Top 10 Tools for ML Model Deployment [Updated 2024]

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Current State of ML Model Deployment

As machine learning continues to advance, organizations are increasingly looking to deploy their models into production environments to drive business value. However, the process of deploying models can be complex and time-consuming, requiring expertise in both data science and infrastructure management. This has led to the development of various tools and platforms aimed at simplifying and streamlining the deployment process.

These model deployment solutions offer a range of features, such as compatibility with popular ML frameworks, performance optimization, scalability, and collaborative capabilities. By leveraging these tools, data scientists and developers can focus on building and refining their models, while the underlying infrastructure is managed automatically. While these platforms have made significant strides in simplifying model deployment, there is still room for improvement in terms of ease of use, customization options, and integration with existing systems.

Criteria for Evaluating ML Model Deployment Tools

Here are the 5 key criteria we considered when evaluating ML model deployment tools:

1. Ease of use: The tool should have a user-friendly interface and intuitive workflows, allowing data scientists and developers to deploy models without extensive infrastructure knowledge.

2. Compatibility: The platform should support a wide range of popular ML frameworks, such as TensorFlow, PyTorch, and scikit-learn, enabling users to deploy models trained in their preferred framework.

3. Performance and scalability: The tool should offer optimized performance and efficient resource utilization, ensuring high-throughput inference and low latency. It should also provide scalability options to handle varying workloads and accommodate growth.

4. Flexibility and customization: The platform should offer flexible deployment options, such as on-premises, cloud, or edge deployments, to cater to different infrastructure requirements. It should also allow for customization and integration with existing systems and tools.

5. Collaboration and governance: The tool should provide features that facilitate collaboration among team members, such as version control, access control, and audit trails. It should also offer governance capabilities to ensure compliance, security, and reproducibility throughout the model lifecycle.

Top 10 Tools for Deploying Machine Learning Models

Modelbit

Modelbit is a powerful platform that simplifies the deployment and management of machine learning models in production environments. With a focus on ease of use and efficiency, Modelbit enables data scientists and developers to bring their models to life quickly and reliably.

Modelbit's intuitive interface allows users to deploy models from various frameworks, such as TensorFlow, PyTorch, and scikit-learn, without getting bogged down in infrastructure complexities. The platform employs advanced optimization techniques to ensure optimal performance and resource utilization, enabling high-throughput inference and minimizing latency.

Modelbit automatically gives your ML models inference APIs

Scalability and reliability are key priorities for Modelbit, who have built their own ML infrastructure to handle things like load balancing and deploying ML models to isolated containers. Built-in fault tolerance and automatic failover mechanisms ensure that models remain highly available and resilient.

The platform also emphasizes collaboration and governance, with features like version control, access control, and audit trails. Teams can work together efficiently, sharing models, experiments, and insights within a secure environment.

Model logs in Modelbit

Modelbit recognizes the importance of customization and provides a plugin architecture that allows users to extend its functionality and integrate with their preferred tools and frameworks.

Pros:

  • Intuitive interface for easy model deployment
  • Advanced optimization techniques for optimal performance
  • Integration with container orchestration platforms for scalability
  • Flexible deployment options for various infrastructures
  • Collaborative features for version control and governance

Key Features:

  • Automated model deployment with the Modelbit Python API
  • Syncs your model code with your git repository
  • Deploys ML models to isolated containers behind REST APIs
  • On-Demand compute that autos-scales
  • Fast cold start times
  • MLOps features like monitoring, alerting, and drift detection

Seldon.io

Seldon.io's Seldon Core is an open-source framework designed to streamline and expedite the deployment of machine learning models. This framework integrates with and serves models constructed using any open-source ML framework, providing developers with flexibility and compatibility.

Seldon Core deploys ML models within Kubernetes, a container orchestration platform. By leveraging Kubernetes, Seldon Core enables users to utilize Kubernetes features, such as customizing resource definitions to handle complex model graphs. This integration allows for scaling and management of deployed models, aiming to optimize performance and resource utilization.

