BigML
BigML is a comprehensive machine learning platform that provides a programmable, scalable, and automated environment for building and deploying predictive models across various business applications and industries.
Keras
Keras is a high-level deep learning API developed for humans that enables you to build, train, and deploy machine learning models with speed and simplicity across multiple frameworks.
Quick Comparison
| Feature | BigML | Keras |
|---|---|---|
| Website | bigml.com | keras.io |
| Pricing Model | Freemium | Free |
| Starting Price | Free | Free |
| FREE Trial | ✘ No free trial | ✘ No free trial |
| Free Plan | ✓ Has free plan | ✓ Has free plan |
| Product Demo | ✓ Request demo here | ✓ Request demo here |
| Deployment | ||
| Integrations | ||
| Target Users | ||
| Target Industries | ||
| Customer Count | 0 | 0 |
| Founded Year | 2011 | 2015 |
| Headquarters | Corvallis, USA | Mountain View, USA |
Overview
BigML
BigML provides you with a unified platform to build, share, and operationalize machine learning models without needing a PhD in data science. You can import your data and immediately start generating insights through an intuitive interface that handles everything from data preprocessing to model deployment. Whether you are working on classification, regression, or cluster analysis, the platform automates the heavy lifting of algorithm selection and parameter tuning.
You can integrate predictive capabilities directly into your applications using their extensive API or execute complex workflows with their domain-specific language, WhizzML. The platform is designed to scale with your needs, supporting everything from small experimental datasets to massive enterprise-grade data processing. It solves the common problem of the 'last mile' in machine learning by making it easy to turn a trained model into a live, functional web service.
Keras
Keras is a deep learning framework designed to reduce your cognitive load when building complex neural networks. It acts as a high-level interface that runs on top of powerful backends like TensorFlow, JAX, or PyTorch, allowing you to switch between them seamlessly based on your project needs. You can move from an initial idea to a functional model faster because the syntax is consistent, readable, and minimizes the number of user actions required for common tasks.
Whether you are a researcher developing new deep learning layers or an engineer deploying models to production, Keras provides the tools to scale your work. You can run your code on CPUs, GPUs, or TPUs without changing your implementation. It is widely used across industries for tasks like image recognition, natural language processing, and forecasting, making it a versatile choice for teams that value developer experience and rapid iteration.
Overview
BigML Features
- Automated Machine Learning Find the best performing models automatically with OptiML, which iterates through various algorithms and parameters for you.
- WhizzML Automation Automate complex machine learning workflows and create repeatable processes using a dedicated domain-specific language.
- Visual Model Interpretation Understand your data better with interactive visualizations of decision trees, ensembles, and clusters that reveal hidden patterns.
- Real-time Predictions Turn your models into immediate web services to generate instant predictions for your web or mobile applications.
- Image Processing Expand your capabilities by training models on image data for visual recognition and classification tasks directly.
- Time Series Forecasting Predict future trends and seasonal patterns in your data with specialized tools for temporal data analysis.
Keras Features
- Multi-Backend Support. Choose the best engine for your task by running your Keras code on JAX, TensorFlow, or PyTorch without rewriting anything.
- Sequential Model API. Create simple stacks of layers quickly for standard deep learning architectures where each layer has exactly one input and output.
- Functional API. Build complex model topologies including multi-output models, directed acyclic graphs, and models with shared layers for advanced research.
- Keras Tuner. Automate the search for the best hyperparameters in your deep learning models to achieve higher accuracy with less manual effort.
- Built-in Preprocessing. Prepare your raw images, text, and structured data for training directly within your model pipeline for easier deployment.
- Mixed Precision Training. Speed up your training times and reduce memory usage by using 16-bit floating-point types on modern GPU and TPU hardware.
Pricing Comparison
BigML Pricing
- Up to 16MB per task
- 2 concurrent tasks
- Unlimited datasets
- Unlimited models
- Access to BigML Gallery
- Everything in FREE, plus:
- Up to 1GB per task
- 8 concurrent tasks
- Priority task execution
- Private model hosting
- Full API access
Keras Pricing
- Full API access
- Commercial usage allowed
- Community-led support
- Multi-backend compatibility
- Regular security updates
- Access to Keras Ecosystem
- Everything in Open Source, plus:
- Public GitHub issue tracking
- Extensive documentation
- Community discussion forums
- Open-source contributions
- Pre-trained model library
Pros & Cons
BigML
Pros
- Intuitive web interface simplifies complex data science tasks
- Excellent documentation and educational resources for beginners
- Powerful API makes integration into existing apps easy
- Visualizations help explain model logic to stakeholders
- Flexible pricing allows for low-cost experimentation
Cons
- Interface can feel dated compared to newer tools
- Advanced users may find visual tools slightly limiting
- Large dataset processing can become expensive quickly
Keras
Pros
- Extremely flat learning curve for beginners
- Excellent documentation and massive community support
- Consistent and simple API reduces coding errors
- Seamless integration with the TensorFlow ecosystem
Cons
- Debugging custom layers can be challenging
- Higher-level abstractions may limit low-level control
- Performance overhead compared to pure low-level code