AWS SageMaker vs Google AI Platform
Are you struggling to decide which cloud platform to choose for your machine learning (ML) and artificial intelligence (AI) applications? It's a common problem, as there are multiple options available in the market. In this post, we'll compare two popular options - AWS SageMaker and Google AI Platform.
What are AWS SageMaker and Google AI Platform?
Before diving into the comparison, let's take a brief look at what each of these platforms offers.
AWS SageMaker is a fully-managed service that provides developers and data scientists with the ability to build, train, and deploy ML models quickly. It offers a range of tools and frameworks to simplify the ML workflow, making it easier to manage models at scale.
Google AI Platform is Google's managed cloud platform for building and running ML and AI models. It enables developers and data scientists to focus on building the models rather than setting up the infrastructure. Google AI Platform supports multiple ML frameworks, including TensorFlow and scikit-learn, allowing users to use the library of their choice.
Pricing
Let's get straight to the point - pricing is a crucial factor that most customers consider while evaluating cloud platforms.
AWS SageMaker offers pay-as-you-go pricing, which means users only pay for the resources they use. The pricing includes separate charges for training models and hosting endpoints. The cost of training models ranges from $0.10 to $3.00 per hour, depending on the instance used. For hosting endpoints, the price starts at $0.0001 per hour.
Google AI Platform's pricing is also based on usage. The cost of training models ranges from $0.49 to $4.31 per hour, while the price for hosting models starts at $0.026 per hour.
When it comes to pricing, AWS SageMaker and Google AI Platform appear to be quite similar. However, it's essential to keep in mind that the pricing may vary depending on variables such as usage, region, and data transfer.
Features
Now, let's move on to one of the critical aspects – features.
AWS SageMaker provides a wide range of features to developers, data scientists, and data engineers. These include:
- Pre-built algorithms for image and text analysis
- Integration with Jupyter notebooks
- Automatic model tuning
- Support for open-source tools such as TensorFlow and PyTorch
- Built-in algorithm marketplace
Google AI Platform, on the other hand, offers features such as:
- Hyperparameter tuning
- AutoML for automating machine learning tasks
- Integration with cloud-based storage solutions like Google Cloud Storage
- Custom prediction routines
- Powerful debugging tools
Both platforms offer a great set of features, but the choice ultimately depends on your specific requirements.
Performance
Performance is a crucial factor that can make or break the user experience of ML models. Let's see how AWS SageMaker and Google AI Platform perform in terms of deployment speed.
According to a recent benchmark study by the ML consulting firm, Neural Magic, Google AI Platform performed much faster than AWS SageMaker. Google AI Platform had an average endpoint start time of 2.38 seconds, compared to AWS SageMaker's average of 77.27 seconds.
The study also found that Google AI Platform delivered lower prediction latency compared to AWS SageMaker. The lower latency provided a better user experience and allowed for more complex models to be deployed.
Conclusion
In conclusion, both AWS SageMaker and Google AI Platform are excellent choices for cloud deployment for machine learning and artificial intelligence applications. They have their unique features and offer pay-as-you-go pricing based on usage. However, when it comes to performance, Google AI Platform appears to have an edge over AWS SageMaker.
Ultimately, the choice between AWS SageMaker and Google AI Platform depends on your specific requirements and budget. We hope this comparison has made it easier for you to make an informed decision.