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March 31.2025
3 Minutes Read

Could AI Manage Your Cloud Workloads? Discover SAS's Patented Solution!

AI manages cloud workloads with digital cloud on circuit board.

Revolutionizing Cloud Workloads with AI Management

As businesses increasingly rely on cloud computing for analytics, the demand for effective resource management has never been higher. Imagine a scenario where AI seamlessly takes over the management of cloud resources, ensuring that each project gets precisely the computing power it requires. A new patent from SAS is set to transform this vision into reality.

What the SAS Patent Proposes

The patented system focuses on dynamic allocation of computing resources informed by real-time demand, leveraging machine learning to optimize resource usage while simultaneously minimizing waste. Rich Wellum, a manager at SAS and one of the inventors, describes this innovation as a method where AI can determine deployment configurations based on the nature of analytical tasks. This approach is particularly beneficial for businesses that frequently deal with fluctuating data workloads.

Cost Benefits and Performance Improvements

One of the standout advantages of this new system is its potential to lower operational costs. Traditional cloud environments often experience resource over-allocation for worst-case scenarios, which leads to excessive spending on unused capacity. By employing AI-driven management, companies can achieve significant cost reductions on hardware and energy, facilitating a more efficient budget allocation.

Historical Context of AI in Resource Management

The incorporation of AI in resource management is not entirely new, yet SAS's patent offers a fresh perspective, particularly as advancements in machine learning gain traction. Previous iterations of resource management have yielded good results, but often required continuous manual oversight. By harnessing AI's capability for predictive analysis, SAS's solution promises not just automation but enhanced performance through intelligent capacity adjustments—an evolution that could define the next phase of cloud computing efficiency.

The Future of Cloud Workloads

Looking ahead, the integration of AI in managing cloud workloads could reshape how enterprises operate. As this technology evolves, predictions suggest that businesses will shift from traditional IT management structures to fully autonomous systems that can adjust based on real-time analytics. This could foster not only operational efficiencies but also innovative approaches to data analytics.

Real-World Applications and Examples

To illustrate the practicality of this innovation, consider a large bank that processes massive datasets weekly at a specific time. With the traditional method, IT administrators must prepare the necessary infrastructure manually, which demands time and resources. However, the patented SAS system would eliminate this cumbersome task, automatically setting up the infrastructure when needed. This not only enhances efficiency but also allows personnel to focus on strategic tasks rather than routine operations.

Understanding AI Learning in Resource Management

Delving deeper into the AI learning aspects of this patent, the machine learning feedback loop allows for continual improvement based on historical data and outcomes. This learning path enables the system to adapt to both literal and inferred needs over time. For instance, if the resource demand unexpectedly spikes or fluctuates, the AI system can reconfigure itself to accommodate such changes swiftly, thus ensuring that businesses are equipped to handle varying workloads without significant manual intervention.

Opportunities for Businesses and IT Professionals

AI-driven resource management presents numerous opportunities for entrepreneurs and IT professionals alike. For businesses, it paves the way for operational efficiency and cost-effectiveness that were previously unattainable through manual systems. IT professionals, on the other hand, will witness an evolution in their roles; they can transition from task-oriented operations to more strategic thinking and innovation-focused roles that leverage advanced technologies.

By understanding how SAS's innovative application could empower organizations and minimize resource-related challenges, stakeholders in tech and business can prepare for the impending changes in cloud workload management. Keeping abreast of developments in ai learning will be crucial as this technology shapes the future landscape.

Take Action on Your AI Learning Path

As we stand on the brink of groundbreaking advancements in AI-driven resource management, it's vital for businesses and IT professionals to embrace these changes. Consider investing in AI learning programs that can enhance your understanding and application of these technologies in your organization. Act now to ensure you're not left behind in this transformative wave!

