Techsplainers by IBM

In this episode of Techsplainers , we dive into SRE observability, a critical practice for ensuring site reliability in today’s dynamic, cloud-native environments. Discover how SRE observability goes beyond traditional monitoring by using telemetry data—metrics, logs, and traces—to provide deep visibility into complex systems. We explain how it supports proactive issue detection, faster incident r...

This episode of Techsplainers explains what data accuracy is, why it matters, and how organizations can achieve it. We explore its role as a core dimension of data quality, the benefits of accurate data for decision-making, compliance, AI, and customer satisfaction, and the common causes of inaccuracies—from human error to outdated information and biased data.

This episode of Techsplainers explains what data integrity is, why it matters, and how organizations can maintain it. We cover the processes and security measures that ensure data remains accurate, complete, and consistent throughout its lifecycle. Learn why data integrity is critical for analytics, compliance, and trust—and explore the five key types of data integrity.

This episode of Techsplainers explains the concept of multi-agent collaboration. It discusses how multi-agent systems, comprising multiple AI agents, coordinate actions in a distributed system to achieve complex tasks. These tasks, once handled only by large language models, now include customer service triage, financial analysis, technical troubleshooting, and more. The podcast details how agents...

In this episode of Techsplainers , host Alice explains the concept of multi-agent systems. She describes how these systems consist of multiple AI agents working together to complete complex tasks, with each agent maintaining autonomy yet cooperating and coordinating through communication and distributed problem-solving. We also discuss different architectural designs (centralized and decentralized...

This episode of Techsplainers explores vibe coding—using AI to generate software code from natural language prompts, accelerating development while freeing humans for creative work.

This episode of Techsplainers explores retrieval augmented generation (RAG)—how connecting AI models to external knowledge sources creates more accurate, up-to-date responses.

This episode of Techsplainers explores vision language models (VLMs)—AI systems that bridge visual and textual understanding to perform tasks from image captioning to visual reasoning.

This episode of Techsplainers explores large language models (LLMs)—how they work, their transformer architecture, and how they're trained and fine-tuned to understand human language.

This episode of Techsplainers examines generative AI—how it creates original content, the technology behind it, and its real-world applications and challenges.

This episode of Techsplainers explores model deployment—the process of moving machine learning models to production environments where they can deliver real business value.

This episode of Techsplainers examines AI Model Lifecycle Management—the methodology and tools needed to manage the complete AI pipeline from data collection to deployment.

This episode of Techsplainers unpacks machine learning pipelines, from data processing and model development to deployment strategies and monitoring in production environments.

This episode of Techsplainers explores the practical components of MLOps pipelines—from data management and model deployment to monitoring, governance, and implementation levels.

This episode of Techsplainers explores MLOps—a set of practices for streamlining machine learning operations, automating deployment, and improving model performance through continuous monitoring.

This episode of Techsplainers investigates notorious ransomware variants, from CryptoLocker to the Colonial Pipeline attack. Learn about ransom payment trends, legal implications, and effective prevention strategies.

This episode of Techsplainers explores how ransomware works, from encryption tactics to multi-level extortion schemes. Learn about different attack vectors, ransomware varieties, and the five stages of a typical attack.

This episode of Techsplainers looks at how phishing attacks work and how to protect. Learn to identify various phishing techniques, recognize warning signs, and implement effective prevention strategies.

This episode of Techsplainers covers how social engineering attacks exploit human psychology rather than technical vulnerabilities. Learn about different attack types—from phishing to baiting—and effective strategies to protect yourself and your organization.

This episode of Techsplainers explores what data breaches are, how they occur, and the strategies that can prevent them. Discover the true costs, common attack vectors, and how AI-powered security can dramatically reduce breach impact.

In this episode of Techsplainers , we dive deeper into network observability, comparing it to network performance monitoring and DevOps observability. Learn how observability goes beyond static thresholds and predefined metrics to provide end-to-end visibility, richer context, and predictive analytics for dynamic network environments. We also explore why network observability is critical for indus...

In this episode of Techsplainers , we explore network observability, a proactive approach to understanding and managing complex network environments. Unlike traditional monitoring, which focuses on predefined metrics, network observability provides real-time visibility into network health and performance across on-premises, hybrid, and multicloud infrastructures. We break down its five pillars—met...

In this episode of Techsplainers , we explore the key differences between observability and monitoring and why both are critical for managing complex IT environments. Monitoring focuses on tracking predefined metrics and alerting teams when something goes wrong, while observability goes further by providing context and insights into why issues occur and how to fix them. We discuss how observabilit...

In this episode of Techsplainers , we break down the three pillars of observability: metrics, logs, and traces. We'll explain how they provide the foundation for understanding complex cloud-native systems. Discover what each pillar does, why they matter, and how they complement each other to deliver actionable insights for DevOps teams. We also explore system events, distributed tracing, and emerg...

This episode of Techsplainers takes a deep dive into observability, a cornerstone of modern DevOps and cloud-native environments. We break down what observability really means—going beyond traditional monitoring to provide full-stack visibility into complex systems. You’ll learn about its three pillars: logs, traces, and metrics, and how they work together to deliver actionable insights. The discu...