Deploying AI/ML based applications is far from trivial. On top of the traditional DevOps challenges, you need to foster collaboration between multidisciplinary teams (data-scientists, data/ML engineers, software developers and DevOps), handle model and experiment versioning, data versioning, etc. Most ML/AI deployments involve significant manual work, but this is changing with the introduction of new frameworks which leverage cloud-native paradigms, Git and Kubernetes to automate the process of ML/AI-based application deployment.
In this session we will explain how ML Pipelines work, the main challenges and the different steps involved in producing models and data products (data gathering, preparation, training/AutoML, validation, model deployment, drift monitoring and so on). We will demonstrate how the development and deployment process can be greatly simplified and automated. We’ll show how you can: a. maximize the efficiency and collaboration between the various teams, b. harness Git review processes to evaluate models, and c. abstract away the complexity of Kubernetes and DevOps.
We will demo how to enable continuous delivery of machine learning to production using Git, CI frameworks (e.g. GitHub Actions) with hosted Kubernetes, Kubeflow, MLOps orchestration tools (MLRun), and Serverless functions (Nuclio) using real-world application examples.