In this article we’re diving into the CNCF Technology Radar for Batch/AI/ML Tools, which identifies the must-watch technologies for 2025. Whether you're optimizing workflows or scaling machine learning (ML) pipelines, this guide will help you prioritize the tools worth adopting—and experimenting with—in the coming year.
The integration of AI and ML into DevOps workflows is no longer optional—it’s the new norm. Mastering MLOps tools today is your gateway to staying relevant tomorrow.
Thanks for reading MLOps.tv! Subscribe for free to receive new posts and support my work.
What’s the CNCF Radar?
The CNCF Technology Radar categorizes tools into four zones:
Adopt: Reliable and mature—these are your production-ready staples.
Trial: Emerging tools worth experimenting with to see how they fit your needs.
Assess: Tools with potential but requiring further evaluation.
Hold: Technologies in early stages or with limited practical use for now.
Let’s dive into the Adopt tools you should prioritize now and explore the Trial tools that are creating a buzz for the future.
The 2025 Must-Haves (Adopt Zone)
Production-ready tools like Apache Airflow and Kubeflow aren’t just technologies—they’re the backbone of scalable, reliable ML pipelines in 2025.
1. Apache Airflow
Apache Airflow is the trusted orchestrator for designing, scheduling, and monitoring workflows. From ML pipelines to ETL tasks, it’s a staple for managing complex dependencies and automating processes. Airflow’s versatility and wide adoption make it a must-have for any DevOps/MLOps toolkit. Learn more: Apache Airflow
2. Kubeflow
Kubeflow simplifies machine learning workflows on Kubernetes. Whether you're training models, running hyperparameter tuning, or deploying at scale, Kubeflow’s modular approach ensures portability and reproducibility across environments. Learn more: Kubeflow
3. CubeFS
CubeFS is a distributed file system tailored for cloud-native environments. It enables efficient, scalable storage management, making it perfect for ML workflows that require heavy data sharing and processing. Learn more: CubeFS
4. Fluid
Fluid accelerates data access in distributed environments by caching and preloading datasets into Kubernetes pods. Ideal for AI and ML workloads, it ensures low-latency operations for real-time data handling. Learn more: Fluid
The Game-Changers to Watch (Trial Zone)
While the Adopt tools are essential, the Trial zone is where innovation happens. These emerging technologies are worth experimenting with to prepare for the future.
Emerging tools like BentoML and MLflow are where innovation thrives. Experimenting with them now means you’re building tomorrow’s workflows today.
1. BentoML
BentoML simplifies the deployment of machine learning models. It provides a streamlined framework for packaging and deploying models, ensuring consistency and scalability in production environments. Learn more: BentoML
2. MLflow
MLflow is an open-source platform for managing the ML lifecycle. It enables tracking experiments, packaging models, and deploying them seamlessly across platforms, making it a favorite for teams bridging the gap between data science and operations. Learn more: MLflow
3. KServe
KServe specializes in model serving, offering a scalable and Kubernetes-native solution for deploying ML models. It supports multiple frameworks, including TensorFlow, PyTorch, and XGBoost, making it a versatile tool for inference tasks. Learn more: KServe
4. KubeRay
KubeRay integrates Ray, a distributed computing framework, with Kubernetes. It enables scalable ML training and hyperparameter tuning, making it a great choice for teams pushing the boundaries of distributed machine learning. Learn more: KubeRay
Why These Tools Matter
The landscape of DevOps is rapidly transforming into MLOps. As organizations incorporate AI/ML into their workflows, the demand for scalable, efficient tools has skyrocketed. Tools in the Adopt zone are production-ready and reliable, while those in the Trial zone allow you to experiment and prepare for emerging trends.
In the rapidly evolving MLOps landscape, the right tools don’t just optimize workflows—they future-proof your career.
Start with Apache Airflow and Kubeflow to master essential orchestration and lifecycle management.
Explore BentoML, MLflow, and others to understand the cutting-edge of model serving and lifecycle management.
By integrating these tools into your stack, you’ll stay ahead of the curve and cement your role as a leader in the MLOps space.
Final Thoughts
The CNCF Radar is more than just a guide—it’s a vision for what’s next. As you plan your learning and adoption strategies for 2025, prioritize tools in the Adopt quadrant and start experimenting with the Trial tools to gain a competitive edge.
I’d love to hear your thoughts: Which tools are you already using, and which are you excited to explore? Let’s discuss how these technologies can shape our workflows and careers.
Attribution: This article is based on insights from the CNCF Technology Radar: Batch/AI/ML Computing, sponsored by SlashData and the Continuous Delivery Foundation. Licensed under the Creative Commons Attribution-NoDerivatives 4.0 License. You can read the full report here.
Thank you for reading MLOps.tv! If you enjoyed this, don’t forget to share it with your Tribe. Let’s grow and thrive together in the exciting world of MLOps.
Until next time, Gourav Shah, Editor in Chief, MLOps.tv
