Deploy Python ML Apps

The tutorial covers various methods for deploying machine learning models, including using pickle files for Python object serialization, HDF5 for large model storage, and deploying models with Scikit-learn. It also discusses model predictive control using Gecko and the benefits of deploying models on cloud platforms like Google Cloud, Azure, and AWS for scalability.

Which of the following is NOT a method mentioned for sharing machine learning models?

• Using a physical USB drive

• Deploying on cloud services

• Creating a Docker container

• Exporting the model to another computer

What is a key advantage of using Docker containers for model deployment?

• They are cheaper than other methods

• They require no internet connection

• They allow for easy scaling and deployment

• They are faster than all other methods

What is a limitation of using pickle files for storing machine learning models?

• They are too large for most applications

• They cannot store certain machine learning models

• They are not compatible with Python

• They cannot store Python objects

Why might you choose to use an HDF5 file over a pickle file?

• HDF5 files are more secure

• HDF5 files are faster to write

• HDF5 files can store larger amounts of data

• HDF5 files are easier to read

What is the primary use of the Keras 'load_model' function?

• To delete a model

• To load a pre-trained model

• To save a model to disk

• To train a new model

In the context of the tutorial, what is the purpose of using a pre-trained TensorFlow model?

• To learn TensorFlow syntax

• To compare with other frameworks

• To test the speed of TensorFlow

• To avoid training a model from scratch

What is a benefit of using logistic regression models in Scikit-learn?

• They are faster to train

• They require less memory

• They can handle more data

• They are always more accurate

Why might K-nearest neighbors require more storage than logistic regression?

• It stores the entire training dataset

• It is less efficient

• It uses more complex algorithms

• It requires more computational power

What is a key feature of model predictive controllers in real-time applications?

• They are faster than other controllers

• They can predict future states

• They do not require any data

• They are easier to implement

Why is it important to store and reload models in real-time control applications?

• To save computational resources

• To ensure continuity after a reboot

• To improve prediction accuracy

• To reduce storage space