Seldon Core offers integration with continuous integration and deployment (CI/CD) tools. This integration enables developers to scale and update model deployments, facilitating iteration and improvement of ML applications. With Seldon Core, organizations can establish pipelines that automatically deploy updated models, aiming to maintain the performance and accuracy of their applications.

Seldon Core includes an alerting system that notifies users when issues arise during the monitoring of models in production environments. This feature helps developers identify and address potential problems, aiming to minimize downtime and ensure the smooth operation of deployed models. Furthermore, Seldon Core allows users to define specific models to interpret certain predictions, providing control over the behavior and output of ML applications.

Seldon Core offers flexibility in deployment options, being available both in the cloud and on-premise. This caters to the diverse needs and preferences of organizations, whether they prefer the scalability of cloud-based deployments or the control of on-premise solutions.

Pros:

  • Supports custom offline models
  • Exposes APIs for real-time predictions to external clients
  • Simplifies the deployment process by abstracting away much of the complexity

Cons:

  • The initial setup process can be somewhat complex, particularly for those new to the framework
  • The learning curve for newcomers may be steep, requiring a certain level of technical expertise and familiarity with ML deployment concepts

TensorFlow Serving

TensorFlow Serving is an open-source platform developed by Google that facilitates the deployment of machine learning models in production environments. It is designed to serve models trained using TensorFlow, a popular open-source library for machine learning and deep learning.

TensorFlow Serving provides a flexible and efficient solution for deploying models as gRPC or REST endpoints, enabling easy integration with other systems and applications. It supports various model formats, including TensorFlow SavedModels and TensorFlow Estimators, allowing developers to deploy their models seamlessly.

One of the key features of TensorFlow Serving is its ability to handle multiple versions of a model simultaneously. This enables developers to perform A/B testing, compare different model versions, and smoothly transition between versions without downtime. TensorFlow Serving also provides built-in support for model versioning and model updates, making it easier to manage and update deployed models over time.

TensorFlow Serving offers scalability and high performance, capable of serving models with low latency and high throughput. It achieves this through efficient resource utilization and optimized serving infrastructure. Additionally, TensorFlow Serving integrates well with other components of the TensorFlow ecosystem, such as TensorFlow Extended (TFX) for end-to-end machine learning pipelines.

Pros:

  • Seamless deployment of TensorFlow models as gRPC or REST endpoints
  • Supports multiple model formats, including TensorFlow SavedModels and Estimators
  • Enables A/B testing and smooth transitions between model versions

Cons:

  • Limited to serving models trained using TensorFlow, may not be suitable for models trained with other frameworks
  • Requires familiarity with TensorFlow and its ecosystem, which can have a learning curve
  • Setting up and configuring TensorFlow Serving can be complex, especially for large-scale deployments
  • Lacks built-in monitoring and logging capabilities, requiring integration with external tools
  • May not be the most lightweight solution for simple model serving scenarios

KServe

KServe, formerly known as KFServing, is a Kubernetes-native platform for serving machine learning models. Developed as part of the Kubeflow project, KServe aims to simplify and standardize the deployment and management of ML models on Kubernetes clusters.

One of the key advantages of KServe is its ability to serve models from various ML frameworks, including TensorFlow, PyTorch, XGBoost, and scikit-learn. This flexibility allows data scientists and developers to choose the framework that best suits their needs without being limited by the serving platform.

KServe leverages the power of Kubernetes to provide scalable and reliable model serving. It automatically scales the model serving instances based on incoming traffic, ensuring optimal resource utilization and high availability. Additionally, KServe supports canary rollouts and traffic splitting, enabling smooth model updates and minimizing the risk of production issues.

To enhance model performance and reduce response latency, KServe includes built-in support for model caching and adaptive batching. It also provides features like request logging, metrics collection, and integration with Prometheus for monitoring and alerting purposes.

While KServe offers several benefits, it also has some limitations. One notable drawback is its dependency on Kubernetes, which may require additional infrastructure setup and management overhead. KServe also lacks advanced model management capabilities, such as model versioning and automatic model retraining based on new data.

