Gain insights into industry-level machine learning by applying advanced TensorFlow and Python techniques. Explore efficient methods for creating scalable models, from data pipelines to deployment and inference.

AML-DLI.tar.gz

case-study

In this course, you'll level up your skills learned in the Industry Case Study and Machine Learning for Software Engineers. You'll take the modeling and data pipeline concepts and apply them to production-level classification and regression models for industry deployment, while continuing to practice the most efficient techniques for building scalable machine learning models. After this course, you will be able to complete industry-level machine learning projects, from data pipeline creation to model deployment and inference.

The code for this course is built around the TensorFlow framework, one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Knowledge of Python and TensorFlow are prerequisites. 

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Applied Machine Learning: Deep Learning for Industry

# Chapter Goals:
- Understand how a `MonitoredTrainingSession` works
- Learn about saving checkpoints and tracking scalar values during training
- Train a machine learning model using a `MonitoredTrainingSession`

# A. Logging values
While `tf.summary.scalar` lets us keep track of certain values in an events file for TensorBoard, it is also useful to directly log values to STDOUT during training. For instance, it is customary to log the loss and iteration count, so we can stop training if there is an issue.

You'll notice each line of output is prepended by "INFO:tensorflow". This just means the [logging level](https://docs.python.org/3/library/logging.html#logging-levels) is set to INFO.

We log specific values while training using a `tf.compat.v1.train.LoggingTensorHook` object. The object is initialized with a dictionary mapping labels to scalar valued tensors. In our example, the labels we used were `'loss'` and `'step'`, for the loss and iteration count tensors, respectively. In the `run_model_training` function, `self.loss` represents the loss tensor and `self.global_step` represents the iteration count, also known as the training step.

To specify the logging frequency, we need to set exactly one of `every_n_iter` or `every_n_secs` as a keyword argument when initializing `tf.compat.v1.train.LoggingTensorHook`. In the example above, we set `every_n_iter` to 100, so that logging is shown every 100 iterations.

We can also use `every_n_secs` to specify a time interval for displaying logged values.

Learn about efficient and structured training in TensorFlow.

What you'll learn in this course

Data Pipeline

Model Execution

Training

Chapter Goals:

A. Logging values