What you'll 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.
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/CDUsing Cloud Build,Container and Artifact Registry
Continuous Training using Airflow for MLWorkflow Orchestration:
Writing Test Cases
Vertex AIEcosystem using Python
Kubeflow Pipelines for ML Workflow and reusable ML components
Deploy Useful Applications using PaLM LLMof GCPGenerative 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.
Your Takeaways
By the end of this course, you won't just understand the theory behind MLOps, you'll be equipped to implement it. The practical experience gained will empower you to handle real-world ML challenges with confidence.
The relevance of machine learning in today's world is undeniable, and with the rise of its importance, there's an increasing demand for professionals skilled in MLOps. This course is designed to bridge the gap between model development and operational excellence, making ML more than just a coding exercise but a tangible asset in solving real-world problems.
So, if you're eager to elevate your ML journey and understand how to make your models truly effective on a platform as powerful as Google Cloud, this course awaits you. Dive in, explore, learn, and let's make ML work for the real world together!