Cons:

  • Requires a Kubernetes cluster, which may add complexity to the deployment process
  • Limited built-in model management features compared to some other serving platforms
  • May have a steeper learning curve for teams not familiar with Kubernetes concepts
  • Integration with existing CI/CD pipelines can be challenging
  • Debugging and troubleshooting issues within the Kubernetes environment can be complex

Pros:

  • Supports serving models from multiple ML frameworks
  • Provides automatic scaling and high availability through Kubernetes
  • Enables canary rollouts and traffic splitting for smooth model updates

Metaflow is a Python library developed by Netflix to simplify the development and deployment of data science and machine learning workflows. It provides a unified framework for building and managing end-to-end data pipelines, from data ingestion to model training and deployment.

One of the key features of Metaflow is its intuitive and expressive API, which allows data scientists to define their workflows using a familiar Python syntax. With Metaflow, users can easily define the steps of their data pipeline, specify dependencies between steps, and handle data flow between them.

Metaflow

Metaflow introduces the concept of "workflows" and "tasks" to encapsulate the logic and dependencies of a data pipeline. Each task represents a unit of work, and tasks can be arranged in a directed acyclic graph (DAG) to define the overall workflow. This modular and graph-based approach makes it easier to understand, debug, and maintain complex data pipelines.

Another notable aspect of Metaflow is its ability to seamlessly integrate with various cloud platforms, such as AWS and Google Cloud. It provides built-in support for running workflows on cloud infrastructure, including automatic provisioning of resources and distributed execution of tasks. This enables users to scale their workflows to handle large datasets and computationally intensive tasks.

Metaflow also offers features like versioning, caching, and resuming of workflows. It automatically tracks the lineage and metadata of each task, allowing users to reproduce and analyze past runs. However, while Metaflow provides a streamlined development experience, it may not be as suitable for highly complex and custom ML architectures compared to more flexible frameworks.

Cons:

  • Limited support for non-Python languages, making integration with existing codebases challenging
  • Steep learning curve for users unfamiliar with the Metaflow paradigm and its abstractions
  • Dependence on cloud platforms for distributed execution, which may incur additional costs
  • Lack of built-in monitoring and alerting capabilities for production deployments
  • May not be as suitable for highly custom and complex ML architectures

Pros:

  • Intuitive and expressive Python API for defining data pipelines
  • Modular and graph-based approach to organizing workflows
  • Seamless integration with cloud platforms for distributed execution

MLflow

MLflow is an open-source platform designed to manage the end-to-end machine learning lifecycle. It provides a set of tools and APIs to track experiments, package code into reproducible runs, share and deploy models, and monitor model performance in production.

One of the key components of MLflow is its Tracking API, which allows data scientists to log parameters, metrics, and artifacts during the model training process. This enables easy comparison and visualization of different experiments, helping users identify the best-performing models and hyperparameters.

MLflow's Projects component provides a standardized format for packaging data science code, making it easier to share and reproduce experiments across different environments. It defines a convention for organizing code and specifying dependencies, allowing users to run projects with a single command.

The Models component of MLflow offers a unified way to package and deploy machine learning models from various frameworks, such as TensorFlow, PyTorch, and scikit-learn. It supports multiple deployment options, including REST API serving, batch inference, and real-time streaming.

MLflow integrates with popular development tools and platforms, such as Jupyter Notebooks, Apache Spark, and Kubernetes, making it adaptable to different workflows and infrastructures. It also provides a central model registry to manage the lifecycle of models, including versioning, staging, and promotion to production.

However, MLflow has some limitations. It lacks advanced data versioning and management capabilities, which can be crucial for reproducibility and data governance. Additionally, while MLflow provides deployment options, it may require integration with external tools for more complex serving scenarios.

Cons:

  • Limited built-in data versioning and management capabilities
  • May require integration with external tools for advanced model serving scenarios
  • Steep learning curve for teams new to MLflow's concepts and APIs
  • Potential performance overhead due to the extensive tracking and logging of experiments
  • Requires careful setup and configuration to ensure secure and scalable deployments

Pros:

  • Comprehensive tracking and comparison of experiments
  • Standardized packaging of data science code for reproducibility
  • Support for multiple deployment options and model frameworks

Ray Serve

Ray Serve is a scalable and flexible framework for serving machine learning models in production. It is built on top of Ray, a distributed computing platform, and provides a high-performance serving system for deploying and managing machine learning models.

