Top Machine Learning Model Deployment Tools For 2022

In the field of technology, machine learning is nothing new. The capacity to automate channels and increase company process flexibility brought about a revolutionary change for numerous industries.

The machine learning lifecycle governs many aspects of developing and deploying trained model APIs in the production environment. Model deployment, which differs from the creation of ML models in that it has a steeper learning curve for beginners, has proven to be one of the most significant challenges in data science.

Model deployment refers to integrating a machine learning model that accepts an input and delivers an output to make helpful business decisions based on data into an already-existing production environment.

However, several technologies have been developed recently to make model deployment simpler. We’ll review a few tools in this article that you may utilize to deploy your machine-learning models.

Docker

With the help of the platform Docker, you can build, distribute, and operate applications inside containers. A container is a type of software that packages code to allow an application to run quickly and consistently in different computing environments. We can alternatively define it as the act of putting codes and dependencies in a container—a closed box. To be used for deploying machine learning models into other settings, Docker also streamlines the containerization and implementation process.

Gradio

Gradio is a flexible user interface (UI) used with Tensorflow or Pytorch models. It’s free, and anyone may quickly access it thanks to an open-source foundation. With the help of the open-source Gradio Python library, we can soon develop user-friendly, adaptable UI components for our machine learning model, an API, or any other function in only a few lines of code.

Gradio provides several UI elements that may be customized and tailored for machine learning models. For instance, Gradio offers simple drag-and-drop image classification that is extremely user-optimized.

Gradio is incredibly quick and straightforward to set up. Direct installation is possible using pip. Furthermore, Gradio simply needs a few lines of code to provide an interface.

The quickest approach to deploying machine learning in front of people is probably through Gradio’s creation of shareable links. Gradio may be used anywhere, whether it’s a standalone Python script or a Jupyter/Colab notebook, unlike the other libraries.

Kubernetes

An open-source platform called Kubernetes is used to manage containerized tasks and operations. A resource object in Kubernetes that provides declarative updates to applications is how we can describe Kubernetes deployment. A deployment enables us to specify an application’s life cycle, for instance, which images to use and how frequently they should be updated.

Kubernetes helps make applications run more steadily and consistently. Utilizing Kubernetes and its vast ecosystem can assist increase productivity and efficiency. Compared to its competitors, Kubernetes may be less expensive.

SageMaker

SageMaker is a fully supervised service. Amazon SageMaker includes modules that can be used independently or in conjunction with one another to construct, train, and deploy ML models. With SageMaker, developers and data scientists can quickly and efficiently build, train, and deploy machine learning models into a production-ready hosted environment at any scale.

It contains an inbuilt Jupyter writing notebook instance for quick and straightforward access to the data sources for research and analysis, so you don’t need to manage any servers. Additionally, it offers popular machine learning methods that have been enhanced and optimized for use against massive amounts of data in a distributed setting.

MLFlow

An open-source platform, MLflow manages the entire ML lifecycle, from experimentation to deployment. It is designed to function with any language, deployment tool, computation, and ML library.

It records and compares parameters and outcomes during trials and experiments. Any cloud can use it because that is how it works. The open-source machine learning frameworks Apache Spark, TensorFlow, and SciKit-Learn integrate with MLflow.

It collects and organizes ML code into a reusable, transferable structure that can be taught to other data scientists or used in real-world settings. It manages and distributes models from various ML libraries to different model serving and inference systems.

TensorFlow Serving

A reliable, high-performance solution for serving machine learning models is called TensorFlow Serving. TensorFlow Serving allows you to use your trained model as an endpoint for deployment. It enables you to develop a REST API endpoint for the trained model.

Modern machine learning algorithms can be easily deployed while keeping the same server architecture and corresponding endpoints. Along with TensorFlow models, it is strong enough to handle many models and data kinds.

Numerous prestigious companies utilize it, and Google built it. It’s a terrific idea to serve as the model’s central model base. Many users can access the model simultaneously because of the serving architecture’s effectiveness. Using the load balancer, any choking caused by a high volume of requests may be simply maintained. Overall, the system has a reasonable performance rate and is scalable and maintainable.

Kubeflow

Kubeflow’s primary goal is to sustain machine learning systems. It is a practical Kubernetes toolkit. The primary tasks involved in maintaining a comprehensive machine learning system are organizing docker containers and packages. It makes machine learning process creation and deployment simpler, making models traceable. It provides various robust ML tools and architectural frameworks to successfully complete multiple ML jobs.

Thanks to the multipurpose UI dashboard, it is simple to manage and keep track of experiments, tasks, and deployment runs. Thanks to the Notebook functionality, we can communicate with the ML system utilizing the designated platform development kit.

Pipelines and components are reusable and modular, allowing for simple fixes. Google created this infrastructure to support TensorFlow jobs via Kubernetes. Later, it expanded into a multi-cloud, multi-architecture framework that runs the entire ML pipeline.

Cortex

An open-source multi-framework tool called Cortex is versatile enough to be used for model monitoring and providing models. It gives you total control over model management activities and can handle various machine learning workflows. It also serves as an alternative to using the SageMaker tool to help models and a model deployment platform on tops of AWS services like Lambda, Fargate, and Elastic Kubernetes Service (EKS).

Cortex now includes open-source initiatives, including TorchServe, TensorFlow Serving, Docker, and Kubernetes. It provides endpoint scalability to handle loads. Any ML libraries or tools can be used in harmony with it.

Multiple models can be deployed over a single API endpoint. It also serves as a way to update endpoints currently in production without pausing the server. It follows the steps of a model monitoring tool by monitoring prediction data and endpoint performance.

Seldon.io

Seldon core, an open-source framework, is available through Seldon.io. The deployment of ML models and experiments is sped up and made more straightforward by this framework. It supports and serves models created using any other open-source machine learning framework. In Kubernetes, ML models are put into use. We can employ cutting-edge Kubernetes features, including changing resource definition to manage model graphs, because it scales with Kubernetes.

Seldon allows you to connect your project to continuous integration and deployment (CI/CD) solutions to grow and update model deployments. It provides a system for alerting you when an issue arises while keeping track of models in production. The model can be defined to interpret particular predictions. Both on-premises and in the cloud are options for this tool.

BentoML

BentoML makes it easier to create machine learning services. For installing and maintaining production-grade APIs, it provides a standard Python-based architecture. With the help of any ML framework and this architecture, users can easily package trained models for online and offline model serving.

The high-performance model server of BentoML allows for adaptive micro-batching and can scale model inference workers independently of business logic. The UI dashboard provides a centralized approach for organizing models and keeping track of deployment procedures.

The setup can be reused with current GitOps workflows thanks to its modular design, and automatic docker image generation makes deployment to production a straightforward and versioned procedure.

Torchserve

A Pytorch model serving framework is called Torchserve. It makes deploying trained PyTorch models at scale simpler. It eliminates the requirement for writing original code for model deployment.

AWS created Torchserve, which is a component of the PyTorch project. For those using the PyTorch environment to build models, this simplifies setup. It makes low-latency, lightweight serving possible. Models deployed have good performance and a wide range of scalability.

Multi-model serving, model versioning for A/B testing, monitoring metrics, and RESTful endpoints for application interaction are just a few of its valuable features. For some ML tasks, including object identification or text classification, Torchserve offers built-in libraries. You might be able to save some of the time you’d spend coding them.