I am preparing for Google Cloud Professional Machine Leargning Engineer certification . The certification is mandatory in the project I am working on.
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.
BigQuery is by far the most important storage and processing service in Google Cloud from ML perspective. It has many integrated functionalities for ML.
Some Google materials refer to it as Fully managed Tensorflow. Vertex AI assumes that data is prepared elsewhere before training the model.
Notes about fundamental ML concepts for Google Cloud ML Engineering certification. Data exploration Perform mainly univariate and bivariate analysis during initial exploration.
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.
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. Recommendations have became signigficant sub topic of machine learning.
Cloud certificates are proof of developer’s competence. But are they beneficial in practice? I am talking about the certification diploma rather than the learning process.
I have more experience from Pandas and Scikit-Learn Python libraries compared to Tensorflow. I was surprised how large the Tensorflow ecosystem with its ML engineering extensions.
Tensorflow Extended (known as TFX) is a framework to define ML pipelines. The extensions are obviously compatible with the core Tensorflow.
Keras is one of the high level APIs in Tensorflow deep learning stack. It is the recommended framework to get started with neural networks, if you do not have special requirements.
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. Vertex AI is the key MLOps product while Google Kubernetes Engine is valid alternative for custom workflows.
After 4 months of intense studying I passed the Google Cloud certification for Professional Machine Learning Engineer! Google Cloud Platform certificates are considered to be challenging compared to other cloud platforms .
Around 30 questions I memorize from the Google Cloud Professional Machine Learning certification exam. You find all sources for exam training questions from my preparation tips.