Metaflow
vs
MLflow
Metaflow
Metaflow is an open-source framework designed to facilitate the development and management of real-world machine learning, AI, and data science projects. It allows users to leverage any Python library, and handles library dependencies both locally and in the cloud.
The framework provides robust workflow orchestration in Python, automatic versioning of variables, and seamless integration with cloud computing resources. Metaflow supports easy deployment of workflows to production and offers scalability for computationally intensive tasks, such as those using GPUs and extensive memory.
MLflow
MLflow is a unified, open-source MLOps platform designed to streamline the entire machine learning and generative AI lifecycle. It provides a comprehensive solution for managing workflows, from initial development stages to final production deployment.
MLflow offers a range of tools for experiment tracking, model management, and deployment, supporting both traditional machine learning and generative AI applications. Its open-source nature allows integration with a wide variety of ML libraries and platforms, making it highly adaptable to different environments and projects.
Metaflow
Pricing
MLflow
Pricing
Metaflow
Features
- Modeling: Use any Python libraries for models and business logic, with managed dependencies.
- Deployment: Deploy workflows to production with a single command, integrating with other systems via events.
- Versioning: Automatic tracking and storage of variables for experiment tracking and debugging.
- Orchestration: Create robust workflows in Python, develop and debug locally, then deploy without changes.
- Compute: Leverage cloud resources for scalable function execution, including GPUs and large memory.
- Data: Access and version data from data warehouses, with data flowing across workflow steps.
MLflow
Features
- Experiment tracking: Visualization and tracking of project progress.
- Generative AI: Tools for improving generative AI quality and building applications with prompt engineering.
- Observability: Enhance LLM observability with tracing.
- Evaluation: Evaluating traditional ML and Generative AI, including Retrieval Augmented Generation applications.
- Models: Package and deploy models.
- Model Registry: Manage models.
- Serving: Securely host LLMs at scale with MLflow Deployments.
Metaflow
Use cases
- Developing and deploying machine learning models.
- Managing and tracking data science experiments.
- Orchestrating complex data workflows.
- Scaling computational tasks using cloud resources.
- Creating reactive production systems triggered by real-time events.
- Training and fine-tuning large language models.
MLflow
Use cases
- Developing and deploying machine learning models.
- Building and managing generative AI applications.
- Tracking and visualizing experiment results.
- Evaluating the performance of LLMs.
- Managing the complete ML lifecycle, from experimentation to production.
Metaflow
Uptime Monitor
Average Uptime
99.76%
Average Response Time
165.83 ms
Last 30 Days
MLflow
Uptime Monitor
Average Uptime
99.34%
Average Response Time
236.92 ms
Last 30 Days
Metaflow
MLflow
