, improving transparency and increasing clinician willingness to rely on AI by over 25%. This speaks to the growing importance of explainability not as an academic goal, but as a clinical necessity. When doctors are expected to justify treatment decisions, AI can't remain an untouchable black box. It must become an accountable partner. The Road Ahead: Ethical AI as Infrastructure The study concludes with a call for governance models, interdisciplinary collaboration, and continuous validation pipelines to be standard components of AI deployments in healthcare. Only 6 of the 15 institutions studied had formal AI ethics boards or compliance oversight processes in place yet those who did reported higher stakeholder trust and smoother regulatory outcomes. "Fair and accountable AI is not a one-time deliverable," Vijaybhasker emphasizes. "It's an infrastructure challenge a cultural, ethical, and operational shift." As AI continues to shape the future of healthcare, voices like his are setting the tone for what responsible innovation should look like. Trustworthy AI isn't just about better predictions; it's about building systems that doctors, patients, and regulators can count on every single time. Closing Thoughts Vijaybhasker Pagidoju's work stands out in a field increasingly defined by its complexity. By combining ethical AI design with principles of infrastructure reliability, his research provides a timely reminder that technology must ultimately serve people fairly, transparently, and accountably. As healthcare continues to evolve, voices like his are helping shape a more trustworthy future for intelligent systems.
