What We Will Discuss:

1. An end-to-end environment for AI development, from data engineering to model deployment, provided by a cloud service. This service integrates with various GCP services and utilizes cutting-edge Google AI technology.
2. A hosted Jupyter notebook solution that enables data scientists to spin up JupyterLab instances with pre-configured ML frameworks and tools. This solution allows easy access and use of GCP services from within the notebooks.
3. A serverless and no-ops ML training service that supports distributed training infrastructure with CPUs, GPUs, and TPUs. This service also provides hyperparameter tuning and pre-built algorithms for TensorFlow, Scikit-learn, and XGBoost models.
4. A human labeling service that provides high-quality labeled data for unstructured data types, such as images, videos, and text. This service also offers continuous evaluation for model performance.

How to prepare for this session?

To enhance your certification journey, we recommend completing the MLE Learning Path courses on Google Cloud Skills Boost.

07 Production Machine Learning Systems (link)

08 Computer Vision Fundamentals with Google Cloud (link)