MLFlow is a framework that supports the lifecycle of machine learning. It indicates that it provides components to monitor your model during training and execution, the ability to save models, load the model into production code, and establish a pipeline.

For many data scientists, platforms like MLflow have become the go-to choice for managing the machine learning lifecycle, guaranteeing a smooth transition and experience. It is a popular open-source ML lifecycle management framework. It includes deployment, experimentation, repeatability, and a central model registry.

Among others, organisations, including Facebook, Databricks, Microsoft, Accenture, and Booking.com, employ MLflow. The platform is independent of libraries. It provides a collection of simple APIs that we may use with any machine learning programme or library already in existence, such as TensorFlow, PyTorch, XGBoost, etc. Laptops, stand-alone programmes, or the cloud can utilise it.

This article will examine some of the interesting MLflow alternatives and review each one's features and capabilities to assist you.

Neptune

Neptune is an MLOps metadata repository. It helps Data Scientists and ML Engineers track experiments and model registries.

The platform includes:

• Log, present, organise and evaluate machine learning experiment data
• Version, store, manage, and query trained models and model-building metadata in a model registry
• Live recording and monitoring of machine learning model training, evaluation, and production runs

Amazon Sagemaker

Amazon SageMaker manages all machine learning (ML) development phases, including model registry. The SageMaker model registry lets you catalogue production models, maintain model versions, associate metadata like training metrics, and manage model approval status.

Amazon SageMaker generates a model version and group by registering a model. An inference pipeline with containers and variables can be registered. Using the AWS Python SDK, create new version models.

Kubeflow

Kubeflow facilitates simple, portable, and scalable deployments of machine learning (ML) processes on Kubernetes. The platform provides a simplified method for deploying the top open-source machine learning algorithms on heterogeneous infrastructures. It is a machine-learning toolbox for Kubernetes.

Verta AI

Verta AI manages and deploys machine learning models in a centralised model registry.

In addition to version control tools for ML projects, the Verta AI system permits tracking of changes in code, data, configuration, and environment. You can consult the audit record to verify the model's compliance and robustness. We can utilise this platform during the whole life cycle of a model.

Comet

Comet is a self-hosted and cloud-based meta-machine learning platform that enables data scientists to monitor, compare, explain, and optimise experiments and models. Comet, backed by users and Fortune 500 firms such as Uber, Autodesk, Boeing, Hugging Face, AssemblyAI, and others, delivers data and insights to develop more robust, more accurate AI/ML models while enhancing team productivity, collaboration, and visibility. 

Azure Machine Learning

Azure Machine Learning is a cloud-based MLOps platform that automates and simplifies the whole ML lifecycle, including model maintenance, deployment, and monitoring. In addition, azure includes the subsequent MLOps capabilities:

• Create replicable ML pipelines.
• Create software environments that are reusable for training and deploying models.
• Register models, package them, and deploy them from anywhere.
• Data governance for the comprehensive ML lifecycle.
• Notify and notify on ML lifecycle events.
• Monitor ML apps for ML-related and operational concerns.
• Automate the ML lifecycle from beginning to end with Azure Machine Learning and Azure Pipelines.

ModelDB

ModelDB is an open-source platform for managing ML model versions, information, and experiments. ModelDB helps make your machine-learning models reproducible. In addition, it enables you to organise your machine learning experiments, creates performance dashboards, and share reports. Finally, it tracks models throughout their lifecycle, including development, deployment, and real-time monitoring.