Where are machine learning models stored?

When dealing with Machine Learning models, it is usually recommended that you store them somewhere. At the private sector, you oftentimes train them and store them before production, while in research and for future model tuning it is a good idea to store them locally.

How are ML models deployed?

The simplest way to deploy a machine learning model is to create a web service for prediction. In this example, we use the Flask web framework to wrap a simple random forest classifier built with scikit-learn. To create a machine learning web service, you need at least three steps.

How do you maintain a ML model?

Monitor Training and Serving Data for Contamination

1. Validate your incoming data.
2. Check for training-serving skew.
3. Minimize training-serving skew by training on served features.
4. Prune redundant features periodically.
5. Validate your model before deploying.
6. Shadow release your model.
7. Monitor your model health.

How do you store a trained model?

How to save trained model in Python?

1. Step 1 – Import the library. from sklearn import model_selection, datasets from sklearn.tree import DecisionTreeClassifier from sklearn.externals import joblib import pickle.
2. Step 2 – Setting up the Data.
3. Step 3 – Training and Saving the model.
4. Step 4 – Loading the saved model.

How do you store deep learning models?

Way to store large Deep Learning Models in production ready environments — using private document storage(Mongo-gridfs)

1. Using numpy dump(.npz)
2. Using pickle to do serialization.
3. Using Amazon/Azure/Google cloud services to store models.

Who decides to deploy AI?

Humans decide what data to collect in the first place, and what data to leave out. Humans decide how to categorize and label that data. Humans decide on the objectives of AI and the criteria on which to evaluate AI.

How often should an algorithm be updated?

In fact, Google is reported to change its search algorithm around 500 to 600 times each year. While most of these updates are small and often aren’t even picked up by users and SEO, every once in a while, Google releases major updates.

Why do models need ML?

ML model management is responsible for development, training, versioning and deployment of ML models. Note: Versioning also includes data, so we can track which dataset, or subset of the dataset, we used to train a particular version of the model. That’s why we do model management.