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#04 Google Cloud Professional Machine Learning Engineer (PMLE) Guide
Are you ready to dive deep into the world of machine learning?
This episode is your comprehensive audio guide to becoming a professional machine learning engineer, drawing from a wealth of information and practical tips. We'll unpack the core concepts, from choosing the right ML model for your specific business needs to mastering data preparation and processing. You'll gain insights into leveraging low-code AI solutions like AutoML and pre-built ML APIs, and understand when to use custom models with frameworks like TensorFlow and KubeFlow.
We'll explore:
Key machine learning techniques such as Regression, Association, Classification, Clustering and Reinforcement learning
The importance of feature engineering, and hyperparameter tuning, and how to choose the best optimizers for your models.
Popular architectures like Linear Classifiers, DNN Classifiers, and Wide and Deep networks.
How to handle different types of data like tabular, text, speech, images, and videos.
Model evaluation metrics and how to monitor your models to ensure high accuracy.
Strategies for scaling ML models with Vertex AI Feature Store, and understanding Vertex AI's prediction capabilities.
Automating and orchestrating ML pipelines with tools like Kubeflow and Vertex AI Pipelines.
We'll also delve into advanced topics such as generative AI models like GANs, TensorFlow Probability, and techniques like embeddings. You’ll understand how to deal with common challenges like overfitting and class imbalance. You'll learn how to use various tools for model explainability including the What-If Tool and the Language Interpretability Tool.
This episode is a must-listen for anyone preparing for the Professional Machine Learning Engineer exam, or those just seeking a better understanding of real-world ML applications and the powerful tools available on Google Cloud.
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By Paul KamauAre you ready to dive deep into the world of machine learning?
This episode is your comprehensive audio guide to becoming a professional machine learning engineer, drawing from a wealth of information and practical tips. We'll unpack the core concepts, from choosing the right ML model for your specific business needs to mastering data preparation and processing. You'll gain insights into leveraging low-code AI solutions like AutoML and pre-built ML APIs, and understand when to use custom models with frameworks like TensorFlow and KubeFlow.
We'll explore:
Key machine learning techniques such as Regression, Association, Classification, Clustering and Reinforcement learning
The importance of feature engineering, and hyperparameter tuning, and how to choose the best optimizers for your models.
Popular architectures like Linear Classifiers, DNN Classifiers, and Wide and Deep networks.
How to handle different types of data like tabular, text, speech, images, and videos.
Model evaluation metrics and how to monitor your models to ensure high accuracy.
Strategies for scaling ML models with Vertex AI Feature Store, and understanding Vertex AI's prediction capabilities.
Automating and orchestrating ML pipelines with tools like Kubeflow and Vertex AI Pipelines.
We'll also delve into advanced topics such as generative AI models like GANs, TensorFlow Probability, and techniques like embeddings. You’ll understand how to deal with common challenges like overfitting and class imbalance. You'll learn how to use various tools for model explainability including the What-If Tool and the Language Interpretability Tool.
This episode is a must-listen for anyone preparing for the Professional Machine Learning Engineer exam, or those just seeking a better understanding of real-world ML applications and the powerful tools available on Google Cloud.