How we create and deploy trained model APIs in production is governed by many aspects of the machine learning lifecycle. The concept of MLOps is very beneficial for dealing with complex ML deployment environments.
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Implementing solid MLOps can bring huge benefits to companies investing in machine learning. Knowing what to use and execute is an important piece of the puzzle. Learning and adapting to new tools that streamline your entire workflow is another.
This article lists the best tools for model deployment. Helps you scale and manage all elements of the machine learning lifecycle, including serving, monitoring, and managing API endpoints.
1、Seldon.io
Seldon.io provides Seldon core, an open source framework. The framework simplifies and accelerates ML model deployment. Seldon processes and serves models built in any other open source ML framework. ML models are deployed in Kubernetes. As it scales with Kubernetes, it enables us to use state-of-the-art Kubernetes features such as custom resource definitions for working with model graphs.
Seldon also provides functionality to connect projects with continuous integration and deployment (CI/CD) tools to extend and update model deployments. It has an alert system that will notify you if there is a problem monitoring the model in production. Models can be defined to explain certain predictions. The tool is available in the cloud and on-premises.
Advantages of Seldon:
- Customize offline models.
- Expose API real-time forecasts to external clients.
- Simplify the deployment process.
Disadvantages of Seldon:
- Setup can be a bit complicated.
- Learning can be difficult for newcomers.
2、BentoML
BentoML simplifies the process of building machine learning services. It provides a standard Python-based framework for deploying and maintaining production-grade APIs. The architecture allows users to easily package trained models using any ML framework for online and offline model serving.
BentoML's high-performance model server supports adaptive micro-batching and the ability to scale model inference workers independently of business logic. The UI Dashboard provides a centralized system to organize models and monitor the deployment process.
Its modular design makes configuration reusable with existing GitOps workflows, and automatic docker image generation makes deploying to production an easy and versioned process.
The multipurpose framework addresses serving, organizing, and deploying ML models. The main focus is to connect data science and DevOps departments to provide a more efficient working environment and generate high-performance scalable API endpoints.
Advantages of BentoML:
- A practical format for easily deploying predictive services at scale
- Enables high-performance model serving and deployment in a single unified format
- Supports model deployment to multiple platforms, not just Kubernetes
BentoML Disadvantages:
- Not focused on experiment management.
- Does not handle horizontal scaling out of the box.
3、TensorFlow Serving
If you want to deploy the trained model as an endpoint, you can use TensorFlow Serving to do so. It allows you to create a REST API endpoint that will serve the trained model.
TensorFlow Serving is a powerful, high-performance system for serving machine learning models. You can easily deploy state-of-the-art machine learning algorithms while maintaining the same server architecture as their respective endpoints. It is powerful enough to serve different types of models and data as well as TensorFlow models.
It was created by Google and is used by many top companies. Serving models as a centralized model repository is a good approach. The serving architecture is efficient enough to allow a large number of users to access the model simultaneously.
If there is blocking due to a large number of requests, it can be easily maintained using a load balancer. Overall, the system has good scalability, maintainability and high performance.
Advantages of TensorFlow Serving:
- Once the deployment model is ready, the tool makes it easy to serve it.
- It can issue batches of requests to the same model, making efficient use of hardware.
- It also provides model version management.
- The tool is easy to use and takes care of model and service management.
Disadvantages of TensorFlow Serving:
- There is no way to ensure zero downtime when loading new models or updating old ones.
- Only available for TensorFlow models.
4、Kubeflow
The main goal of Kubeflow is to maintain machine learning systems. It is a powerful suite designed specifically for Kubernetes. The main operations include packaging and organizing docker containers to help maintain the entire machine learning system.
Kubeflow simplifies the development and deployment of machine learning workflows, enabling model traceability. It provides a powerful set of ML tools and architectural frameworks to efficiently perform various ML tasks.
The versatile UI dashboard makes it easy to manage and track experiments, tasks and deployment runs. Notebook functionality enables us to interact with ML systems using specified platform development kits.
Components and pipelines are modular and can be reused to provide quick solutions. The platform was started by Google to serve TensorFlow tasks via Kubernetes. It later expanded to a multi-cloud, multi-architecture framework that executes entire machine learning pipelines.
Advantages of Kubeflow:
- A consistent infrastructure that provides monitoring, health checks, every replication, and extension with new features.
