Machine Learning (ML) solutions often start on a simple platform like a virtual machine, which is great for initial research. However, as the system scales and enters production, automation becomes crucial. Cloud suites such as Google Vertex AI, Azure Machine Learning, and AWS Sagemaker, can streamline this process.
For example, model training is more efficient with a managed service that automatically scales compute resources based on your training needs, eliminating the cost of idle resources –- as happens when you use the Jupyter Notebook or a VM alone for training.
We’ll cover all parts of the ML process, including development, hypertuning, deployment for inference, experiments, model management, monitoring, performance, and operating the entire pipeline.
Joshua Fox has 20 years experience as a software architect in software product companies, and now advises tech companies on their gnarliest cloud challenges as a senior cloud architect at DoiT International. See more at joshuafox.com/publications
