Good References About GCP for ML Engineer

- GCP Updates and Use Cases
    - [Google Cloud blog](https://cloud.google.com/blog/products)
    - [Google Cloud big data product list](https://cloud.google.com/products/big-data/)
    - [Google Cloud customers and case studies](https://cloud.google.com/customers/#/)

- On-premise's workloads Migration to GCP
    - [Migrating Hadoop to Google Cloud](https://cloud.google.com/solutions/migration/hadoop/hadoop-gcp-migration-overview)
    - [Database release log](https://cloud.google.com/blog/products/databases)
    - [Dataproc release log](https://cloud.google.com/blog/products/dataproc)
- DataPipeline Tools
    - [Cloud Pub/Sub Documentation](https://cloud.google.com/pubsub/) + [release blog](https://cloud.google.com/pubsub/docs/release-notes)
    - [Cloud Dataflow Documentation](https://cloud.google.com/dataflow/) + [release blog](https://cloud.google.com/blog/products/data-analytics/) + [templates](https://cloud.google.com/dataflow/docs/guides/templates/provided-templates)
    - [Data Studio Documentation](https://developers.google.com/datastudio/) + [templates](https://datastudiogallery.appspot.com/gallery)
    - [BigQuery - Scheduling queries](https://cloud.google.com/bigquery/docs/scheduling-queries)
    - [BigQuery ML - Creating a k-means clustering model](https://cloud.google.com/bigquery-ml/docs/kmeans-tutorial)
    - [Blog Post: Building a data science team](https://towardsdatascience.com/how-to-hire-a-machine-learning-team-b8055fff57f)
    - [Blog Post:  Ten roles for your data science team](https://hackernoon.com/top-10-roles-for-your-data-science-team-e7f05d90d961)
- Build ML models on GCP
    - [Cloud AutoML](https://cloud.google.com/automl/)
    - [BigQuery ML](https://cloud.google.com/bigquery/docs/bigqueryml-intro)
    - [Building custom ML models with TensorFlow](https://www.tensorflow.org/tutorials/)



- Sample Resource at [training-data-analyst](https://github.com/kackey0-1/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