Deploying machine learning models can be a challenging task, but it is a critical step towards making them useful. Without proper deployment, a machine learning model is nothing but lines of code. Fortunately, there are many tools and techniques available that can help in deploying machine learning models effectively. In this article, we will explore some of the best tools and techniques that can be used to deploy machine learning models.
Machine Learning Models: Tools
1. TensorFlow Serving
Deploying your trained machine learning model as an endpoint is easy with TensorFlow Serving. With this powerful and reliable system, you can create a REST API endpoint to serve your models to users. It's designed to handle different types of models and data, including state-of-the-art machine learning algorithms and TensorFlow models.
As it was created by Google, it's trusted by many top companies. Using a centralized model base to serve your models is an efficient way to make them accessible to a large pool of users simultaneously. Try TensorFlow Serving for a seamless deployment experience.
Advantages:
This tool allows easy serving once the deployment models are ready.
It can initiate batch requests to the same model, so hardware is used efficiently.
It offers model versioning management as well.
ÂThe tool is easy to use and takes care of model and serving management.
Disadvantages:
There is no way to ensure zero downtime when new models are loaded or old ones are updated.
Works only with TensorFlow models.
2. MLflow
Looking for a tool to streamline your entire Machine learning lifecycle? Look no further than MLflow! With solutions for experimentation, reproducibility, deployment, and model registry, this open-source platform can be used by individual developers or teams. Plus, it can be integrated into any programming ecosystem and works with a variety of Machine learning libraries. Keep your ML process organized with MLflow's four main functions: Tracking, Projects, Models, and Model Registry.
Advantages:
The model tracking mechanism is easy to set up.
It offers very intuitive APIs for serving.
The logging is practical and simplified, so it’s easy to run experiments.
Code-first approach.
Disadvantages:
The addition of extra workings to the models is not automatic.
Not quite easy and ideal for deploying models to different platforms.
3. Cortex
Cortex is a versatile open-source tool for managing machine learning models, including serving and monitoring. With its multi-framework support, it gives you full control over model management operations and serves as an alternative to SageMaker for model deployment on top of AWS services.
Cortex integrates with Docker, Kubernetes, TensorFlow Serving, and TorchServe, and can work with any ML library or tool. It provides endpoint scalability to handle heavy loads and enables deploying multiple models in a single API endpoint.
Additionally, Cortex allows updating production endpoints without any downtime and includes model monitoring capabilities to supervise endpoint performance and prediction data.
Advantages:
An auto-scaling feature that allows APIs to be secure when network traffic fluctuates.
Support for multiple platforms such as Keras, TensorFlow, Scikit-learn, PyTorch, etc.
No downtime when models are being updated.
Disadvantages:
The setup process can be somewhat daunting.
4. Seldon.io
Simplify and accelerate your ML models and experiment deployment with Seldon core - an open-source framework by Seldon.io. With Kubernetes as its base, it allows scaling and customization of resource definitions to handle model graphs. Connect your project with CI/CD tools for seamless model deployment updates and receive alerts via its monitoring system. Available both on the cloud and on-premise.
Advantages:
Custom offline models.
Real-time predictions exposing APIs to external clients.
Simplifies the deployment process.
Disadvantages:
The setup can be a bit complex.
It can be difficult to learn for newcomers.
5. AWS Sagemaker
Amazon Web Services (AWS) Sagemaker is a powerful and scalable service that enables ML developers to build, train, and deploy machine learning models quickly. It simplifies the entire ML process by eliminating some of the complex steps, providing hassle-free scalability, and reducing development time.
Developing an ML model can be a complex and time-consuming process, often involving intricate tools and workflows that can be difficult to configure. AWS Sagemaker provides a centralized toolset that includes all the components needed for machine learning, which eliminates the need for configuration, saves time, and reduces costs.
With Sagemaker, you can accelerate model production and deployment with minimal effort and cost, as it can be used with any ML framework. It also provides prediction tracking and capture, as well as schedule monitoring, making it easier to manage the entire ML lifecycle.
Advantages:
The setup process is simple and can run with Jupyter Notebook. Hence, the management and deployment of scripts are simplified.
The cost is modular, based on the feature you use.
Model training is done on multiple servers.
Disadvantages:
The steep learning curve for junior developers.
Strict workflows make it hard to customize.
Works only with the AWS ecosystem.
6. Torchserve
Torchserve is a powerful PyTorch model-serving framework that simplifies the deployment of trained models at scale. Developed by AWS and available as part of the PyTorch project, it removes the need to write custom code for model deployment, making it easy for PyTorch users to set up. With built-in libraries for some ML tasks, it delivers high-performance, low-latency serving and enables multi-model serving, model versioning for A/B testing, metrics for monitoring, and RESTful endpoints for application integration.
Advantages:
Scaling deployed models is simplified.
Serving endpoints are lightweight with a high-performance scale.
Disadvantages:
Changes and updates happen often because the tool is experimental.
Works only with PyTorch Models.
Which Tool to choose?
