As machine learning (ML) keeps transforming industries, the need for more trustworthy tools that simplify the development, deployment, and monitoring of ML models has reached an all-time high. In 2024, a handful of standout platforms have established themselves as key players in the ML landscape. This article dives deep into four of the most notable options. These options are Kubeflow, MLflow, TensorFlow Extended (TFX), and Seldon. Additionally, this article will explore what sets them apart from other options, their strengths and weaknesses, and what we can expect from them in 2025.
Kubeflow: The comprehensive ML platform
Kubeflow, an open-source ML toolkit, has proven a powerful choice for deploying and scaling ML workflows on Kubernetes. Known for its flexibility, Kubeflow is built to handle complex ML pipelines, making it a popular option for data scientists and engineers who need comprehensive solutions. With its pipeline orchestration and strong scalability, thanks to its Kubernetes foundation, Kubeflow enables handling numerous training jobs and seamless model deployment. However, despite these advantages, the platform comes with a steeper learning curve and requires expertise in Kubernetes, which can be a challenge for teams without sufficient DevOps knowledge. Nevertheless, its open-source, community-driven nature ensures continuous improvement, positioning it well for future advancements.
Outlook for 2025: As AI infrastructure evolves, Kubeflow is expected to offer enhanced integration with other cloud-native tools and more user-friendly features, making it accessible to a broader range of users. A 2023 Forbes article titled “Kubeflow Joins CNCF To Accelerate The Adoption Of MLOps”, highlighted the growing trend of Kubernetes-based ML platforms, with Kubeflow leading the charge due to its flexibility.
MLflow: The versatile experiment tracker
MLflow has cemented its reputation as a go-to platform for managing the ML lifecycle, particularly for experiment tracking and reproducibility. Its simplicity and versatility make it appealing, as it supports popular programming languages and frameworks like Python and Apache Spark. MLflow’s comprehensive tracking capabilities and model registry facilitate collaboration and model versioning. While its deployment capabilities are functional, they can appear basic compared to more sophisticated tools like Kubeflow. Organizations might need to supplement MLflow with additional deployment solutions for large-scale projects. MLflow's low entry barrier and user-friendly approach make it highly favored among smaller teams and individual developers.
A fruitful 2025: MLflow is poised to strengthen its deployment features and introduce more collaborative tools to bridge the gap between experiment tracking and robust production deployment. According to a TechCrunch article titled “The global future of remote work, according to three HR startup leaders”, MLflow's 2024 update introduced extended support for cloud-based tracking, paving the way for more remote work collaborations.
TFX (TensorFlow Extended): The powerhouse for production pipelines
TensorFlow Extended (TFX) offers an extensive platform for teams looking to build and deploy production-grade ML pipelines, especially those already invested in TensorFlow. With pre-built components for data validation, transformation, and training, TFX streamlines the creation of end-to-end workflows. Its seamless integration with TensorFlow allows optimized performance and efficient scaling, particularly on Google Cloud. However, its tight integration with TensorFlow can be a limitation for teams using diverse ML frameworks, and the complexity of its setup can be daunting for smaller teams without specialized expertise. Despite this, TFX’s robust support from Google and its suitability for large-scale, production-level pipelines make it a compelling choice for enterprises focused on reliability.
TensorFlows potential in 2025: With the MLOps field continuing to mature, TFX is expected to introduce enhanced automation features and tools for better model monitoring and retraining, catering to the growing need for adaptive and scalable ML solutions. *Reference*: A 2024 paper from Google Research titled “Learning the importance of training data under concept drift” discussed new TFX components that aim to improve automated retraining and model drift detection.
Seldon: The specialist for model serving
Seldon stands out as a specialized tool for model deployment and serving, supporting multiple frameworks like TensorFlow, PyTorch, and XGBoost. Its advanced serving capabilities, such as A/B testing, shadow deployments, and canary releases, make it a robust option for businesses requiring production flexibility. Seldon's focus on explainability and monitoring adds value by ensuring models perform reliably post-deployment. However, unlike all-in-one solutions like Kubeflow or TFX, Seldon is limited to the model-serving phase of the ML lifecycle, meaning users often need to pair it with other tools for full ML pipeline management. This specialization, while powerful, can be seen as a drawback for teams looking for comprehensive platforms.
Potential steps in 2025: As responsible AI becomes more critical, Seldon is set to enhance its monitoring and explainability tools to meet these demands. The platform will likely continue to expand its compatibility with new ML frameworks and integrate more seamlessly with MLOps practices. The 2024 Gartner Report titled “Seldon Named in the 2023 Gartner Market Report, A CTO’s Guide to the Generative AI Technology Landscape” shed light on MLOps tools and further emphasized Seldon's growth as a leader in model-serving technology, noting its adaptability to various deployment environments.
Conclusion
The ML landscape in 2024 showcases an array of powerful tools, each with its strengths tailored to specific needs. Kubeflow’s unmatched scalability makes it ideal for large projects requiring complex orchestration, whereas MLflow’s simplicity is a perfect fit for tracking experiments and lightweight collaboration. TFX, backed by Google, caters to teams needing production-grade workflows, while Seldon remains the go-to for advanced model serving and monitoring. As we move into 2025, the focus will likely shift toward improved user experiences, enhanced automation, and deeper integration with the broader MLOps ecosystem, ensuring that these tools continue to meet the demands of an ever-evolving AI landscape.
