Best 8 Machine Learning Model Deployment Tools That You Need to Know

Posted January 12, 2021
Model deployment tools

Machine learning is nothing new in the tech world. It brought about a revolutionary change for many industries, with the ability to do channel automation, and add flexibility to business workflows. 

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 MLOps 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.

TensorFlow Serving

Tensorflow serving

Source

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.
  • ­The 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

🧩 See our integration with Tensorflow


RELATED ARTICLE
How to Serve Machine Learning Models with TensorFlow Serving and Docker


MLflow

MLflow

Source

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. 

🧩 See our integration with MLflow


RELATED ARTICLE
Best Tools to Manage Machine Learning Projects


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.

👉 See the best MLflow alternatives

Kubeflow

Kubeflow

Source

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 how Neptune.ai compares with Kubeflow

Cortex

Cortex

Source

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.

Seldon.io

Seldon

Source

Seldon.io offers Seldon core, which is an open-source framework. This framework simplifies and accelerates ML models and experiment deployment. 

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 deployments. 

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

Source

BentoML simplifies the process of building machine learning API endpoints. It offers a standard, yet simplified architecture to migrate trained ML models to production. 

It lets you package trained models, using any ML framework to interpret them, for serving in a production environment. It supports online API serving as well as offline batch serving. 

BentoML has a flexible workflow with a high-performance model server. The server also supports adaptive micro-batching. The UI dashboard offers a centralized system to organize models and monitor deployment processes. 

The working mechanism is modular, making the configuration reusable, with zero server downtime. It’s a multipurpose framework addressing ML model serving, organization, and deployment. The main focus is to connect the data science and DevOps departments for a more efficient working environment and to produce high-performance scalable API endpoints.

BentoML pros:

  • Supports high-performance model serving, model management, model packaging, and a unified model format.
  • Supports deployment to multiple platforms.
  • Flexible and modular design.

BentoML cons:

  • Doesn’t focus on experimentation management.
  • Doesn’t handle horizontal scaling out-of-the-box.

AWS Sagemaker

AWS Sagemaker

Source

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

🧩 See our integration with AWS SageMaker

Torchserve

Torchserve

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

Conclusion

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.

Handling and organizing a full-scale machine learning lifecycle is no easy task. These tools will help you save time and effort in doing so.

Good luck!

Resources:

ML Infrastructure Tools for Production Aparna Dhinakaran

Developer Relation at InstaMobile

READ NEXT

ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It

Jakub Czakon | Posted November 26, 2020

Let me share a story that I’ve heard too many times.

”… We were developing an ML model with my team, we ran a lot of experiments and got promising results…

…unfortunately, we couldn’t tell exactly what performed best because we forgot to save some model parameters and dataset versions…

…after a few weeks, we weren’t even sure what we have actually tried and we needed to re-run pretty much everything”

– unfortunate ML researcher.

And the truth is, when you develop ML models you will run a lot of experiments.

Those experiments may:

  • use different models and model hyperparameters
  • use different training or evaluation data, 
  • run different code (including this small change that you wanted to test quickly)
  • run the same code in a different environment (not knowing which PyTorch or Tensorflow version was installed)

And as a result, they can produce completely different evaluation metrics. 

Keeping track of all that information can very quickly become really hard. Especially if you want to organize and compare those experiments and feel confident that you know which setup produced the best result.  

This is where ML experiment tracking comes in. 

Continue reading ->