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: MLOps & Deployment
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…
Model Versioning Systems
Model versioning systems are tools and methods used to keep track of different versions of machine learning models as they are developed and improved. They help teams manage changes, compare performance, and ensure that everyone is working with the correct model version. These systems store information about each model version, such as training data, code,…
Serverless Function Management
Serverless function management refers to the process of deploying, monitoring, scaling, and maintaining small pieces of code called functions on cloud platforms, without having to manage the underlying servers. This approach allows developers to focus on writing the code that handles specific tasks, while the cloud provider automatically handles the infrastructure, scaling, and availability. Serverless…
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,…
Log Analysis Pipelines
Log analysis pipelines are systems designed to collect, process and interpret log data from software, servers or devices. They help organisations understand what is happening within their systems by organising raw logs into meaningful information. These pipelines often automate the process of filtering, searching and analysing logs to quickly identify issues or trends.
Inference Latency Reduction
Inference latency reduction refers to techniques and strategies used to decrease the time it takes for a computer model, such as artificial intelligence or machine learning systems, to produce results after receiving input. This is important because lower latency means faster responses, which is especially valuable in applications where real-time or near-instant feedback is needed….
Continuous Integration Automation
Continuous Integration Automation is a process in software development where code changes are automatically tested and merged into a shared codebase. This automation ensures that new code works well with existing code and helps catch errors early. It uses tools and scripts to automatically build, test, and sometimes deploy code whenever developers make changes.
Automation Testing Frameworks
Automation testing frameworks are structured sets of guidelines and tools that help software teams automatically test their applications. These frameworks provide a standard way to create, organise, and run test scripts, making the testing process more efficient and reliable. They support repeatable and consistent testing, which helps in finding bugs early and maintaining software quality…
Event-Driven Automation Pipelines
Event-driven automation pipelines are systems where processes or tasks automatically start in response to specific events or triggers. Instead of running on a fixed schedule, these pipelines respond to changes such as new data arriving, a user action, or a system alert. This approach helps organisations react quickly and efficiently by automating workflows only when…