Model serving architectures are systems designed to make machine learning models available for use after they have been trained. These architectures handle tasks such as receiving data, processing it through the model, and returning results to users or applications. They can range from simple setups on a single computer to complex distributed systems that support…
Category: Artificial Intelligence
Continuous Model Training
Continuous model training is a process in which a machine learning model is regularly updated with new data to improve its performance over time. Instead of training a model once and leaving it unchanged, the model is retrained as fresh information becomes available. This helps the model stay relevant and accurate, especially when the data…
Time Series Forecasting
Time series forecasting is a way to predict future values by looking at patterns and trends in data that is collected over time. This type of analysis is useful when data points are recorded in a sequence, such as daily temperatures or monthly sales figures. By analysing past behaviour, time series forecasting helps estimate what…
Statistical Hypothesis Testing
Statistical hypothesis testing is a method used to decide if there is enough evidence in a sample of data to support a specific claim about a population. It involves comparing observed results with what would be expected under a certain assumption, called the null hypothesis. If the results are unlikely under this assumption, the hypothesis…
Data Drift Detection
Data drift detection is the process of monitoring and identifying when the statistical properties of input data change over time. These changes can cause machine learning models to perform poorly because the data they see in the real world is different from the data they were trained on. Detecting data drift helps teams take action,…
Neural Network Backpropagation
Neural network backpropagation is a method used to train artificial neural networks. It works by calculating how much each part of the network contributed to an error in the output. The process then adjusts the connections in the network to reduce future errors, helping the network learn from its mistakes.
Autoencoder Architectures
Autoencoder architectures are a type of artificial neural network designed to learn efficient ways of compressing and reconstructing data. They consist of two main parts: an encoder that reduces the input data to a smaller representation, and a decoder that tries to reconstruct the original input from this smaller version. These networks are trained so…
Deep Belief Networks
Deep Belief Networks are a type of artificial neural network that learns to recognise patterns in data by stacking multiple layers of simpler networks. Each layer learns to represent the data in a more abstract way than the previous one, helping the network to understand complex features. These networks are trained in stages, allowing them…
Recurrent Neural Network Variants
Recurrent Neural Network (RNN) variants are different types of RNNs designed to improve how machines handle sequential data, such as text, audio, or time series. Standard RNNs can struggle to remember information from earlier in long sequences, leading to issues with learning and accuracy. Variants like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)…
Convolutional Layer Design
A convolutional layer is a main building block in many modern neural networks, especially those that process images. It works by scanning an input, like a photo, with small filters to detect features such as edges, colours, or textures. The design of a convolutional layer involves choosing the size of these filters, how many to…