Kiroframe Blog
Insights, Tips and Best Practices on ML and MLOps: Your guide to Machine Learning and Operations
Versioning Machine Learning models: architecture, practices, and common pitfalls
Versioning Machine Learning models: architecture, practices, and common pitfalls Table of contents Why versioning Machine Learning models matters in 2026 What does “Versioning machine learning models” actually mean? Machine Learning model versioning in practice: what teams usually miss Common mistakes in versioning prompts and models Platforms for ML model versioning: what to look for ML models versioning across the full
ML pipeline architecture: a practical blueprint for designing Reliable Machine Learning workflows
Machine Learning pipeline architecture: a practical blueprint for designing Reliable ML workflows Table of contents What is ML pipeline architecture? From experimentation to production pipelines Core Machine Learning pipeline components Architectural patterns in ML pipelines Designing for enterprise-scale requirements Common pitfalls in ML pipeline architecture Future trends shaping ML pipelines Conclusion: Architecture as the foundation of sustainable ML Machine learning
What is Machine Learning as a Service (MLaaS)
What is Machine Learning as a Service (MLaaS), and the types of MLaaS solutions in 2026 Table of contents Machine Learning as a Service (MLaaS): definition and core idea Why MLaaS matters in 2026 How MLaaS works: an overview Types of MLaaS: from pre-trained APIs to custom platforms Choosing the right type of MLaaS for your needs Conclusion: MLaaS in
DeepSeek vs. ChatGPT: how businesses compare modern language models
DeepSeek vs. ChatGPT: how businesses compare modern language models Table of contents Why enterprises compare DeepSeek and ChatGPT What makes ChatGPT a general-purpose business assistant What defines DeepSeek’s efficiency-oriented approach DeepSeek vs. ChatGPT: enterprise comparison across key dimensions Real-world scenarios: how companies choose Common misconceptions in DeepSeek vs. ChatGPT discussions How to decide between DeepSeek and ChatGPT DeepSeek vs. ChatGPT:
LLM models vs. small language models
Large language models models vs. small language models: how businesses choose Table of contents What are language models in a business context? What defines LLM models? What are small language models? Large language models vs. small language models: enterprise comparison Real-world scenarios: how companies choose Common misconceptions and pitfalls How to choose between LLM models and small language models Why
AI model comparison: how businesses choose the right AI engine
AI model comparison: how businesses choose the right AI engine Table of contents Why choosing an AI model is a business decision, not just a technical one The AI model selection matrix: key criteria businesses should evaluate Comparing AI model types using real business scenarios AI model comparison by approach: open-source, proprietary, and hybrid Common mistakes companies make during AI
AI models for business: types, use cases, benefits, and real-world trade-offs
AI models for business: types, use cases, benefits, and real-world trade-offs Artificial intelligence is no longer an experimental technology reserved for large tech companies. According to Gartner, global AI spending is forecast to reach nearly $1.5 trillion this year, with growth expected to exceed $2 trillion in 2026. This surge reflects not only increased investment in infrastructure and software, but
Effective ways to debug and profile machine learning model training
Machine learning (ML) models have become a cornerstone of modern technology, powering applications from image recognition to natural language processing. Despite widespread adoption, developing and training ML models remains intricate and time-intensive. Debugging and profiling these models, in particular, can pose significant challenges. This article delves into practical tips and proven best practices to help you effectively debug and profile
MLOps artifacts: data, model, code
In modern machine learning workflows, everything revolves around three core artifacts: data, models, and code. These aren’t abstract concepts — they are the essential building blocks that determine whether ML systems are reliable, reproducible, and scalable.
Most MLOps frameworks consider these artifacts the foundation of the entire lifecycle. To keep ML systems stable and repeatable, teams must maintain pipelines
Experiment Tracking: Definition, Benefits, and Best Practices
The practice of recording and maintaining important data (metadata) of different experiments when creating machine learning models is known as experiment tracking. This action contains specifics like the many machine learning models utilized, the model hyperparameters (such as the size of a neural network), training data versions, and the code used to create the model. Creating ML models involves a
Unlocking machine learning performance metrics: a deep dive
Assessing the effectiveness and reliability of machine learning models through performance metrics is the cornerstone of progress in this dynamic field. These metrics are not mere accessories but indispensable tools, guiding developers in honing algorithms and elevating their performance to new heights. This article emphasizes that choosing a project’s most suitable performance metric can be daunting, but conducting evaluations of
Harnessing the power of Machine Learning to optimize processes
As organizations strive to modernize and optimize their operations, machine learning (ML) has emerged as a valuable tool for driving automation. Unlike traditional rule-based automation, ML excels in handling complex processes and continuously learns, leading to improved accuracy and efficiency over time.