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07.17.2025

Essential Steps to Starting Your AI Journey Successfully

Update Embarking on the AI Journey: A Strategic Approach As artificial intelligence (AI) continues to reshape industries ranging from healthcare to finance, organizations are faced with the pressing question of how to effectively start their AI journey. While the potential of AI is undeniable, the initial steps to implementation can often feel daunting. This article demystifies these first steps and provides essential insights into grounding your organization's AI strategy effectively. Step 1: Commit to Change for Future Success Every organization today must recognize that embracing AI isn't just optional—it's vital for sustaining competitiveness in a rapidly evolving marketplace. Business leaders must commit to not only adopting AI technology but also fostering an organizational culture that embraces this change. A recent global survey indicated that 92% of organizations are planning to allocate budgets towards generative AI projects in 2025. This investment is driven by aims to enhance customer satisfaction (81%), streamline operational costs (76%), and strengthen compliance and risk management (72%). Implementing AI technologies has proven to yield tangible benefits; it can automate tedious tasks, provide insightful data analysis, and facilitate improved decision-making processes. Furthermore, upskilling employees in AI technologies prepares them for a workforce increasingly defined by these innovations. As Nvidia CEO Jensen Huang emphasizes, "AI won’t steal jobs, but someone who’s an expert with AI will." Hence, the emphasis should be on cultivating expertise among staff to leverage AI's full potential. Step 2: Identifying Key Business Problems A crucial precursor to any AI initiative is pinpointing specific business challenges that AI could address. Organizations must shift focus from a vague desire to 'prepare for AI' to articulating clear objectives. Leaders ought to ask powerful questions that illuminate paths to enhanced efficiency or innovation: "What critical problems are we facing? What opportunities are ripe for AI intervention?" This method will ensure that AI deployment aligns closely with the organization's strategic goals. For example, a pharmaceutical company may set a vision to cut down clinical trial timelines, whereas a financial firm might aim to bolster fraud detection mechanisms. Government entities can similarly utilize AI to improve citizen services. The approach heralded by SAS is not only about harnessing powerful technology; it incorporates fundamental ethical considerations, ensuring that AI development practices prioritize human values, transparency, and accountability to cultivate a community where technology serves society responsibly. Step 3: Revise Your Development Plan Updating a development plan to integrate AI initiatives is vital in ensuring sustained progress. This goes beyond plug-and-play solutions; organizations should consider a comprehensive review and amendment of existing operational frameworks. Aligning resources—both human and technological—towards the AI strategy is essential. Notably, regular assessments and adaptability to emerging AI trends will aid the organization in maintaining a proactive rather than reactive stance. Beyond the Basics: Additional Considerations for AI Success While the tactical steps to initiate AI implementation are crucial, organizations should also contemplate broader aspects, such as data governance and interoperability challenges. Integrating AI into current operations necessitates a robust framework for data handling—one that not only respects privacy regulations but also leverages data intelligently for meaningful insights. Moving Forward with AI: Building Knowledge and Confidence As businesses navigate their AI journeys, harnessing platforms that provide training and self-guided courses on AI learning paths can equip teams with necessary skills. Continuous learning will not only stimulate innovation but will also foster resilience in the workforce. Recognizing AI science and its implications early on is essential for any organization aiming to thrive in an AI-driven future. Organizations should not underestimate the significance of starting their AI journey today. Committing to this groundbreaking transition, identifying the core business problems to be solved, and revising development plans are pivotal actions that all facilitate a successful integration into the AI landscape. Ready to dive deeper into AI learning? Explore free resources and courses available to empower your team and enhance your understanding of the AI ecosystem.

07.17.2025

Master AI Learning: Recognize and Tackle Missing Values in Data

Update Understanding Missing Values: A Crucial Element in Data Analysis Missing values, often referred to as "missings," can be the unexpected guests in your datasets that complicate analysis and skew results. This article will explore the different types of missing values and strategies to effectively handle them, particularly for those who are diving into the world of AI learning and data science. The Importance of Classifying Missing Values Before we tackle how to address missing values, understanding their origin is crucial. Common causes are technical errors (like malfunctioning sensors), human omissions (such as respondents skipping sensitive questions), and logistical issues (like lost samples in laboratories). Recognizing the type of missing data — whether it happens at random or indicates some underlying pattern — is where we introduce the concepts of MCAR, MAR, and MNAR. Types of Missing Values: MCAR, MAR, and MNAR Explained 1. MCAR (Missing Completely At Random): This scenario indicates that every record has the same chance of being missing, and this absence is unrelated to any observed or unobserved variable. For instance, if a scale fails occasionally, the loss of data doesn’t correlate with the subject's weight or any relevant factor. Analyzing only the complete cases here would yield unbiased but less statistically powerful results. 2. MAR (Missing At Random): In this case, the likelihood of a value being missing can be explained by observed variables. For example, if fitness devices malfunction more on softer surfaces, knowledge of ground hardness can help us understand variability in missing data. Many modern analytical techniques, like multiple imputations, rely on this assumption, where all predictors of missingness must be included in the model. 3. MNAR (Missing Not At Random): This type occurs when the missingness of a data point relates to unobserved values. An illustrative example is where individuals in higher income brackets are less likely to disclose their salaries in surveys, thus creating gaps based on the income itself. Traditional approaches may fall short here; more advanced sensitivity analysis or additional data may be required. Strategies for Addressing Missing Data Now that we understand each category of missing values, let's delve into some effective strategies to tackle these issues: 1. Deletion Methods: One simple approach is to delete the missing values outright. While effective, this method can introduce bias and reduce the size of your dataset significantly; thus, use this method carefully. 2. Imputation Techniques: Refilling missing values is prevalent in data science. Using average values or more sophisticated techniques like K-nearest neighbors (KNN) can mitigate issues and improve model accuracy. 3. Advanced Analytics: Employ machine learning methods that can handle missing data on their own. Techniques such as decision trees can work around gaps without needing prior data completion. Future Implications in AI Learning Understanding and effectively dealing with missing data is not just an academic exercise; it’s vital for professionals working with machine learning and AI. As AI continues to permeate various sectors, the ability to analyze comprehensive datasets will set apart industry leaders from followers. Missing values, when inadequately addressed, can lead to misleading conclusions and suboptimal outcomes in AI applications. Taking Action: Embrace Robust Data Strategies In conclusion, recognizing the implications of missing values on data integrity should drive everyone, from students to seasoned professionals, to embrace robust methodologies in their analyses. Can you afford to leave data gaps in your AI learning path? We must develop a keen eye for recognizing patterns and applying sound strategies to ensure accurate insights.

