Machine Learning Ops: Google Cloud – Real World Data Science

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

Google Cloud Platform is gaining momentum in today’s cloud landscape, and MLOps is becoming indispensable for streamlined machine learning projects

In the fascinating journey of Data Science, there’s a significant step between creating a model and making it operational. This step is often overlooked but is crucial – it’s called Machine Learning Ops (MLOps). Google Cloud Platform (GCP) offers some powerful tools to help streamline this process, and in this course, we’re going to delve deep into them.

Topics covered in the course  : 

• CI/CD Using Cloud Build,Container and Artifact Registry
• Continuous Training using Airflow for ML Workflow Orchestration:
• Writing Test Cases
• Vertex AI Ecosystem using Python
• Kubeflow Pipelines for ML Workflow and reusable ML components
• Deploy Useful Applications using PaLM LLM of GCP Generative AI 

Why Take This Course?

• Tailored for Beginners with programming background: A basic understanding and expertise of data science is enough to start. We’ll guide you through everything else.
• Practical Learning: We believe in learning by doing. Throughout the course, real-world projects will help you grasp the concepts and apply them confidently.
• GCP Professional ML Certification Prep: While the aim is thorough understanding and implementation, this course will also provide a strong foundation for those aiming for the GCP Professional ML Certification.