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—metrics, logs, traces, context, and correlation—and explain how they work together to deliver actionable insights. You will also learn about key capabilities, such as intelligent alerting, topology mapping, and continuous performance analysis, as well as the benefits of observability for security, cloud migration, and cost optimization. Whether you are an IT professional or a tech enthusiast, this episode will help you understand why network observability is critical for resilience and efficiency in today’s digital world. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by PJ Hagerty

This episode of Techsplainers introduces network observability and explains why it is essential for modern IT environments. Learn about its five pillars, key features, and how it helps businesses move from reactive monitoring to proactive optimization for better performance and reliability.

Part 1: What is network observability?

This episode of&nbsp;Techsplainers&nbsp;explores AI Model Lifecycle Management, the comprehensive methodology for managing artificial intelligence models throughout their entire lifecycle. We discuss why a structured approach to AI deployment is critical for enterprise success, especially when decisions made by AI systems can significantly impact business outcomes. The podcast outlines the four main stages of the AI pipeline: collect (making data accessible), organize (creating an analytics foundation), analyze (building AI with trust), and infuse (operationalizing AI across business functions). We also examine the essential components of effective AI lifecycle management, including data governance, quality assurance, fairness evaluation, and explainability. The episode concludes by highlighting the key features needed in AI management tools—from ease of model training and deployment at scale to comprehensive monitoring capabilities—using IBM Cloud Pak for Data as an illustrative example of an end-to-end platform designed to increase the throughput of data science activities and accelerate time to value from AI initiatives. Find more information at&nbsp;<a href="https://www.ibm.com/think/podcasts/techsplainers" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/podcasts/techsplainers</a> Narrated by Ian Smalley

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.

What is AI lifecycle management?

This episode of&nbsp;Techsplainers&nbsp;explores the machine learning pipeline—the systematic process of designing, developing, and deploying machine learning models. We break down the entire workflow into three distinct stages: data processing (covering ingestion, preprocessing, exploration, and feature engineering), model development (including algorithm selection, hyperparameter tuning, training approaches, and performance evaluation), and model deployment (addressing serialization, integration, architecture, monitoring, updates, and compliance). The podcast also emphasizes the critical "Stage 0" of project commencement, where stakeholders define clear objectives, success metrics, and potential obstacles before starting technical work. Throughout the discussion, we highlight how each stage contributes to creating effective, high-performing ML models while examining various training methodologies—from supervised and unsupervised learning to reinforcement and continual learning approaches. Special attention is given to model monitoring and maintenance, acknowledging that deployment is not the end but rather the beginning of a model's productive life cycle. Find more information at&nbsp;<a href="https://www.ibm.com/think/podcasts/techsplainers " rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/podcasts/techsplainers </a> Narrated by Ian Smalley

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

What is a machine learning pipeline?

This episode of&nbsp;Techsplainers&nbsp;explores the practical implementation of MLOps, diving into the key components that comprise an effective machine learning operations pipeline. We examine the five essential elements: data management (including acquisition, preprocessing, and versioning), model development (covering training, experimentation, and evaluation), model deployment (focusing on packaging and serving), monitoring and optimization (highlighting performance tracking and retraining), and collaboration and governance (emphasizing version control and ethical guidelines). The podcast also investigates how generative AI and large language models are reshaping MLOps practices before explaining the four maturity levels of MLOps implementation—from manual processes to fully automated systems with continuous monitoring and governance. Throughout the episode, we emphasize that organizations should select the appropriate MLOps maturity level based on their specific needs rather than pursuing the most advanced level by default. Find more information at&nbsp;<a href="https://www.ibm.com/think/podcasts/techsplainers" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/podcasts/techsplainers</a> Narrated by Ian Smalley

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

Part 2: What is MLOps?

This episode of&nbsp;Techsplainers&nbsp;introduces MLOps (machine learning operations), a methodology that creates an efficient assembly line for building and running machine learning models. The podcast explains how MLOps evolved from DevOps principles to address the unique challenges of ML development, including resource intensity, time consumption, and siloed teams. We explore the key benefits of MLOps—increased efficiency through automation, improved model accuracy through continuous monitoring, faster time to market, and enhanced scalability and governance. The episode details eight core principles that define effective MLOps practices: collaboration, continuous improvement, automation, reproducibility, versioning, monitoring and observability, governance and security, and scalability. Finally, we examine the key elements of successful MLOps implementation, including the necessary technical and soft skills, essential tools like ML frameworks and CI/CD pipelines, and best practices for model lifecycle management. Find more information at&nbsp;https://www.ibm.com/think/podcasts/techsplainers. Narrated by Ian Smalley

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

Part 1: What is MLOps?

This episode of Techsplainers continues the exploration of ransomware, focusing on notorious variants that have caused billions in damages worldwide. The podcast examines landmark ransomware families, including CryptoLocker, which kickstarted modern ransomware attacks; WannaCry, which infected 200,000 computers across 150 countries; and Darkside, responsible for the Colonial Pipeline attack that disrupted 45% of the US East Coast's fuel supply. Listeners will learn about the evolution of ransomware tactics, from standard encryption to AI-powered "Ransomware 3.0" like PromptLock. The discussion also covers ransom payment trends—noting that 63% of victims now refuse to pay—along with law enforcement's stance against payments and potential legal consequences. The episode concludes with essential prevention strategies, including maintaining offline backups, regular patching, employee training, and establishing formal incident response plans that can save organizations nearly $1 million per attack through faster identification. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Bryan Clark

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.

Part 2: What is ransomware?

This episode of Techsplainers demystifies ransomware—malicious software that encrypts victims' data and demands payment for its release. The podcast explains how ransomware has evolved from simple encryption attacks to sophisticated double and triple extortion tactics that threaten data leaks and attacks on business partners. Listeners will learn about different types of ransomware, including encrypting (crypto) ransomware, screen-locking variants, leakware, mobile ransomware, wipers, and scareware. The discussion covers common infection vectors, such as phishing, software vulnerabilities, and credential theft, along with the growing "Ransomware-as-a-Service" business model that allows criminals without technical skills to deploy attacks. The episode walks through the five stages of a typical ransomware attack, from initial access to the final ransom demand, highlighting why these attacks cost victims an average of USD 5.08 million, according to IBM's research. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Bryan Clark

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.

Part 1: What is ransomware?

Introducing the Techsplainers by IBM podcast, your new podcast for quick, powerful takes on today’s most important AI and tech topics. Each episode brings you bite-sized learning designed to fit your day, whether you’re driving, exercising, or just curious for something new. This is just the beginning. Tune in every weekday at 6 AM ET for fresh insights, new voices, and smarter learning.

Part 1: What is network observability?

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Recent Episodes

Part 1: What is network observability?

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Related Resources

Recent Episodes

What is AI lifecycle management?

What is a machine learning pipeline?

Part 2: What is MLOps?

Part 1: What is MLOps?

Part 2: What is ransomware?

Part 1: What is ransomware?