Real-world End to End Machine Learning Ops on Google Cloud

From Model Development to Deployment: Streamlining Machine Learning Workflows on Google Cloud

Created by: Sid Raghunath
Cloud/Data Engineering/Analytics/Architecture

What Will I Learn?

• Comprehensive understanding of Google Cloud Platform's suite for MLOps, diving deep into tools like Airflow,Cloud Build, Google Container and Artifact Registry
• Hands-on proficiency in orchestrating, deploying, and monitoring machine learning workflows using GCP Composer/Airflow and Vertex AI services.
• Best practices and methodologies to ensure scalable, reproducible, and efficient machine learning pipelines on the cloud.
• Insights and techniques tailored to help in preparation for the GCP Professional ML Certification exam, bolstering your credentials in the cloud ML domain.

Requirements

• Basic experience in developing Data science models ,concepts and terminologies
• Working knowledge of Python, as the course will involve hands-on coding and scripting
• Prior basic understanding and experience on using Google Cloud platform
• Desire to expand and deepen skill sets in MLOps and cloud-based machine learning solutions

Target audience

• Data scientists and machine learning engineers looking to streamline their ML workflows and deploy models efficiently using Google Cloud Platform.
• Cloud professionals aiming to specialize in machine learning operations and seeking hands-on experience with GCP's suite of tools.
• Developers and IT professionals who want to understand the intersection of cloud computing and machine learning, and how to harness them together effectively.
• Teams or individuals preparing for the GCP Professional ML Certification exam and seeking comprehensive coverage of the required topics.
• Anyone interested in staying updated with the latest trends in cloud-based machine learning and MLOps practices.