This episode of Techsplainers explains data quality management (DQM)—a set of practices that ensure data is accurate, complete, consistent, timely, unique, and valid. Learn why high-quality data is critical for business intelligence, regulatory compliance, and AI performance, and explore key techniques like data profiling, cleansing, validation, and monitoring. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by Matt Finio

This episode of Techsplainers covers data quality management—what it is, why it matters, and how it drives better decisions, compliance, and AI success.

What is data quality 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?

This episode of&nbsp;Techsplainers&nbsp;explores phishing—the deceptive technique cybercriminals use to trick victims into revealing sensitive information or downloading malware through fraudulent communications. The podcast explains why phishing is the most common data breach vector, accounting for 16% of all breaches and costing organizations an average of USD 4.8 million. Listeners will discover various phishing methods, including bulk email phishing, targeted spear phishing, executive-focused whaling, business email compromise, SMS phishing (smishing), voice phishing (vishing), social media attacks, and QR code scams (quishing). The discussion highlights how generative AI has transformed phishing, enabling scammers to create more convincing messages in minutes instead of hours. The episode concludes with practical advice on spotting phishing red flags—like urgency tactics, unsolicited requests, poor grammar, and spoofed URLs—and implementing preventative measures, such as security awareness training, multi-factor authentication, and advanced threat detection tools. Find more information at&nbsp;https://www.ibm.com/think/podcasts/techsplainers. Narrated by Bryan Clark

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.

What is phishing?

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.

What is data quality management?

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Recent Episodes

What is data quality management?

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Recent Episodes

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?

What is phishing?