I am preparing for Google Cloud Professional Machine Learning Engineer certification .
This is a summary of Google Cloud Platform (GCP) products relevant for Machine Learning Engineer role.
BigQuery is by far the most important storage and processing service in Google Cloud from ML perspective.
Some Google materials refer to it as Fully managed Tensorflow.
Notes about fundamental ML concepts for Google Cloud ML Engineering certification.
Dataflow product in Google Cloud is mandatory for advanced data processing pipelines for machine learning solutions.
I heard about artificial neural networks first time around 2017. Since then I have tried to understand their behavior and explain them in a simple way.
Recommendation systems are useful to personalize experience and find relevant items among huge catalogs.
Cloud certificates are proof of developer’s competence. But are they beneficial in practice?
I have more experience from Pandas and Scikit-Learn Python libraries compared to Tensorflow.
Tensorflow Extended (known as TFX) is a framework to define ML pipelines.
Keras is one of the high level APIs in Tensorflow deep learning stack.
Some notes about image recognition while preparing for Google Cloud MLE certification.
Natural Language Processing (NLP) refers to tools and methods to explore text data as well as identifiy patterns and making predictions.
Google Cloud Platform has excellent toolset to operationalize and productionize machine learning models.
After 4 months of intense studying I passed the Google Cloud certification for Professional Machine Learning Engineer!
Around 30 questions I memorize from the Google Cloud Professional Machine Learning certification exam.