MLOps, which began as a set of best practices to reliably and efficiently deploy and maintain ML models in production, is fast evolving into a robust, independent approach to ML lifecycle management.
MLOps applies to the entire lifecycle of a model, allowing data, development and production teams to work more closely together, impacting everything from diagnostics to business metrics.
According to Cognilytica, the MLOps market is expected to grow by almost $4 billion by 2025.
The current environment offers plenty of opportunity for growth and increased efficiency. In a Forrester survey of business decision-makers, 22% reported a 1 to 3 month deployment process for newly developed ML models and an additional 18% reported the process taking longer than 3 months.
Inefficient timelines and workflows mean many ML projects fail before ever delivering business value, but as the field of MLOps continues to mature, the productivity and revenue gains from machine learning will rise dramatically.
Kubeflow & MLflow
Two open-source projects, Kubeflow and MLflow, are key to the MLOps gains we see developing.
Kubeflow, based on Google's method to deploy TensorFlow models, is a cloud-native framework designed to simplify complicated ML workflows on Kubernetes and make the deployments portable and scalable. CoreWeave is deeply committed to integrating and supporting open-source Kubernetes projects, like Kubeflow.
- Notebooks: Kubeflow allows users to create and manage Jupyter notebooks, which allow multiple users to contribute to a project simultaneously.
- Pipelines: Kubeflow is pipeline based, allowing users to build and deploy scalable, portable ML workflows.
- Training: Developers can train their ML models on Kubeflow, using a variety of frameworks.
MLflow, who’s core philosophy is to put as few constraints as possible on your workflow, manages the entire ML lifecycle with enterprise reliability, security and scale. MLflow is compatible with any ML library, able to determine most aspects of any code by convention and integrates into an existing codebase with minimal changes.
- Tracking: MLflow provides an API and UI which allows users to log parameters, code versions, metrics and output files to be visualized later.
- Project: MLflow packages reusable data science code with each project housing a code directory that uses descriptor files to indicate dependencies and how to run the code.
- Models: MLflow distributes ML models in a variety of languages with tools to assist in deployment. Each model is then saved as a directory.
- Flexibility: Users can access each MLflow component separately, to for example export Models without using Tracking or Projects, however, the components are designed to work well together.
- Experimentation: MLFlow supports experimentation, reproducibility, deployment and a central model registry.
As MLOps tools advance, CoreWeave clients can take full advantage of the newest resources, which frees them up to focus on what they do best. CoreWeave offers a flexible infrastructure, on top of which clients can implement their favorite MLOps tools to manage the entirety of their training pipeline, before serving models at scale on CoreWeave's industry leading inference stack.
Model training requires a massive scale of GPU resources, and can be complex from a technical perspective. Here’s why the most exciting ML and AI companies are partnering with CoreWeave:
- Right-Size Your Workloads: No two models are the same, and neither are their compute requirements. With the industry’s broadest selection of GPUs on top of the industry’s fastest and most flexible cloud infrastructure, CoreWeave allows you to optimize your workloads.
- Bare-Metal via Kubernetes: Remove hypervisors from your stack by deploying containerized workloads. CoreWeave empowers you to realize the benefits of bare-metal without the burden of managing infrastructure.
- Machine Learning DNA: Machine Learning is in our DNA, and our infrastructure reflects it. Whether you are training or deploying models, we built CoreWeave Cloud to reduce your set-up and improve performance.
- Inference Service: CoreWeave delivers the industry’s leading inference solution, complete with 5-second spin-up times and responsive auto-scaling to help you serve models as efficiently as possible. In addition to maximizing performance, we build infrastructure to optimize spend, so you can scale confidently without breaking your budget.
- Model Training:With state-of-the-art 40GB and 80GB A100 distributed training clusters and North America's largest deployment of A40s, CoreWeave is built to encourage scale, not inhibit it.
- DevOps & MLOps: Full documentation available to get up & running quickly. Consider our engineers as an extension of your team, providing DevOps and MLOps experience to help you optimize your workloads
As organizations around the world face the complexities of building and training increasingly large models, CoreWeave is here to help. We reduce the complexities of managing infrastructure, so you can focus on what you do best; build and train powerful, world changing models.
The future is bright. The ML and AI applications are unlimited. Contact CoreWeave today to get started.