Originally published September 2021 | Updated December 2024 According to Forrester, over 25% of global data and analytics employees report annual losses exceeding $5 million due to poor data quality, with 7% estimating losses of $25 million or more. This number is staggering and considering machine learning optimization (MLOps) can significantly reduce these losses by […]
How to Build a Machine Learning Model Read More »
Multi-model serving (MMS) is cutting-edge functionality with massive potential to enable a team to scale the deployment of models on a small infrastructure footprint by intelligently scheduling models to shared servers. This is made even more effective by activating “Overcommit” allowing servers to handle more models than can fit in memory. This is done by keeping
What is Multi-Model Serving and How Does it Transform your ML Infrastructure? Read More »
MLOps is an exciting and relatively new concept in the world of AI. MLOps, or Machine Learning Operations, is a methodology that details how to effectively develop, deploy, and monitor models in a structured and segmented manner. In real-world ML applications, the emphasis is on the smooth development of models, and on their scalability. Having
What is MLOps going to do for you? Read More »
Machine learning model inference is the use of a machine learning model to process live input data to produce an output. It occurs during the machine learning deployment phase of the machine learning model pipeline, after the model has been successfully trained. Machine learning model inference can be understood as making a model operational, or
Machine Learning Model Inference vs Machine Learning Training Read More »
Machine learning pipelines are used to optimize and automate the end-to-end workflow of a machine learning model. Core elements of the machine learning process can be refined or automated once mapped within a machine learning pipeline. As more and more organizations leverage the power of machine learning, models are developed and deployed within increasingly diverse
What is a Machine Learning Pipeline? A Step By Step Guide Read More »
Machine learning is increasingly becoming an integral part of the modern digital landscape, with models being deployed in more industries and organizations than ever before. An exponential increase in the amount of data being collected by organizations has increased the complexity of processing this data. But the potential value an organization can extract from ever-more
MLOps: What is it and Why it Matters Read More »
Reinforcement learning is a method of training machine learning models through trial and error and feedback. The model will be given a goal and list of known actions. The model’s input is the measurement of its environment and current state, and output is the model’s action to move between states. Positive or negative reward signals are released
What is Reinforcement Learning? Read More »
Machine learning algorithms learn to make predictions or decisions by learning, from historical data, a model of the underlying process connecting inputs (a.k.a. features) and outputs (a.k.a. labels). If this process underlying the data remains unchanged throughout a model’s lifetime then its performance is likely to remain stable over time. However, if the process changes
Decision trees in machine learning are a common way of representing the decision-making process through a branching, tree-like structure. It’s often used to plan and plot business and operational decisions as a visual flowchart. The approach sees a branching of decisions which end at outcomes, resulting in a tree-like structure or visualization. Decision trees
Decision Trees in Machine Learning Explained Read More »
Machine Learning Regression is a technique for investigating the relationship between independent variables or features and a dependent variable or outcome. It’s used as a method for predictive modeling in machine learning, in which an algorithm is used to predict continuous outcomes. Solving regression problems is one of the most common applications for machine learning
Machine Learning Regression Explained Read More »
SELDON, SELDON CORE, ALIBI and the Drop Logo are all trade marks of Seldon Technologies Limited
© 2025 Seldon Technologies. All Rights Reserved.
