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The industrial manufacturing industry is the top adopter of artificial intelligence, with 93 percent of leaders stating their organizations are at least moderately using AI. Manufacturers are frequently facing different challenges such as unexpected machinery failure or defective product delivery.
Manufacturing companies can reduce their maintenance costs or customize product designs by adopting manufacturing AI solutions in manufacturing plants. Nearly half (49%) of respondents from the automotive and manufacturing sectors believe that AI will be crucial to manufacturing success in the next five years.
Increasing data demands and complex network architectures complicate network management, often leading to reduced performance and productivity. This article presents 10 essential network performance best practices, offering a pathway to efficient network management.
In the dynamic landscape of network security, maintaining robust defenses against evolving cyber threats is paramount. The firewall, serving as the first line of defense against unauthorized access, plays a crucial role in safeguarding information systems. Regular firewall audits are essential to ensure the effectiveness and efficiency of these security barriers.
This article covers a complete explanation of role-based access control (RBAC) along with a step-by-step guide explaining how RBAC works, and how organizations can orchestrate it to meet their network security policies against modern threats.
AI transformation including generative AI is one of the top priorities for CEOs. Though AI is critical for enterprises its procurement and deployment is different from software procurement which enterprises are familiar with.Being aware of these differences and following best practices can help enterprises achieve success with AI procurement.
Foundation models like ChatGPT with many capabilities (e.g. translation, text generation) trained on public data have launched the generative AI wave. However, businesses need to work with specialized enterprise generative AI systems trained on private data for increased effectiveness.
Digital solutions powered by artificial intelligence (AI) and machine learning models are being implemented in almost every industry worldwide. Organizations need to collect and harvest large amounts of data, either by themselves or by working with AI data collection services, to successfully leverage these technologies, specifically to train and improve them.
In the rapidly advancing world of artificial intelligence (AI), developers strive to create machines capable of learning autonomously. The field of reinforcement learning, a subset of machine learning, plays a crucial role in these efforts, setting the stage for AI systems to learn from their actions.
With the growing adoption and complexity of AI solutions, companies are seeking new ways to develop and implement the technology in their business operations.