Top 10 Open-source AI/ML platform engineering tools

Building AI and Machine Learning platforms in the cloud is no longer difficult. In this article, we will examine the top 10 Open-source AI/ML platform engineering tools.

KitOps

KitOps is an innovative open-source project to enhance collaboration among engineering teams working on integrating or managing self-hosted AI/ML models.

At the heart of KitOps is the ModelKit, which enables the seamless sharing of all necessary artifacts in the AI/ML model lifecycle. This includes datasets, code, doc, configurations, and models. It uses a YAML-based configuration file that simplifies sharing all the configurations for the secure development process. It integrates seamlessly with Docker, Terraform, and Kubernetes to automate and accelerate your development cycle.

Feast

Feast (Feature Store) is an open-source feature store that helps teams operate production ML systems at scale by allowing them to define, manage, validate, and serve features for production AI/ML.

Feast helps ML platform/MLOps teams with DevOps experience to productionize real-time models. It also helps these teams build a feature platform that improves collaboration between data engineers, software engineers, machine learning engineers, and data scientists.

MLflow

MLflow is an open-source platform, purpose-built to assist machine learning practitioners and teams in handling the complexities of the machine learning process. MLflow focuses on the full lifecycle for machine learning projects, ensuring each phase is manageable, traceable, and reproducible.

It also contains a registry that allows developers to centralize their model stores for lifecycle tracking and versioning.

Comet

Comet tracks ML experiments and provides insights into model performance. It helps you evaluate LLMs and monitor your models in production.

With its powerful dashboard, teams can get visual insights to analyse model performance.

Prometheus

It is a great analytics tool for real-time infrastructure and machine learning deployment monitoring. Creating custom dashboards lets developers gain insights into their infrastructure.

Apache Airflow

Airflow helps you automate, monitor, and schedule machine learning pipelines and workflows. Workflows are created using Python and are built to handle large and complex workflows.

Airflow has various operators and lists that integrate with any system or process you are working on.

BentoML

BentoML simplifies model packaging and deployment into production. It helps teams deploy machine learning models at scale and build production-level AI systems.

Seldon

Seldon Core is a native Kubernetes tool for deploying and monitoring machine learning models. It addresses all the complexities of Kubernetes, allowing your engineers to deploy the model at scale without having expertise in how to deploy Kubernetes.

DVC

Data Version Control ensures reproducibility by tracking your dataset, codes, and experiments using GitOps principles. Its Git-like interface integrates easily with all your GIT repositories.

Kubeflow

Kubeflow handles all the complexities of containerization and supports end-to-end pipeline automation and distributed training on large data sets, making it ideal for production-grade machine learning systems.

And there you have it: 10 Open-source AI/ML platform engineering tools. Whether you are building scalable pipelines, tracking experiments, or deploying models in production, tools like KitOps can tackle the complexities of machine learning projects and model development while keeping your workflow efficient, user-friendly, and robust.