Amazon SageMaker is the Amazon Web Services (AWS) cloud service for building, training and deploying machine learning models. In late 2024 the original service was renamed “Amazon SageMaker AI”; since then, “Amazon SageMaker” has referred to a broader platform for data, analytics and AI with the SageMaker Unified Studio interface (as of October 2026). It is used mainly by data science teams running models on AWS infrastructure.
A machine learning pipeline is a defined sequence of steps that turns raw data into a deployable model: collecting and cleaning data, preparing features, training, evaluating, deploying and monitoring. Automating these steps makes results reproducible and lets new data be processed with little effort. A retailer, for instance, can retrain and test its sales forecasting model every week using the latest figures.
MLflow is an open-source platform for managing the entire life cycle of machine learning models, originally introduced by Databricks in 2018. It records experiments with their parameters and metrics, manages model versions in a registry and simplifies deployment. More recent versions also support tracing and evaluating applications built on language models. Teams can therefore trace which model was produced with which data and settings.
Kubeflow is an open-source platform for running machine learning workflows on Kubernetes, from data preparation through training and hyperparameter search to model serving. Originally initiated by Google, the project belongs to the Cloud Native Computing Foundation, where it reached “graduated” status in August 2026 (as of October 2026). It is aimed mainly at teams that train their own models at scale on their own or cloud infrastructure.
Google Colab (Colaboratory) is a Google cloud service for running Python code in Jupyter-style notebooks directly in the browser, with no local installation. The free tier also provides GPUs and TPUs, subject to availability and with runtime limits; paid plans offer more computing power. Colab is popular for teaching, data analysis and trying out models, and a built-in Gemini assistant helps write and fix code (as of October 2026).
