Real-world End to End Machine Learning Ops on Google Cloud
Real-world End to End Machine Learning Ops on Google Cloud
English | 2025 | h264, yuv420p, 1920x1080 | 48000 Hz, 2channels | Duration: 6h 3m | 565 MB
English | 2025 | h264, yuv420p, 1920x1080 | 48000 Hz, 2channels | Duration: 6h 3m | 565 MB
This course guides learners through practical ML Ops on Google Cloud, from setup to advanced workflow orchestration. It starts with GCP services and ML Ops fundamentals, then covers building CI/CD pipelines using Cloud Build, Artifact Registry, and Cloud Run. Participants deploy scalable, containerized ML models and perform automated testing.
Next, learners use Airflow with Cloud Composer for continuous training, keeping models updated and managing failures with alerts. The course dives into Vertex AI for efficient model training, deployment, registry management, and predictions, supported by CI/CD automation. Advanced topics include Kubeflow Pipelines, hyperparameter tuning, explainability, and model versioning, enabling refined, transparent AI solutions.
The final section covers generative AI and large language models like PaLM 2 on Google Cloud, with hands-on labs and custom deployments. Practical assignments throughout ensure real-world skills to confidently manage end-to-end ML Ops workflows.