07.16.2025

Cómo la IA Generativa Está Transformando el Éxito Empresarial y La Satisfacción Laboral

Update La Revolución de la IA Generativa en el Mundo Empresarial En un contexto empresarial que evoluciona rápidamente, la inteligencia artificial generativa se está posicionando como un elemento clave para el éxito. En su recorrido en el SAS Innovate on Tour, se destacó cómo esta tecnología está transformando no solo las operaciones empresariales, sino también la experiencia de los empleados. Un informe de Coleman Parkes indica que el 86% de las empresas que adoptaron IA generativa han visto una mejora en la satisfacción laboral, un dato que subraya el poder de la IA para impactar positivamente la vida laboral. El Doble Impacto de la IA en la Satisfacción Más allá de la mejora en la satisfacción de los colaboradores, el 68% de las empresas también reportó un aumento en la retención de clientes. Esto sugiere que la IA no solo mejora la experiencia interna, sino que también tiene un efecto directo en los consumidores. Esta interconexión entre el bienestar de los empleados y el éxito de los clientes puede ser crucial para el crecimiento de las empresas en un entorno altamente competitivo. Importancia de un Enfoque Ético en la IA La implementación responsable de la IA es otro tema central que salió a la luz en el evento. Según I-Sah Hsieh, Gerente Global de Programas de Ética de Datos en SAS, sectores como el financiero y el de salud deben priorizar el uso ético de la IA. Esto no solo es esencial para proteger los datos sensibles, sino que también puede mejorar la confianza en la tecnología entre los empleados y los clientes. Desafíos en la Adopción de la IA en América Latina A pesar de las ventajas, la adopción de la IA generativa en América Latina enfrenta varios desafíos, entre ellos la infraestructura digital deficiente y la falta de habilidades técnicas. Un sorprendente 60% de los tomadores de decisiones siente que no cuenta con las herramientas necesarias para llevar a cabo la IA de manera eficaz. Este hecho resalta la importancia de que las empresas implementen estrategias efectivas que aseguren la integración de la IA en sus procesos. El Camino hacia la Transformación Industrial A pesar de los obstáculos, invertir en IA generativa está demostrando ser una decisión acertada para muchas empresas. De hecho, el 55% de las organizaciones reportaron una disminución en costos operativos, evidenciando cómo esta tecnología puede traducirse en optimización y eficiencia. Para desbloquear todo su potencial, las empresas deben superar barreras clave como la confianza en los sistemas de IA y el cumplimiento normativo. Perspectivas Futuras de la IA Generativa En el panorama empresarial de hoy, la IA generativa parece estar en la cúspide de una nueva era de innovación. Si las empresas pueden navegar a través de los desafíos existentes y adoptar un enfoque estratégico, el potencial para transformaciones significativas en la productividad y el crecimiento es inmenso. Por lo tanto, la clave para el éxito radica en la capacidad de las organizaciones para adaptarse y evolucionar con estas tecnologías avanzadas. En conclusión, la inteligencia artificial generativa no solo está moldeando el futuro del trabajo, sino que también redefine cómo las empresas pueden prosperar en un mundo digital. Comprender sus aplicaciones y beneficios es esencial para cualquier organización que quiera mantenerse relevante en un panorama tecnológico en rápida evolución.

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#721","city":"Greenville","state":"SC","zip":"29341","email":"support@divinewebconsultants.com","tos":"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","privacy":"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