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Comparing Open Source Deployment and Serving Tools for Machine Learning Models
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Navigating the Landscape of MLOps Tools for Efficient ML Model Deployment
The world of machine learning (ML) is as dynamic and diverse as it is complex, with lots of tools and practices that aim to streamline and enhance the deployment and serving of ML models.
The challenge lies not only in the development of these models but also in their deployment, management, and scaling in production environments.
To be honest, there are too many MLops tools and packages, that you might get lost, as I did.
This case study discusses open-source MLOps tools, offering insights and comparative analysis to guide practitioners through the best options available for deploying and serving ML models efficiently.
Problem
ML and AI engineers need to be deploying and managing ML models at scale.
This is compounded by requirements for tracking experiments, managing model versions, and ensuring reproducibility across environments.
The deployment phase introduces its own set of challenges, including the need for scalable serving, managing dependencies, and ensuring high availability and low latency.
The absence of standardised practices and tools further complicates this landscape, making the deployment and serving of ML models a hard task for many teams.
There are too many tools ->
Solution
To address these challenges, a variety of open-source tools have emerged, each offering unique features and capabilities to streamline the ML lifecycle. We explores several key players in the field, including TensorFlow Serving, MLflow, AWS SageMaker, Seldon Core and BentoML, examining their strengths and weaknesses in the context of deploying and serving ML models.
TensorFlow Serving
TensorFlow Serving stands out for its high-performance serving system, designed specifically for TensorFlow models but extendable to others.
It offers robust features like version management, batch processing, and out-of-the-box integration with TensorFlow, making it a strong contender for teams deeply invested in the TensorFlow ecosystem.
However, its steep learning curve and lack of direct customer support, being an open-source project, may pose challenges for some teams.
MLflow
MLflow presents itself as a versatile platform, catering to the end-to-end ML lifecycle, including experiment tracking, model packaging, and serving. I especially enjoy it for model repository and model management, being able to collaborate and bring models to production.
Its open interface and compatibility with a wide range of ML libraries and deployment tools make it a flexible choice for diverse environments.
The limitations of a self-managed instance are in user management which can be a burden for larger teams or more complex projects.
AWS SageMaker
AWS SageMaker offers a fully managed service, streamlining every step of the ML lifecycle from model building and training to deployment. Its integrated Jupyter notebooks, optimized algorithms, and easy deployment capabilities make it an attractive option for teams looking for an all-in-one solution.
However, the cost associated with its deployment and the potential for vendor lock-in are significant considerations for organizations.
Seldon Core
Seldon Core is a powerful solution for deploying, scaling, and managing machine learning models in Kubernetes environments.
It is designed to convert ML models from various frameworks (such as TensorFlow, PyTorch, and others) into production-ready REST/GRPC microservices.
Seldon Core stands out for its support of complex inference pipelines, allowing for the orchestration of multiple models, transformers, and routers to create sophisticated ML workflows. It offers advanced features like A/B testing, rolling updates, and model monitoring, making it highly suitable for enterprises seeking to operationalize their ML models at scale.
However, its reliance on Kubernetes as the underlying platform might require a steep learning curve and substantial DevOps expertise, potentially limiting its accessibility to teams without dedicated Kubernetes support.
BentoML
BentoML bridges the gap between data science and DevOps, offering a user-friendly approach to packaging and serving ML models across frameworks.
It supports major machine learning frameworks, including MLflow models, TF models and other, a high-performance API serving system, and model management features position it as a compelling choice for teams prioritizing ease of use and flexibility.
However, its focus is more on model serving than on the broader aspects of ML lifecycle management and even model deployment infrastructure.
