MLOps in Google Cloud
Google Cloud Platform has excellent toolset to operationalize and productionize machine learning models. Vertex AI is the key MLOps product while Google Kubernetes Engine is valid alternative for custom workflows.
Tensorflow Extended (TFX) for MLOps
Tensorflow Extended (known as TFX) is a framework to define ML pipelines. The extensions are obviously compatible with the core Tensorflow.
Dataflow for ML Engineers in Google Cloud
Dataflow product in Google Cloud is mandatory for advanced data processing pipelines for machine learning solutions. It performs typical data engineering tasks by allowing same code to execute both batch and streaming.
Vertex AI for ML Engineers in Google Cloud
Some Google materials refer to it as Fully managed Tensorflow. Vertex AI assumes that data is prepared elsewhere before training the model.
Machine learning products in Google Cloud
This is a summary of Google Cloud Platform (GCP) products relevant for Machine Learning Engineer role. Google philosphy seems to be that moving to their platform requires minimal changes to the existing solution.
Running Flask frontend and backend in Kubernetes
Kubernetes have been everywhere lately. Especially in the context of MLOps. I gave it a try by creating web app with Python Flask.
Comparison of machine learning platforms in major clouds
Comparing the major machine learning platforms AWS SageMaker, Azure Machine Learning, Google Vertex AI and Databricks.
What is a machine learning platform?
What is a machine learning platform? Introducing different components such as workbench, MLOps tools and cloud computation.