The Google Cloud for Data Scientists training course is designed to prepare beginner data scientists and machine learning practitioners to implement regression and classification models in TensorFlow using both structured and unstructured data and then serve the models, elastically and resiliently, with Google Cloud.
The course begins with students getting to know the core data science and machine learning concepts that will be important throughout the course. Next, students will use Google Cloud Jupyter notebook hosting environment, Colab, to prepare a structured dataset sourced from a publicly accessible and Serverless data warehouse. Students will use the dataset to explore basic features of TensorFlow, including its various Application Programming Interfaces (APIs), and learn how to use Google Cloud Machine Learning Engine for distributed training, hyperparameter tuning, and serving of your model as a web service based API. Students will learn how to avoid the training-serving skew problem with an effective feature processing pipeline and explore the importance of feature engineering for building high-performance machine learning systems based on case studies and best practices. The remainder of the course will leverage previous learning about using Google Cloud for structured data and apply it to unstructured data and image classification. The course concludes with students working with convolutional neural networks, implementing changes to TensorFlow models to use convolutional layers, batch normalization, dropout, transfer learning, and applying image-specific data augmentation techniques.
This course targets beginner data scientists and machine learning engineers who have some experience developing with Python, SQL, and Linux Shell. The course will be conducted on Google Cloud Platform. All students need is a reasonably powerful laptop running an up-to-date browser (preferably Chrome).
Purpose
|
Learn how to create and deploy high-performance data science and machine learning systems on Google Cloud for regression and classification use cases leveraging both structured and unstructured datasets. |
Audience
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Developers and developer teams looking to use Google Cloud in a data science context. |
Role
| Data Engineer - Data Scientist - Software Developer - Technical Manager |
Skill Level
| Introduction |
Style
| Fast Track - Hack-a-thon - Learning Spikes - Workshops |
Duration
| 3 Days |
Related Technologies
| Google Cloud | Tensorflow |
Productivity Objectives
- Adopt TensorFlow to create regression and classification models.
- Deploy statistical and deep learning models to Google Cloud for training and serving.
- Apply feature engineering to structured and unstructured datasets.
- Optimize performance metrics of regression and classification models.
- Evaluate and use end-to-end data science and machine learning pipelines.