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 industries like financial services and telecommunications, where performance and reliability are non-negotiable. From real-time latency detection in high-frequency trading to managing 5G networks and edge computing, observability tools enable proactive optimization and self-healing capabilities. This episode highlights how observability helps organizations adapt to evolving technologies and maintain seamless connectivity across hybrid and multi-cloud infrastructures. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by PJ Hagerty

This episode of Techsplainers compares network observability with network performance monitoring and DevOps observability. Discover how observability delivers deeper insights, proactive optimization, and why it is vital for industries like financial services and telecommunications.

Part 2: What is network observability?

This episode of&nbsp;Techsplainers&nbsp;explores generative AI, the revolutionary technology that creates original content like text, images, video, and code in response to user prompts. We walk through how these systems work in three main phases: training foundation models on massive datasets, tuning them for specific applications, and continuously improving their outputs through evaluation. The podcast traces the evolution of key generative AI architectures—from variational autoencoders and generative adversarial networks to diffusion models and transformers—highlighting how each contributes to today's powerful AI tools. We examine generative AI's diverse applications across industries, from enhancing customer experiences and accelerating software development to transforming creative processes and scientific research. The episode also addresses emerging concepts like AI agents and agentic AI while candidly discussing the technology's challenges, including hallucinations, bias, security vulnerabilities, and deepfakes. Despite these concerns, the episode emphasizes how organizations are increasingly adopting generative AI, with analysts predicting 80% implementation by 2026. 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&nbsp;Amanda Downie

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

What is generative AI?

This episode of&nbsp;Techsplainers&nbsp;explores model deployment, the crucial phase that brings machine learning models from development into production environments where they can deliver real business value. We examine why deployment is so critical—according to Gartner, only about 48% of AI projects make it to production—and discuss four primary deployment methods: real-time (for immediate predictions), batch (for offline processing of large datasets), streaming (for continuous data processing), and edge deployment (for running models on devices like smartphones). The podcast walks through the six essential steps of the deployment process: planning (preparing the technical environment), setup (configuring dependencies and security), packaging and deployment (containerizing the model), testing (validating functionality), monitoring (tracking performance metrics), and implementing CI/CD pipelines (for automated updates). We also address key challenges organizations face when deploying models, including high infrastructure costs, technical complexity, integration difficulties with existing systems, and ensuring proper scalability to handle varying workloads. 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 model deployment—the process of moving machine learning models to production environments where they can deliver real business value.

What is model deployment?

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?

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 2: What is network observability?

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Recent Episodes

Part 2: What is network observability?

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Related Resources

Recent Episodes

What is generative AI?

What is model deployment?

What is AI lifecycle management?

What is a machine learning pipeline?

Part 2: What is MLOps?

Part 1: What is MLOps?