BigML vs TensorFlow: What are the differences?

Introduction: BigML and TensorFlow are two popular machine learning platforms used for building and deploying machine learning models.

1. Deployment and Scalability: BigML provides a user-friendly interface for deploying and scaling machine learning models without the need for deep technical expertise, making it ideal for users with limited programming knowledge. On the other hand, TensorFlow is more suitable for advanced users looking to implement custom algorithms and complex neural network architectures that require a high degree of customization and scalability.

2. Model Interpretability: BigML offers built-in interpretability features such as local and global explanations, which help users understand how the model makes predictions. In contrast, TensorFlow prioritizes performance and flexibility over interpretability, requiring users to implement their own interpretability methods or rely on additional tools and libraries.

3. Ease of Use: BigML emphasizes ease of use and simplicity, with a focus on streamlining the machine learning process by automating many of the tasks involved in model building. TensorFlow, on the other hand, provides a more hands-on and customizable approach, giving users more control over the entire machine learning pipeline.

4. Community Support: TensorFlow has a large and active community of developers and researchers who contribute to the platform's continuous improvement, share resources, and provide support to other users. BigML, while also having a supportive community, may not offer as extensive resources and community engagement compared to TensorFlow.

5. Cost Structure: BigML offers a pay-as-you-go pricing model, allowing users to access its platform at a relatively lower cost compared to other machine learning platforms. In contrast, TensorFlow is open-source and free to use, but users may incur additional costs for cloud computing resources when deploying models at scale.

6. Use Cases: BigML is well-suited for users who prioritize ease of use, model interpretability, and quick deployment of machine learning models for business applications such as predictive analytics and decision-making. TensorFlow, on the other hand, is ideal for researchers, data scientists, and developers working on cutting-edge research projects, sophisticated neural network designs, and large-scale deep learning applications where customization and performance are critical.

In Summary, BigML is a user-friendly platform suitable for quick model deployment and interpretability, while TensorFlow is a more customizable and scalable platform favored by advanced users for complex machine learning tasks.