To be future-proof in the new average volatility, supply chains need to address greater complexity quicker than ever. Technology is a critical enabler in solving this challenge. However, companies must select technology solutions that enable them to make faster decisions and break down silos, yet still be flexible enough to…

What is a knowledge graph? Figure 1. A simple knowledge graph representation Source: Wikipedia A knowledge graph is a structured representation of information and knowledge in the form of a graph. It consists of nodes (real world entities or concepts) connected by edges (relationships or associations). The purpose of a knowledge graph is to model…

Data mesh emphasizes data decentralization, autonomy, and productization. In contrast, the data fabric architecture advocates centralization and unified data access. Both are valuable approaches to democratizing data and insights, but they differ in their underlying philosophy and architecture. Table of contents Is data fabric the same as data mesh? Let’s explore Key benefits…

Working with Machine Learning models requires a different set of organizational capabilities as opposed to building software. These new sets of capabilities are driven by MLOps — which is further divided into different ML model processes, one of which is Model Management. Machine Learning model management is a class of…

As machine learning and AI propagate in software products and services, we must establish best practices and tools to test, deploy, manage, and monitor ML models in real-world production. In short, with MLOps we strive to avoid “technical debt” in machine learning applications. SIG MLOps defines “an optimal MLOps experience…

MLOps Definition MLOps — the term itself derived from machine learning or ML and operations or Ops — is a set of management practices for the deep learning or production ML lifecycle. These include practices from ML and DevOps alongside data engineering processes designed to efficiently and reliably deploy ML models in…

Why You May Need a Data Mesh Many organizations have invested in a central data lake and a data team with the expectation to drive their business based on data. However, after a few initial quick wins, they notice that the central data team often becomes a bottleneck. The team cannot handle all the analytical questions of…

What Is a Data Pipeline? A data pipeline is a series of steps and processes for moving, transforming, and processing data from one system to another. It ensures data availability, accuracy, and accessibility for analytics and decision-making. The pipeline includes stages such as data ingestion, extraction, transformation, validation, storage, analysis, and delivery, as well as…

The increasing volume and complexity of enterprise data, and its central role in decision-making and strategic planning are driving organizations to invest in the people, processes and technologies they need to gain valuable business insights from their data assets. That includes a variety of tools commonly used in data science…