Chart
Developer Tools
Maximize Your Reach: Unleashing the Potential of Promote Your Tool
That's where Chart comes in. Chart is a new startup that aims to simplify the ML deployment process. Chart packages your models into high-performant C++ servers and deploys them into your own cloud account. You don't need to worry about the complexities of MLOps, such as model conversion, optimization, testing, monitoring, and scaling. Chart handles all of that for you.
How does Chart work? Chart transforms your ML models into CUDA/HIP optimized C++ code for lightning-fast inference. You can use any framework or library to train your models, such as TensorFlow, PyTorch, or Scikit-learn. Then, you simply integrate your cloud provider (such as AWS, Azure, or Google Cloud) and Chart deploys fast, auto-scaling inference servers in your own cloud account. You can access your models via REST API or gRPC.
What are the benefits of using Chart? Chart offers several advantages over other ML deployment solutions:
- Speed: Chart uses C++ and CUDA/HIP to optimize your models for GPU/CPU inference. This can result in up to 10x faster inference than Python-based solutions.
- Cost: Chart deploys your models in your own cloud account, so you only pay for what you use. You can also leverage spot instances or preemptible VMs to reduce your cloud costs even further.
- Control: Chart gives you full control over your ML deployment. You can monitor your model performance, logs, and metrics via a web dashboard. You can also update or rollback your models with a single click.
- Security: Chart ensures that your models and data are secure and compliant. You can use encryption, authentication, and authorization to protect your API endpoints. You can also use custom domains and SSL certificates to enable HTTPS.
Chart is currently in beta and accepting requests for early access. If you are interested in trying out Chart for your ML deployment needs, you can sign up on their website: https://www.getcharteditor.com/
Pros
- It allows users to deploy high-performant ML models with ease.
- It transforms ML models into CUDA/HIP optimized C++ code for lightning-fast inference.
- It deploys fast, auto-scaling inference servers in the user's own cloud account.
- It abstracts away all the complexities of MLOps.
- It is backed by Y Combinator, a prestigious startup accelerator.
Cons
- It is still in beta stage and requires access request.
- It may not support all types of ML models or cloud providers.
- It may have bugs or security issues that are not yet discovered or fixed.
- It may have higher costs than other alternatives depending on the usage and cloud provider.
- It may face competition from other similar services or products in the market.