One of the key features of Ray Serve is its ability to serve models from any Python-based machine learning library, such as TensorFlow, PyTorch, or scikit-learn. This flexibility allows developers to choose the most suitable framework for their specific use case without being constrained by the serving infrastructure.

Ray Serve simplifies the deployment process by providing a unified API for defining and deploying models as microservices. It abstracts away the complexities of load balancing, scaling, and fault tolerance, enabling developers to focus on building and iterating on their models.

With Ray Serve, models can be easily scaled horizontally to handle increasing traffic and workloads. It automatically distributes the load across multiple replicas of the model, ensuring high availability and low latency. Ray Serve also supports canary deployments and traffic splitting, allowing for controlled rollouts and testing of new model versions.

Another advantage of Ray Serve is its seamless integration with the Ray ecosystem. It can leverage Ray's distributed computing capabilities to parallelize and accelerate model inference, enabling efficient utilization of cluster resources. Ray Serve also integrates with Ray's fault tolerance and recovery mechanisms, ensuring reliable and resilient serving of models.

However, Ray Serve has some drawbacks to consider. It introduces a new set of abstractions and concepts, which may require a learning curve for developers unfamiliar with the Ray ecosystem. Additionally, Ray Serve's performance may be impacted by the overhead of inter-process communication and serialization, especially for models with large input/output payloads.

Cons:

  • Requires familiarity with the Ray ecosystem and its concepts
  • Potential performance overhead due to inter-process communication and serialization
  • Limited built-in monitoring and logging capabilities compared to some other serving frameworks
  • May not be suitable for serving models with extremely low-latency requirements
  • Debugging and troubleshooting can be challenging in a distributed serving environment

Pros:

  • Supports serving models from various Python-based machine learning libraries
  • Provides a unified API for deploying models as microservices
  • Enables automatic scaling and load balancing of model replicas

TorchServe

TorchServe is a flexible and scalable open-source platform for serving PyTorch models in production environments. Developed by Amazon Web Services (AWS) in collaboration with Facebook, TorchServe aims to simplify the deployment and management of PyTorch models while providing high-performance serving capabilities.

One of the key advantages of TorchServe is its seamless integration with the PyTorch ecosystem. It supports serving models trained using the latest versions of PyTorch, enabling developers to leverage the full power and flexibility of the framework. TorchServe also provides a simple and intuitive API for loading and serving models, making it easy to deploy PyTorch models with minimal code changes.

TorchServe offers a range of features to optimize model serving performance. It supports multi-model serving, allowing multiple models to be hosted on the same server instance. This enables efficient utilization of resources and reduces the overhead of managing separate server instances for each model. TorchServe also includes built-in support for model versioning, enabling smooth updates and rollbacks of deployed models.

To ensure high availability and scalability, TorchServe integrates with container orchestration platforms like Kubernetes. It can be easily deployed and scaled within a Kubernetes cluster, leveraging the platform's automatic scaling and fault tolerance capabilities. TorchServe also provides monitoring and logging functionalities, enabling developers to track model performance and identify issues in real-time.

However, TorchServe has some limitations to consider. While it excels at serving PyTorch models, it may not be the best choice for serving models from other frameworks. Additionally, TorchServe's custom configuration and deployment process may require some learning and adaptation for teams unfamiliar with the platform.

Cons:

  • Limited support for serving models from frameworks other than PyTorch
  • Requires familiarity with TorchServe's custom configuration and deployment process
  • May not be as feature-rich as some other serving platforms in terms of advanced model management capabilities
  • Dependency on the PyTorch ecosystem, which may limit flexibility in certain scenarios
  • Potential performance overhead due to the additional abstraction layer introduced by TorchServe

Pros:

  • Seamless integration with the PyTorch ecosystem
  • Simple and intuitive API for loading and serving models
  • Supports multi-model serving for efficient resource utilization

Triton Inference Server

Triton Inference Server, developed by NVIDIA, is a high-performance and scalable open-source platform for serving machine learning models in production environments. It is designed to simplify the deployment and management of models while providing efficient inference capabilities across various hardware platforms, including GPUs, CPUs, and custom accelerators.