- Simplify the onboarding process for new team members.
- Standardizing processes helps build security and better control over the infrastructure.
Disadvantages of Kubeflow:
- Difficult to set up and configure manually.
- High availability is not automatic and requires manual configuration.
- The learning curve for this tool is steep.
5、Cortex
Cortex is an open source multi-framework tool that is flexible enough to be used as a model serving tool and for purposes such as model monitoring.
With its ability to handle different machine learning workflows, it gives you full control over model management operations. It can also serve as an alternative to serving models using the SageMaker tool, as well as a model deployment platform based on AWS services such as Elastic Kubernetes Service (EKS), Lambda, or Fargate.
Cortex extends to open source projects such as Docker, Kubernetes, TensorFlow Serving, and TorchServe. It can be combined with any ML library or tool. It provides scalability of endpoints to manage load.
It allows you to deploy multiple models in a single API endpoint. It also acts as a solution to update production endpoints without stopping the server. It covers the footprint of model monitoring tools, monitoring the performance of endpoints, and forecasting data.
Advantages of Cortex:
- Autoscaling ensures that APIs remain secure during fluctuating network traffic.
- Support Keras, TensorFlow, Scikit-learn, PyTorch and other platforms.
- No downtime is required to update models.
Cortex disadvantages:
- The setup process can be a little daunting.
6、AWS Sagemaker
AWS Sagemaker is a powerful service from Amazon. It enables machine learning developers to quickly build, train, and deploy machine learning models.
It simplifies the entire machine learning process by removing some complex steps, thus providing highly scalable machine learning models.
The machine learning development lifecycle is a complex iterative process. It forces you to integrate complex tools and workflows. This task can be demanding and irritating, and it can consume a lot of your time. Not to mention the hassle of getting things wrong while configuring.
Sagemaker makes this process easier, providing all the components for machine learning in a centralized toolset. There is no need to configure everything, as it is already installed and ready to use.
This can speed up the production and deployment of models with minimal effort and cost. The tool can be used with endpoints created using any machine learning framework. It also provides forecast tracking and capture as well as schedule monitoring.
Advantages of AWS Sagemaker:
- It's easy to set up and run with Jupyter Notebook. Thus, the management and deployment of scripts is simplified.
- Costs are modular, depending on which features you use.
- Model training is done on multiple servers.
AWS Sagemaker Disadvantages:
- The learning curve for junior developers is steep.
- Rigid workflows make customization difficult.
- Only available in the AWS ecosystem
7、MLflow
If you're looking for an open source tool to organize the entire machine learning lifecycle, then MLflow might be the right platform for you.
MLflow provides solutions for managing ML processes and deployments. It can be experimented with, reproduced, deployed, or become a central model registry.
The platform can be used by individual developers and teams for machine learning deployments. It can be incorporated into any programming ecosystem. The library is built to meet various technical needs and can be used with different machine learning libraries.
Organize the entire machine learning lifecycle around four main functions: Tracking, Projects, Models, and Model Registry.
It helps simplify the process of automating ML model tracking. But one of its disadvantages is that it cannot handle model definition automatically. This means that extra work needs to be added manually to the model definition.
Advantages of MLflow:
- The model tracking mechanism is easy to set up.
- It provides very intuitive API services.
- Logging is practical and simplified, so it's easy to experiment.
- A code-first approach.
MLflow Disadvantages:
- Adding extra work to the model is not automatic.
- Not as easy, but great for deploying models to different platforms.
8、Torchserve
Torchserve is a Pytorch model serving framework. It simplifies large-scale deployment of trained PyTorch models. It eliminates the need to write custom code for model deployment.
Torchserve is designed by AWS and provided as part of the PyTorch project. This makes for easy setup for those building models using the PyTorch environment.
It supports lightweight services with low latency. The deployed model has high performance and wide scalability.
Torchserve has built-in libraries for certain ML tasks, such as object detection or text classification. It saves you time writing code. It provides powerful features such as multi-model serving, model versioning for A/B testing, metrics for monitoring, and RESTful endpoints for application integration.
Advantages of Torchserve:
- Simplifies scaling of deployment models.
- Service endpoints are lightweight and scale with high performance.
Torchserve Cons:
- Since the tool is experimental, changes and updates are frequent.
- Only available for PyTorch models
Original Link: 8 Best ML Model Deployment Tools—BimAnt