This GCP-AMLTF: Advanced Machine Learning with TensorFlow on Google Cloud course will give you hands-on experience optimizing, deploying, and scaling a variety of production ML models. You’ll learn how to build recommendation systems
and scalable, accurate, and production-ready models for structured data, image data, time series, and natural language text.

Skills Covered

  • Implement the various flavors of production ML systems—static, dynamic, and continuous training; static and dynamic inference; and batch and online processing.
  • Solve an ML problem by building an end-to-end pipeline, going from data exploration, preprocessing, feature engineering, model building, hyperparameter tuning, deployment, and serving.
  • Develop a range of image classification models from simple linear models to high-performing convolutional neural networks (CNNs) with batch normalization, augmentation, and transfer learning.
  • Forecast time-series values using CNNs, recurrent neural networks (RNNs), and LSTMs.
  • Apply ML to natural language text using CNNs, RNNs, LSTMs, reusable word embeddings, and encoder-decoder generative models.
  • Implement content-based, collaborative, hybrid, and neural recommendation models in TensorFlow.

Who Should Attend

  • Data Engineers and programmers interested in learning how to apply machine learning in practice.
  • Anyone interested in learning how to leverage machine learning in their enterprise.

Course Curriculum


To get the most out of this training, participants should have:

  • Knowledge of machine learning and TensorFlow to the level covered in Machine Learning on Google Cloud coursework.
  • Experience coding in Python.
  • Knowledge of basic statistics.
  • Knowledge of SQL and cloud computing (helpful).

Download Syllabus