How we create and deploy trained model APIs in the production environment is governed by many aspects of the machine learning lifecycle. The concept of MLOps has been very beneficial for dealing with complex ML deployment environments.
Implementing solid MLOps can generate big benefits for a company invested in machine learning. Understanding what to employ and execute is an essential part of the puzzle. Learning and adapting to a new tool that simplifies the overall workflow is a whole other thing.
This article lists the best tools used for model deployment. All the essentials to help you scale and manage the machine learning lifecycle, involving serving, monitoring, and managing the API endpoint.
Interested in other MLOps tools?
When building their ML pipelines, teams usually look into a few other components of the MLOps stack.
If that’s the case for you, here are a few article you should check:
Seldon.io
Seldon.io offers Seldon core, which is an open-source framework. This framework simplifies and accelerates ML model deployment.Â
It handles and serves models that are built in any other open-source ML frameworks. ML models are deployed in Kubernetes. As it scales with Kubernetes, it enables us to use state of the art Kubernetes features, such as customizing resource definition to handle model graphs.
Seldon also offers the power to connect your project with continuous integration and deployment (CI/CD) tools to scale and update model deployment.Â
It has an alerting system that notifies you when a problem occurs while monitoring models in production. You can define the model to interpret certain predictions. This tool is available in the cloud, as well as on-premise.
Seldon pros:
- Custom offline models.
- Real-time predictions exposing APIs to external clients.
- Simplifies the deployment process.
Seldon cons:
- The setup can be a bit complex.
- It can be difficult to learn for newcomers.
BentoML
BentoML simplifies the process of building machine learning services. It offers a standard, Python-based architecture for deploying and maintaining production grade APIs. This 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 as well as the ability to scale model inference workers separately from business logic. The UI dashboard offers a centralized system to organize models and monitor deployment processes.
Its modular design makes the configuration reusable with existing GitOps workflows and automatic docker image generation makes deployment to production a simple and versioned process.
The multipurpose framework addresses the ML model serving, organization, and deployment. The main focus is on connecting data science and DevOps departments for a more efficient working environment and to produce high-performance scalable API endpoints.
BentoML pros:
- A practical format for easily deploying prediction services at scale
- Supports high-performance model serving and deployment in a single unified format
- Supports model deployment to multiple platforms, not just Kubernetes
BentoML cons:
- Doesn’t focus on experimentation management.
- Doesn’t handle horizontal scaling out-of-the-box.
TensorFlow Serving
If you want to deploy your trained model as an endpoint, you can do that with TensorFlow Serving.
It lets you create a REST API endpoint that will serve the trained model. TensorFlow Serving is a robust, high-performance system for serving machine learning models.
You can deploy a state of the art machine learning algorithms easily while maintaining the same server architecture with its respective endpoints. It’s powerful enough to serve different types of models and data, along with TensorFlow models.
It was created by Google, and many top companies use it. Serving the model as a centralized model base is a great way to do it. The serving architecture is efficient enough for a large pool of users to access the model at the same time.
If there’s any choking due to a large number of requests, it can be easily maintained using the load balancer. Overall, the system is scalable and maintainable with a high-performance rate.
TensorFlow Serving pros:
- 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.
TensorFlow Serving cons:
- There is no way to ensure zero downtime when new models are loaded or old ones are updated.
- Works only with TensorFlow models.
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Kubeflow
The main objective of Kubeflow is to maintain machine learning systems. It’s a powerful kit designed for Kubernetes.
The main operations include packages, and organizing docker containers that help maintain an entire machine learning system.
It simplifies the development and deployment of machine learning workflows, in turn making models traceable. It offers a set of powerful ML tools and architectural frameworks to perform various ML tasks efficiently.
The multifunctional UI dashboard makes it easy to manage and track experiments, tasks, and deployment runs. The Notebook feature enables us to interact with the ML system using the specified platform development kit.
Components and pipelines are modular and can be reused to offer quick solutions. This platform was started by Google to serve the TensorFlow tasks through Kubernetes. It later scaled to a multi-cloud, multi-architecture framework that executes the entire ML pipeline.
Kubeflow pros:
- Consistent infrastructure, offers monitoring, health check, replication each time, as well as extensions to new features.
- Simplifies the on-boarding of new team members.
- A standardized process helps establish security and better control over the infrastructure.
Kubeflow cons:
- Difficult to set up and configure manually.
- High availability is not automatic and needs to be manually configured.
- The learning curve of this tool is steep.
Check also
Cortex
Cortex is an open-source multi-framework tool that is flexible enough to be used as a model serving tool, as well as for purposes like model monitoring.
