Free MLOps Workshop (Apr 14)

A free machine learning operations workshop is scheduled for Tuesday, April 14, with hands-on labs focused on getting models into production. (x.com) Machine learning operations, or MLOps, is the engineering work that turns a model from a notebook into a service that can be tested, deployed, updated, and watched in production. Google’s current training materials define it as deploying, testing, monitoring, and automating machine learning systems in production. (www.skills.google) The workshop listing says the April 14 session will cover end-to-end workflows, continuous integration and continuous delivery across Amazon Web Services, Microsoft Azure, and Google Cloud, plus Kubeflow, feature stores, and large language model operations. The description says the format is lab-based rather than lecture-only. (x.com) Continuous integration and continuous delivery means code changes trigger automated build, test, and deployment steps instead of manual releases. Amazon Web Services says those pipelines are sets of automated instructions that usually build software, run tests, and deploy code to an environment. (docs.aws.amazon.com) The cloud angle matters because each major provider now publishes its own MLOps playbook. Microsoft Learn has a current Azure Machine Learning path on end-to-end MLOps with GitHub Actions and production deployment, while Google Cloud teaches MLOps on Vertex AI with pipeline labs and automation. (learn.microsoft.com) (www.skills.google) Kubeflow is one of the open-source toolkits in that stack. The Kubeflow project describes its pipelines as reusable end-to-end machine learning workflows, and its site now also highlights Kubernetes-native training for large language model fine-tuning. (github.com) (www.kubeflow.org) Feature stores are shared systems for saving the input signals a model uses, so training and live predictions pull from the same source instead of drifting apart. Google’s MLOps architecture guide places feature handling inside the production pipeline alongside validation, training, evaluation, deployment, and continuous retraining. (docs.cloud.google.com) Large language model operations, or LLMOps, is the same production discipline applied to generative artificial intelligence systems, including fine-tuning, prompt management, and vector databases. Amazon Web Services says its MLOps guidance can extend to large language model operations in generative artificial intelligence workloads. (docs.aws.amazon.com) The pitch for the April 14 session is practical training: build the pipeline, run the cloud tooling, and ship a model the way platform teams expect. That makes the workshop less about learning one library and more about learning the release process around machine learning itself. (x.com)