-
GCP Updates and Use Cases
-
On-premise's workloads Migration to GCP
-
DataPipeline Tools
- Cloud Pub/Sub Documentation + release blog
- Cloud Dataflow Documentation + release blog + templates
- Data Studio Documentation + templates
- BigQuery - Scheduling queries
- BigQuery ML - Creating a k-means clustering model
- Blog Post: Building a data science team
- Blog Post: Ten roles for your data science team
-
Build ML models on GCP
-
Sample Resource at training-data-analyst
- Machine Learnning
- courses/machine_learning/deepdive2/launching_into_ml/labs/decision_trees_and_random_Forests_in_Python.ipynb
- courses/machine_learning/deepdive2/launching_into_ml/labs/improve_data_quality.ipynb
- courses/machine_learning/deepdive2/launching_into_ml/labs/intro_linear_regression.ipynb
- courses/machine_learning/deepdive2/launching_into_ml/labs/intro_logistic_regression.ipynb
- courses/machine_learning/deepdive2/launching_into_ml/labs/decision_trees_and_random_Forests_in_Python.ipynb
- courses/machine_learning/deepdive2/launching_into_ml/labs/python.BQ_explore_data.ipynb
- TensorFlow (lower layer code)
- courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/tensors-variables.ipynb
- courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/write_low_level_code.ipynb
- Load data into tf.data
- courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/load_images_tf.data.ipynb
- courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/load_diff_filedata.ipynb
- courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/feat.cols_tf.data.ipynb
- TFRecord and tf.example
- courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/tfrecord-tf.example.ipynb
- courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/2_dataset_api.ipynb
- Machine Learnning
More than 1 year has passed since last update.
Good References About GCP for ML Engineer
Last updated at Posted at 2022-01-16
Register as a new user and use Qiita more conveniently
- You get articles that match your needs
- You can efficiently read back useful information
- You can use dark theme