One of the key strengths of Triton Inference Server is its ability to serve models from multiple frameworks, such as TensorFlow, PyTorch, ONNX, and TensorRT. This flexibility allows data scientists and developers to choose the most suitable framework for their specific use case and deploy models seamlessly using a unified serving platform.

Triton Inference Server offers a range of features to optimize inference performance and resource utilization. It supports dynamic batching, which automatically combines multiple inference requests into batches to improve throughput and reduce latency. Triton also provides model ensemble support, enabling the composition of multiple models to create more complex and accurate inference pipelines.

To ensure scalability and high availability, Triton Inference Server can be deployed in various deployment scenarios, including Kubernetes clusters, cloud platforms, and edge devices. It integrates with container orchestration tools and supports horizontal scaling to handle increasing workloads. Triton also provides built-in monitoring and metrics collection, enabling developers to track model performance and resource utilization.

However, Triton Inference Server has some limitations to consider. It may have a steeper learning curve compared to other serving platforms, especially for teams unfamiliar with NVIDIA's ecosystem and tools. Additionally, while Triton supports multiple frameworks, it may not have the same level of integration and optimization for all frameworks compared to platform-specific serving solutions.

Cons:

  • Steeper learning curve, particularly for teams not familiar with NVIDIA's ecosystem
  • May require additional setup and configuration for optimal performance on non-NVIDIA hardware
  • Limited built-in model management and versioning capabilities compared to some other serving platforms
  • Potential compatibility issues with certain model architectures or custom operators
  • May not be as lightweight and resource-efficient as some other serving solutions for simple use cases

Pros:

  • Supports serving models from multiple frameworks, providing flexibility
  • Offers dynamic batching and model ensemble support for optimized inference performance
  • Provides scalability and high availability through integration with container orchestration tools

ONNX Runtime

ONNX Runtime is an open-source inference engine developed by Microsoft for serving models in the Open Neural Network Exchange (ONNX) format. ONNX is an open standard for representing machine learning models, enabling interoperability between different frameworks and platforms.

One of the key advantages of ONNX Runtime is its ability to serve models from various popular frameworks, such as TensorFlow, PyTorch, scikit-learn, and more. By converting models to the ONNX format, developers can deploy them using ONNX Runtime, regardless of the original framework used for training. This promotes model portability and reduces the dependency on specific frameworks.

ONNX Runtime is designed for high-performance inference and provides optimizations for different hardware platforms, including CPUs, GPUs, and specialized accelerators like Intel's OpenVINO and Xilinx's FPGA. It leverages graph optimization techniques and hardware-specific libraries to maximize inference speed and efficiency.

To support deployment in various environments, ONNX Runtime offers a C++ API and language bindings for Python, C#, and Java. It can be easily integrated into existing applications and deployed in different scenarios, such as cloud services, edge devices, and mobile platforms. ONNX Runtime also provides extensibility mechanisms for custom operators and optimizations.

However, ONNX Runtime has some limitations. While it supports a wide range of models, some complex or custom models may face compatibility issues when converted to the ONNX format. Additionally, the performance of ONNX Runtime may vary depending on the specific model architecture and the target hardware platform.

Cons:

  • Some complex or custom models may face compatibility issues when converted to the ONNX format
  • Performance may vary depending on the model architecture and target hardware platform
  • Limited built-in model management and versioning capabilities compared to some other serving platforms
  • Requires additional steps to convert models from their original framework to ONNX format
  • May not have the same level of community support and ecosystem as some other popular serving platforms

Pros:

  • Supports serving models from various frameworks by leveraging the ONNX format
  • Provides optimizations for different hardware platforms to maximize inference performance
  • Offers a C++ API and language bindings for easy integration into existing applications

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