With its ability to address different machine learning workflows, it grants you full control over model management operations. It also acts as an alternative to serving models with the SageMaker tool, and a model deployment platform on top of AWS services like Elastic Kubernetes Service (EKS), Lambda, or Fargate.
Cortex expands to open-source projects like Docker, Kubernetes, TensorFlow Serving, and TorchServe. It can work with any ML libraries or tools in cohesion. It offers scalability of endpoints to manage loads.
It lets you deploy multiple models in a single API endpoint. It also acts as a solution to update the already production endpoints without halting the server. Covering the footsteps of a model monitoring tool, it supervises the endpoint’s performance, as well as prediction data.
Cortex pros:
- Auto-scaling feature which 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.
Cortex cons:
- The setup process can be somewhat daunting.
AWS Sagemaker
AWS Sagemaker is a powerful service provided by Amazon. It gives ML developers the ability to build, train, and deploy machine learning models quickly.
It simplifies the whole machine learning process by removing some of the complex steps, thus providing highly scalable ML models.
The machine learning development lifecycle is a complex iterative. It forces you to integrate complex tools and workflows. This task can be demanding and irritating, and it may consume a lot of your time. Not to mention the trouble of getting errors while configuring.
Sagemaker makes this process easier, providing all components used for machine learning in a centralized toolset. There’s no need to configure each one, as it is already installed and ready for use.
This accelerates model production and deployment with minimal effort and cost. The tool can be used for endpoints created using any ML frameworks. It also offers prediction tracking and capture, as well as schedule monitoring.
AWS Sagemaker pros:
- The setup process is simple and can run with Jupyter Notebook. Hence, the management and deployment of scripts is simplified.
- The cost is modular, based on the feature you use.
- Model training is done on multiple servers.
AWS Sagemaker cons:
- Steep learning curve for junior developers.
- Strict workflows make it hard to customize.
- Works only with AWS ecostystem
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MLflow
If you’re looking for an open-source tool to organize your entire ML lifecycle, this might be the platform for you.
MLflow provides solutions for managing the ML process and deployment. It can do experimentation, reproducibility, deployment, or be a central model registry.
The platform can be used for ML deployment by individual developers as well as teams. It can be incorporated into any programming ecosystem. The library is built to satisfy various technological needs and can be used with different machine learning libraries.
Organizing the entire ML lifecycle revolves around four main functions: Tracking, Projects, Models, and Model Registry.
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It helps to simplify the process of automating ML model tracking. But one downside can be its inability to address the model definition automatically. This means that the addition of extra workings to model definition needs to be done manually.
MLflow pros:
- 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.
MLflow cons:
- The addition of extra workings to the models is not automatic.
- Not quite easy and ideal for deploying models to different platforms.
May interest you
Torchserve
Torchserve is a Pytorch model serving framework. It simplifies the deployment of trained PyTorch models at scale. It removes the need to write custom code for model deployment.
Torchserve was designed by AWS and is available as part of the PyTorch project. This makes setup easy for those who are using the PyTorch environment for building models.
It enables lightweight serving with low latency. Deployed models have high-performance and a broad scalability spectrum.
Torchserve has built-in libraries for some ML tasks, like object detection or text classification. It can save you some time that you’d spend coding them. It delivers powerful features, like multi-model serving, model versioning for A/B testing, metrics for monitoring, and RESTful endpoints for application integration.
Torchserve pros:
- Scaling deployed models is simplified.
- Serving endpoints are lightweight with a high-performance scale.
Torchserve cons:
- Changes and updates happen often because the tool is experimental.
- Works only with PyTorch Models
What next?
The creation and deployment of high-performance and scalable machine learning models are challenging tasks.
Luckily, the deployment tools and frameworks listed in this article can help you create robust ML models and deploy them quickly and with ease.
Since you got to this point, you’re probably building or updating your MLOps stack. So here are a few resources you might look into next.
Best tools for other components of the ML pipeline:
- The Best MLOps Tools and How to Evaluate Them
- 15 Best Tools for ML Experiment Tracking and Management
- Best Tools for Model Tuning and Hyperparameter Optimization
- Best Machine Learning Workflow and Pipeline Orchestration Tools
- Best Tools to Do ML Model Monitoring
Real-world examples of how others built their MLOps:
- Real-World MLOps Examples: End-To-End MLOps Pipeline for Visual Search at Brainly
- Real-World MLOps Examples: Model Development in Hypefactors
- Building ML Pipeline: 6 Problems & Solutions [From a Data Scientist’s Experience]
- This Is Our MLOps Tool Stack: Continuum Industries
