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 emerging capabilities like continuous profiling, which offer deeper visibility into application performance. Whether you are a developer, IT professional, or tech enthusiast, this episode will help you understand how observability accelerates troubleshooting, optimizes performance, and supports modern digital transformation. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by PJ Hagerty

This episode of Techsplainers explores the three pillars of observability—metrics, logs, and traces—and how they work together to deliver full-stack visibility into complex systems. Learn why these pillars matter for DevOps and explore emerging features like profiling that enhance performance monitoring and troubleshooting.

Three pillars of observability

This episode of&nbsp;Techsplainers&nbsp;explores vision language models (VLMs), the sophisticated AI systems that bridge computer vision and natural language processing. We examine how these multimodal models understand relationships between images and text, allowing them to generate image descriptions, answer visual questions, and even create images from text prompts. The podcast dissects the architecture of VLMs, explaining the critical components of vision encoders (which process visual information into vector embeddings) and language encoders (which interpret textual data). We delve into training strategies, including contrastive learning methods like CLIP, masking techniques, generative approaches, and transfer learning from pretrained models. The discussion highlights real-world applications—from image captioning and generation to visual search, image segmentation, and object detection—while showcasing leading models like DeepSeek-VL2, Google's Gemini 2.0, OpenAI's GPT-4o, Meta's Llama 3.2, and NVIDIA's NVLM. Finally, we address implementation challenges similar to traditional LLMs, including data bias, computational complexity, and the risk of hallucinations. 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 Amanda Downie

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.

What are vision language models (VLMs)?

This episode of&nbsp;Techsplainers&nbsp;explores large language models (LLMs), the powerful AI systems revolutionizing how we interact with technology through human language. We break down how these massive statistical prediction machines are built on transformer architecture, enabling them to understand context and relationships between words far better than previous systems. The podcast walks through the complete development process—from pretraining on trillions of words and tokenization to self-supervised learning and the crucial self-attention mechanism that allows LLMs to capture linguistic relationships. We examine various fine-tuning methods, including supervised fine-tuning, reinforcement learning from human feedback (RLHF), and instruction tuning, that help adapt these models for specific uses. The discussion covers practical aspects like prompt engineering, temperature settings, context windows, and retrieval augmented generation (RAG) while showcasing real-world applications across industries. Finally, we address the significant challenges of LLMs, including hallucinations, biases, and resource demands, alongside governance frameworks and evaluation techniques used to ensure these powerful tools are deployed responsibly. Find more information at <a href="https://www.ibm.com/think/podcasts/techsplainers" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/podcasts/techsplainers</a> Narrated by Amanda Downie

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.

What are large language models (LLMs)?

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?

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.

Three pillars of observability

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Recent Episodes

Three pillars of observability

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Related Resources

Recent Episodes

What are vision language models (VLMs)?

What are large language models (LLMs)?

What is generative AI?

What is model deployment?

What is AI lifecycle management?

What is a machine learning pipeline?