Oracle Corporation

01/23/2025 | Press release | Distributed by Public on 01/23/2025 16:23

MLOps Simplified: Deploying MLflow On OCI

Organizations are increasingly adopting machine learning to achieve their goals and stay competitive, but to unlock its full value, they need machine learning operations (MLOps).

MLOps is the practice of streamlining and automating ML workflows, enabling scalable deployments, efficient collaboration, and consistent model performance.

Here, MLflow comes in. As an open-source platform, MLflow provides the essential tools to implement MLOps practices effectively. It acts as a bridge between ML development and deployment,helping ensure that every step of the ML lifecycle is well-managed and reproducible.

With MLflow, you can track and log every detail of your ML experiments, including parameters, metrics, and artifacts, all in one centralized location. It allows you to compare different models and their performance side by side, making it easier to select the best one for deployment. MLflow's autologging feature automatically captures training details without manual input, saving time and reducing errors. It provides tools to register models with version control, manage their lifecycle, and deploy them to various environments seamlessly.

The platform supports team collaboration through shared workspaces, making it simple to share results and insights. MLflow also ensures reproducibility, so your workflows run consistently regardless of the environment.

An automation on GitHub deploys MLflow on OCI and describes the process.

Core concepts in MLflow
MLflow has the following 4 compoments:

Tracking: Tracks and logs experiments, metrics, parameters, and models. Enables comparative analysis of different runs.
Projects: Packages reusable code in a standardized format for collaborative projects.
Models: Provides a standard unit for packaging and reusing machine learning models.
Registry: A central model repository that supports versioning and lifecycle management of models.
MLflow and Kubeflow: A key comparison
When comparing MLflow to Kubeflow, both serve distinct purposes. MLflow focuses on tracking experiments and managing the ML lifecycle, making it lightweight and user friendly. On the other hand, Kubeflow is designed for automating ML workflows and orchestrating pipelines. While MLflow has a less steep learning curve and is easier to integrate across platforms, Kubeflow shines in complex scenarios requiring extensive pipeline automation. Depending on your needs, either platform can be the right choice, but MLflow's simplicity and flexibility make it ideal for many ML use cases.

Integrating MLflow with OCI
Combining MLflow with Oracle Cloud Infrastructure (OCI) unlocks significant advantages. OCI's managed Kubernetes service, Oracle Kubernetes Engine (OKE), provides a scalable and reliable environment for deploying MLflow. OCI Object Storage acts as a secure repository for artifacts like datasets and models, while Resource Manager simplifies the deployment. To enhance security, you can also use Oracle Integrated Application with MLflow, offering advanced authentication and user management.

Deploying MLflow on OCI creates an ecosystem where teams can focus on creating and refining models without worrying about infrastructure challenges. By using OCI's services, MLflow becomes a powerful tool for end to end machine learning lifecycle.

One click deployment
With Oracle Resource Manager, you can deploy a fully configured MLflow environment in just a few steps. The process includes setting up the network, provisioning an OKE cluster, an Object Storage bucket and configuring a load balancer. This integration allows MLflow to operate efficiently on Kubernetes with minimal configuration.

Conclusion
By adopting MLflow on OCI, organizations gain the ability to focus on what truly matters: Creating impactful ML solutions. This powerful integration empowers teams to innovate with confidence, knowing their infrastructure is built for success.

For more information, see the following resources:

MLflow
OCI Resource Manager
OCI Object Storage
Oracle Kubernetes Engine
OCI Integrated Application