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ML Ops Platform at Cloudflare

We’ve been relying on ML and AI for our core services like Web Application Firewall (WAF) since the early days of Cloudflare. Through this journey, we’ve learned many lessons about running AI deployments at scale, and all the tooling and processes necessary. We recently launched – The Standard DAG Composer
Apache Airflow is the standard as a DAG (Directed Acyclic Graphs)-based orchestration approach. With a vast community and extensive plugin support, Airflow excels in handling diverse workflows. The flexibility to integrate with a multitude of systems and a web-based UI for task monitoring make it a popular choice for orchestrating complex sequences of tasks. Airflow can be used to run any data or machine learning workflow.

Argo Workflows – Kubernetes-native Brilliance
Built for Kubernetes, Argo Workflows embraces the container ecosystem for orchestrating workflows. It boasts an intuitive YAML-based workflow definition and excels in running microservices-based workflows. The integration with Kubernetes enables scalability, reliability, and native container support, making it an excellent fit for organizations deeply rooted in the Kubernetes ecosystem. Argo Workflows can also be used to run any data or machine learning workflow.

Kubeflow Pipelines – A Platform for Workflows
Kubeflow Pipelines is a more specific approach tailored for orchestrating machine learning workflows. “KFP” aims to address the unique demands of data preparation, model training, and deployment in the ML landscape. As an integrated component of the Kubeflow ecosystem, it streamlines ML workflows with a focus on collaboration, reusability, and versioning. Its compatibility with Kubernetes ensures seamless integration and efficient orchestration.

Temporal – The Stateful Workflow Enabler
Temporal takes a stance by emphasizing the orchestration of long-running, stateful workflows. This relative newcomer shines in managing resilient, event-driven applications, preserving workflow state and enabling efficient recovery from failures. The unique selling point lies in its ability to manage complex, stateful workflows, providing a durable and fault-tolerant orchestration solution.

In the orchestration landscape, the choice ultimately boils down to the team and use case. These are all open source projects, so the only limitation is support for different styles of work, which we find is worth the investment.


Achieving optimal performance necessitates an understanding of workloads and the underlying use cases in order to provide teams with effective hardware. The goal is to enable data scientists and strike a balance between enablement and utilization. Each workload is different, and it is important to fine tune each use case for the capabilities of GPUs and CPUs to find the perfect tool for the job.  For core datacenter workloads and edge inference, GPUs have leveled up the speed and efficiency that is core to our business. With observability and metrics consumed by Prometheus, metrics enable us to track orchestration to be optimized for performance, maximize hardware utilization, and operate within a Kubernetes-native experience.


Adoption is often one of the most challenging steps in the MLops journey. Before jumping into building, it is important to understand the different teams and their approach to data science. At Cloudflare, this process began years ago, when each of the teams started their own machine learning solutions separately. As these solutions evolved, we ran into the common challenge of working across the company to prevent work from becoming isolated from other teams. In addition, there were other teams that had potential for machine learning but did not have data science expertise within their team. This presented an opportunity for MLops to step in — both to help streamline and standardize the ML processes being employed by each team, and to introduce potential new projects to data science teams to start the ideation and discovery process.

When able, we have found the most success when we can help get projects started and shape the pipelines for success. Providing components for shared use such as notebooks, orchestration, data versioning (DVC), feature engineering (Feast), and model versioning (MLflow) allow for teams to collaborate directly.

Looking forward

There is no doubt that data science is evolving our business and the businesses of our customers. We improve our own products with models, and have built AI infrastructure that can help us secure applications and applications built with AI. We can leverage the power of our network to deliver AI for us and our customers. We have partnered with machine learning giants to make it easier for the data science community to deliver real value from data.

The call to action is this: join the Cloudflare community in bringing modern software practices and tools to data science. Be on the lookout for more data science from Cloudflare. Help us securely leverage data to help build a better Internet.

Source:: CloudFlare

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