How can DevOps help ML to train models cheaper

Machine learning algorithms can be very useful for a wide variety of applications such as in medicine, email filtering, speech recognition, computer vision, and others. It is an inevitable decision if no fully satisfactory algorithm is available.

Creating and training a machine learning model can be costly. But only if you don’t know how to optimize your costs. You can achieve it with DevOps practices.

It may sound as if DevOps is the answer to everything. But you have to try in order to make sure: start with improving the infrastructure, continue with automation, then CI/CD, and so on. Between these stages, check how much your costs had changed.

But for starters, here’s how DevOps can help to cut costs on training:

Infrastructure

It is the most important part: DevOps can optimize your costs on the infrastructure. Here are some best practices to do it in order to improve your work with ML:

1. Infrastructure as Code or IaC allows organizations to automate infrastructure provisioning and management. Define infrastructure as code to get rid of manual infrastructure management and easier replicate it and scale.
2. Cloud Computing: DevOps practices can be leveraged to take advantage of cloud computing, which can reduce the cost of infrastructure for ML projects. Cloud providers offer flexible, pay-as-you-go models, which can optimize the upfront costs of infrastructure and allow organizations to scale it as needed.
3. Containers and Kubernetes: These are powerful tools for managing infrastructure. They simplify the deployment and scaling of applications and services.
4. Auto-scaling: Allows organizations to automatically adjust the resources required to support ML models in production. By automatically scaling the infrastructure, organizations can cut the costs of unused resources and ensure that models have enough of them to perform optimally.

Automation

Since automation is at the core of DevOps, it can help organizations to reduce the time and resources required to build, deploy, and maintain ML models. Automating repetitive tasks, such as data preparation and model deployment, can free up time for data scientists and engineers to focus on more valuable tasks.

Continuous Integration and Delivery

DevOps practices, such as continuous integration and delivery, can help to quickly and consistently deploy ML models to production. This can reduce the time and costs of manual deployment and improve the reliability of ML models in production.

Collaboration

Collaboration between development and operations teams is crucial for DevOps and can help organizations rapidly identify and resolve issues with ML models. By breaking down silos between teams and promoting open communication, DevOps can help to decrease the time and costs associated with troubleshooting and maintenance.

Monitoring and Maintenance

Monitoring and maintenance allow your ML models to run smoothly in production. By proactively monitoring models and making updates as needed, you can save the time you would need to fix problems and maintain models.

Conclusion

DevOps practices are useful to reduce costs associated with ML projects. Automate processes, improve collaboration, and streamline deployment and maintenance to optimize your expenses. It is also very important to pay attention to the infrastructure of your project. With Infrastructure as Code, cloud computing, containers and Kubernetes, and auto-scaling you can perform better and more efficiently.

If you consider DevOps practices to enhance your ML project, we share your idea. It can help your organization to deliver ML models faster and more reliably.

Want to implement DevOps in your project to get the most out of it? Reach out to us via contact@dedicatted.com.