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Serverless Machine Learning with Tensorflow on Google Cloud Platform

Welcome to Serverless Machine Learning on Google Cloud Platform

  • Data Engineers - reduced
  • Machine Learning ~ tune parameters on function to predict values
  • Machine Learning algorithms need numbers
  • Try Tensorflow http://playground.tensorflow.org

Module 1: Getting Started with Machine Learning

Vector, Matrix, Tensor!?

Rank Name Explanation
0 Scalar (magnitude only)
1 Vector (magnitude and direction)
2 Matrix (table of numbers)
3 3-Tensor (cube of numbers)
n n-Tensor (you get the idea)

Terms Defined:

  • Input -
  • Weights -
  • Batch Size - size of evaluation
  • Gradient Decsent - method of evaluation (lowest error)
  • Evaluation - evaluating over entire dataset
  • Training -
  • Epoch - traversal through dataset

Create Machine Learning Datasets

In this lab, you will:

Explore a dataset using BigQuery and Datalab Sample the dataset and create training, validation, and testing datasets for local development of TensorFlow models Create a benchmark to evaluate the performance of ML against What you need To complete this lab, you need:

A Google Cloud Platform project (if not, please sign up for a free trial and come back here). Begin the lab https://codelabs.developers.google.com/codelabs/dataeng-machine-learning/

Module 2: Building ML models with Tensorflow

Tensorflow

  • Data flow graphs - separate construction and execution
  • Portable (C++)

Getting Started with Tensorflow

In this lab, you will learn the following on how the TensorFlow Python API works:

  • Building a graph
  • Running a graph
  • Feeding values into a graph
  • Find area of a triangle using TensorFlow

Begin the Lab https://codelabs.developers.google.com/codelabs/dataeng-machine-learning/index.html?index=#5

Note: You should only complete Parts 1-5 of this Codelab and then return to this course.

Machine Learning using tf.learn

In this lab, you will implement a simple machine learning model using tf.learn:

  • Read .csv data into a Pandas dataframe
  • Implement a Linear Regression model in TensorFlow
  • Train the model
  • Evaluate the model
  • Predict with the model
  • Repeat with a Deep Neural Network model in TensorFlow

Begin the Lab https://codelabs.developers.google.com/codelabs/dataeng-machine-learning/index.html?index=#6

Note: Only complete Part 7 of the Codelab and then return to this course.

TensorFlow on Big Data

In this lab, you will learn how to:

  • Read from a potentially large file in batches
  • Do a wildcard match on filenames
  • Break the one-to-one relationship between inputs and features

Begin the Lab https://codelabs.developers.google.com/codelabs/dataeng-machine-learning/index.html?index=#7

Note: Only complete Part 8 of the Codelab and then return to this course.

Module 3: Scaling ML models with Cloud ML Engine

Module 4: Feature Engineering