BigML vs H2O: What are the differences?

# BigML vs. H2O

BigML and H2O are both popular machine learning platforms, but they have key differences that set them apart. In this comparison, we will highlight the top 6 differences between BigML and H2O.

1. **Model Interpretability**: BigML provides built-in model explanations and interpretations, which can help users understand how a model makes predictions and the importance of different features. H2O, on the other hand, requires users to use additional tools or techniques for model interpretability.

2. **Scalability**: H2O is known for its scalability, especially when dealing with large datasets and complex models. It can efficiently handle big data and parallel processing, making it a preferred choice for users working with massive amounts of data. BigML, while efficient, may struggle with extremely large datasets and resource-intensive computations.

3. **Deployment Options**: BigML offers a cloud-based platform for model deployment and hosting, simplifying the process for users to deploy their models into production. H2O, on the other hand, provides users with more flexibility by supporting on-premises deployments and integration with other cloud platforms.

4. **Community Support**: H2O has a strong open-source community, with active contributors and user forums where users can seek help and share knowledge. BigML, while also having a supportive community, may not have as extensive resources and community engagement as H2O.

5. **Automated Machine Learning (AutoML)**: BigML has a robust AutoML feature that automates the model building process, making it easier for users to quickly generate and evaluate models. H2O also has AutoML capabilities, but users may need to fine-tune the process and parameters more compared to BigML's user-friendly interface.

6. **Cost Structure**: BigML offers a pay-per-usage pricing model, allowing users to pay for the resources they consume. On the other hand, H2O follows a subscription-based pricing model, which may be more cost-effective for users who require frequent access to advanced features.

In Summary, BigML and H2O have distinct differences in terms of model interpretability, scalability, deployment options, community support, AutoML capabilities, and cost structure.