MLOps and how can DevOps be extended to MLOps?
Machine Learning can deliver value only when they are deployed in production; however, deploying and managing them is a challenging task, particularly when it requires at scale. DevOps is a well-established framework today as it focuses more on automating and streaming infrastructure/software development and deployment. The principles of DevOps can also be extended to MLOps for the lifecycle of machine learning models.
DevOps works on the CI/CD (Continuous Integration/Continuous Deployment) model, and it ensures code changes are verified and deployed reliably. In MLOps, this framework needs to be extended to CT (Continuous Training) as well so that model retraining can be achieved when new data arrives and to ensure models stay relevant.
DevOps tracks application code changes, and the same feature needs to be extended to MLOps, where versioning of data, models, hyperparameters, and training code can be maintained and managed to ensure reproducibility.
The DevOps framework enables application monitoring/performance and MLOps also requires monitoring of model drift, data drift and prediction accuracy in real time to be more consumer friendly and ensure performance degradation is detected and corrected.
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DevOps uses tools like Terraform, AWS CloudFormation, Pulumi, Ansible, Salt, Puppet, etc., to manage infrastructure as code (IaC). MLOps requires IaC to be extended to automate ML pipelines using tools like AWS SageMaker Pipelines, Kubeflow, MLflow, Azure Data factory, Vertex AI Pipelines, etc.
And when it comes to collaboration, DevOps brings developers and operations teams together, similarly, MLOps needs to bring together data scientists, engineers, DevOps, and business teams to ensure the respective ML models deliver business value.
Like DevOps at initial days had its challenges, ML introduces new complexities that require specialized skills, tools, and workflows. As the requirements of ML and AI become more prominent, organizations require the delivery of AI models faster, more reliable, and at scale. Certainly, when they are deployed in production and consumed by the customer, an effective monitoring in real-time would be an utmost requirement for the organizations to make it more effective and efficient.
Please comment your opinion about the challenges and gaps you see today in the overall framework of the AI and ML deployment and management Lifecycle.
Cloud & Enterprise Architecture | Driving Digital Transformation & Innovation
1 个月One big challenge is making sure AI models are reproducible, which means keeping track of datasets, hyperparameters, and training code so everything stays consistent. Adding continuous training into DevOps pipelines isn’t easy because models need to retrain automatically without breaking things. Scaling AI systems while keeping costs under control is tricky since these workloads need a lot of computing power. Monitoring for data drift in real-time is another struggle because models lose accuracy over time and need retraining. On top of that, getting data scientists, engineers, DevOps, and business teams to work together smoothly is tough since they all have different priorities and ways of thinking. That said, these challenges are exactly why MLOps is such an exciting space, and as the ecosystem matures, we're seeing more robust solutions emerge to tackle them. Your blog does a great job of highlighting the parallels between DevOps and MLOps while emphasizing the need for automation, monitoring, and collaboration. It’s a well-structured and insightful take on how organizations can bridge the gap between machine learning models and real-world deployment. Great read!