Here is the difference between the tools which we have discussed above:
Factor | TensorFlow Serving | MLflow | Cortex | Seldon.io | AWS SageMaker | TorchServe |
|---|---|---|---|---|---|---|
Primary Use Case | Deployment of trained TensorFlow models | Experiment tracking, model packaging and deployment | Deployment and serving of machine learning models | Deployment of machine learning models at scale | End-to-end ML development, deployment and management | Deployment of PyTorch models |
Language Support | C++, Python, Java, Go | Python, R, Java, Scala | Python, Go | Python | Multiple languages including Python, R, Java, Scala, etc. | Python |
Deployment Methods | REST API, gRPC | REST API, CLI, Python API | REST API, CLI, Python API | REST API, gRPC | REST API, CLI, Python API | REST API, CLI, Python API |
Model Serving Features | TensorRT, GPUs, Multi-model Serving, etc. | Model registry, model packaging, experiment tracking, model versioning, etc. | Auto-scaling, canary deployment, traffic splitting, etc. | Advanced algorithms and metrics, model versioning, canary testing, etc. | Auto-scaling, distributed training, hyperparameter optimization, etc. | Custom handlers, model versioning, GPU support, etc. |
Framework Support | TensorFlow only | Any Framework | Any Framework | Any Framework | Any Framework | PyTorch only |
Model Registry | No | Yes | No | Yes | Yes | No |
Model Monitoring | No | No | Yes | Yes | Yes | No |
Model Optimization | No | No | No | Yes | No | No |
Model Explainability | No | No | No | No | Yes | No |
Machine Learning Models: Techniques
Here are some of the techniques which you can use to deploy machine learning models more efficiently.
1 .RESTfulAPI
This technique involves creating a RESTful API that exposes the model’s functionality over the internet. A RESTful API is a web service that follows the principles of Representational State Transfer (REST), which is a standard for designing and interacting with web resources. It can accept requests from clients, such as web browsers or mobile apps, and return responses in a standardized format, such as JSON or XML. A RESTful API can be implemented using various frameworks and languages, such as Flask, Django, FastAPI, etc. in Python, or Spring Boot, Express, etc. in Java or JavaScript. This technique is suitable for scenarios where the model needs to provide real-time or on-demand predictions to a large number of users or applications over the internet.
2. Cloud Services
It involves using cloud services that provide end-to-end solutions for building, training, deploying, and managing machine learning models. Some examples of cloud services are Amazon SageMaker, Google Cloud AI Platform, Azure Machine Learning, etc. These cloud services can provide various features and benefits, such as pre-built algorithms and frameworks, distributed training and inference, model versioning and monitoring, auto-scaling and load balancing, security and compliance, etc.2 This technique is suitable for scenarios where the model needs to leverage the power and convenience of cloud computing without worrying about the technical details.
3. AutoML
It involves using automated machine learning (AutoML) tools that can automate the entire machine learning pipeline, from data preparation to model deployment. Some examples of AutoML tools are Google Cloud AutoML, Microsoft Azure AutoML, H2O Driverless AI, etc. These AutoML tools can provide various features and benefits, such as data cleaning and preprocessing, feature engineering and selection, hyperparameter tuning and optimization, model selection and evaluation, model deployment and management, etc. This technique is suitable for scenarios where the model needs to be built quickly and easily without requiring much expertise or effort from the user.
4. Containerization
This technique involves packaging the model and its dependencies into a container, such as a Docker container. A container is a standalone unit of software that can run on any platform or environment that supports containerization. It can isolate the model from the underlying infrastructure and ensure its portability and reproducibility.
A container can also be deployed on various platforms or services that support container orchestration, such as Kubernetes, Docker Swarm, Amazon ECS, etc. This technique is suitable for scenarios where the model needs to run in a consistent and controlled environment across different machines or clouds.
5. Serverless
This technique involves deploying the model on a serverless platform, such as AWS Lambda or Google Cloud Functions. A serverless platform is a cloud service that provides on-demand execution of code without requiring the user to manage servers or infrastructure. A serverless platform can scale up or down automatically based on demand and only charge for the resources used.
A serverless platform can also integrate with other cloud services, such as Amazon S3, Google Cloud Storage, etc., to store and access data. This technique is suitable for scenarios where the model needs to handle sporadic or unpredictable requests with low latency and high availability.
6. Batch Processing
A batch processing platform is a distributed computing system that can process large volumes of data in batches or chunks. It can run the model on multiple nodes or clusters and parallelize the computation and communication. It can also store and access data from various sources or formats, such as files, databases, streams, etc.
A batch processing platform can be programmed using various frameworks and languages, such as PySpark, Spark MLlib, Scikit-learn, etc. This technique is suitable for scenarios where the model needs to provide offline or periodic predictions to users or applications based on historical or aggregated data.
7. Embedded System
It involves deploying the model on an embedded system, such as a microcontroller, microprocessor, FPGA, ASIC, etc. An embedded system is a dedicated hardware device that can run the model with high efficiency and low power consumption. It can also be integrated with other devices or systems, such as robots, drones, cars, etc.
It can be programmed using various frameworks and languages, such as TensorFlow Lite for Microcontrollers, Arduino, C/C++, etc. This technique is suitable for scenarios where the model needs to provide high-performance and low-cost predictions to users or applications in a specific domain or environment.
Conclusion
There are several powerful tools and techniques available for deploying machine learning models. Each tool has its strengths and weaknesses, and the choice of the tool depends on the specific needs of the project. The selection of the tool will depend on the specific requirements of the project, such as scalability, reliability, ease of use, compatibility with different machine learning libraries, and deployment on-premises or in the cloud. It is essential to evaluate each tool based on its features and capabilities before making a final decision. With the right tools and techniques, deploying machine learning models can be a seamless and straightforward process, enabling businesses to unlock the full potential of their models and gain valuable insights to drive their operations forward.