Renat Alimbekov's personal blog -Notes about Machine Learning, Data Science and Analytics Engineering

BentoML – Faster Machine Learning Prototype

In this post, I’ll show you how to create a working prototype of a web application with a working machine learning model in 50 lines of Python code. Imagine you have a cool project idea. Now you need to implement MVP (minimum viable product) and show it to your manager / partner / investor or just show off to your friends.

We will be using BentoML. It is a flexible, high-performance platform that is ideal for building an MVP.

BentoML features:

supports multiple machine learning frameworks including Tensorflow, PyTorch, Keras, XGBoost, and more.
own cloud deployment with Docker, Kubernetes, AWS, Azure and many more
high performance online service via API
web dashboards and APIs for managing model registry and deployment

Python logging HOWTO: logging vs loguru

In this post we will try to choose a library for logging in Python. Logs help to record and understand what went wrong in the work of your service. Informational messages are often written to the logs. For example: parameters, quality metrics and model training progress. An example of a piece of model training log:

An example of a piece of model training log

Machine Learning Models in production: Flask and REST API

A trained machine learning model alone will not add value for business. The model must be integrated into the company’s IT infrastructure. Let’s develope REST API microservice to classify Iris flowers. The dataset consists of the length and width of two types of Iris petals: sepal and petal. The target variable is Iris variety: 0 – Setosa, 1 – Versicolor, 2 – Virginica.

Saving and loading a model

Before moving on to develope API, we need to train and save the model. Take the RandomForestClassifier model. Now let’s save the model to a file and load it to make predictions. This can be done with pickle or joblib.

import pickle filename = 'model.pkl'
pickle.dump(clf, open(filename, 'wb'))

We’ll use pickle.load to load and validate the model.

loaded_model = pickle.load(open(filename, 'rb'))
result = loaded_model.score(X_test, y_test) 
print(result)

The code for training, saving and loading the model is available in the repository — link

How to make your CV attractive with a pet project

For junior Date Scientists, a CV consists of courses taken, education, and possibly not the most relevant work experience. Such resumes are not much different from the bulk of job seekers.

Working on a pet project is a great opportunity to improve skills. If you add the implemented pet-project to the CV, it will immediately become attractive and a topic for conversation at the interview will appear.

So what is a pet-project? Pet-project is a project that is done for yourself. It is created outside of work and is often self-interested. For example: sports, electronics, food preparation, auto, travel, medicine, etc. The project will help expand professional skills and learn new ones that will be useful in work.

Here are some ideas for projects in Data Science that you can get started with:

How To Learn a Data Science – my experience

Overview of online courses to enter the Data Scientist profession. All courses are completed and a subjective view and experience is described. Only English language courses are included in this note.