MACHINE LEARNING

MLOps

Machine Learning

Definition

MLOps is the discipline that combines machine learning and operations to manage the complete lifecycle of AI models in production. It includes data versioning, automated training, performance monitoring, and model updating. For companies with multiple models in production, MLOps is essential to ensure reliability and maintainability over time.

Related terms

Machine Learning

Model Serving

Model serving is the process of making an ML model available for use in production via APIs or web services. It includes load management, scalability, latency monitoring, and version management. Platforms like AWS SageMaker, Google Vertex AI, and open source solutions make deployment accessible to SMEs as well.

Machine Learning

AutoML

AutoML (Automated Machine Learning) is the set of tools that automate the ML model-building process: algorithm selection, parameter optimization, feature engineering. Platforms like Google AutoML and H2O allow non-experts to build effective models. For SMEs without in-house data scientists, AutoML democratizes access to machine learning.

Automation & RPA

Data Pipeline

A data pipeline is an automated flow that moves and transforms data from a source to a destination. Unlike traditional ETL, modern pipelines process data in real time or near real time. For example, a pipeline can collect IoT data from machinery, aggregate it, and feed a production dashboard updated to the minute.

EXPLORE

More terms in Machine Learning

Supervised Learning

Supervised learning is the ML technique in which the model learns from labeled examples: for each input, the correct output is provided. For example, by showing thousands of emails labeled as 'spam' or 'not spam', the model learns to classify new emails. It is the most common approach in business applications because it produces reliable and interpretable results.

Unsupervised Learning

Unsupervised learning is the ML technique in which the model finds patterns in data without predefined labels. The model autonomously discovers hidden structures such as groups of similar customers (clustering) or data anomalies. It is useful when you don't know what to look for: AI can reveal market segments or anomalous behaviors you hadn't considered.

Reinforcement Learning

Reinforcement learning is an ML technique in which an agent learns by interacting with an environment and receiving rewards or penalties. It is used to optimize complex strategies such as dynamic pricing, logistics routing, and warehouse management. The agent experiments with different actions and learns which strategy maximizes long-term results.

Classification

Classification is the ML task that assigns a categorical label to an input. Examples: classifying emails (spam/not spam), support tickets (urgent/normal/low), documents (invoice/order/contract), or product images (compliant/defective). It is one of the most widespread and immediate uses of AI in SMEs, with fast and measurable ROI.

Regression

Regression is the ML task that predicts a continuous numerical value. Unlike classification which assigns categories, regression estimates quantities: sales forecasting, real estate price estimation, production cost forecasting. It is fundamental for demand forecasting and financial planning in SMEs.

Clustering

Clustering is an unsupervised ML technique that automatically groups similar data together. It is used to segment customers based on purchasing behavior, group similar products, identify patterns in support calls. The model doesn't need to be told how many groups to find (in many algorithms): it discovers them autonomously in the data.

See all glossary terms →

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