Designing good, maintainable classes is a challenge. Sometimes it even feels like there is a decision to make in every single line of code. This talk will help you master this challenge. It will explain … * … why small classes are beautiful;
* … why it is so important to encapsulate variation points;
* … why inheritance is rarely the answer for customization;
* … how to write good and maintainable constructors;
* … how to make sure class invariants are maintained;
* … how to handle member data;
* … how to write good member functions;
* … how to write good supporting functions;
* … why your private members are not private at all. PUBLICATION PERMISSIONS:
CppCon Organizer provided Coding Tech with the permission to republish CppCon tech talks. CREDITS:
CppCon YouTube channel: https://www.youtube.com/channel/UCMlGfpWw-RUdWX_JbLCukXg https://www.youtube.com/watch?v=_JMJY7j5bHA

This talk will give an introduction to Darts (https://github.com/unit8co/darts), an open-source library for time series processing and forecasting. Darts provides a wide variety of models and tools under a unified and user-friendly API. We will give a high level introduction to both time series forecasting and the main features of Darts. Description
Time series are everywhere in science and business, and the ability to forecast them accurately and efficiently can provide decisive advantages. Darts is an open-source Python library, which provides a wide variety of forecasting models and tools under a single and user-friendly API. It puts emphasis on reducing the experiment cycle duration and improving the ease of using, comparing and combining different models; from ARIMA to deep learning models. This talk will give a tour of Darts and some of its main features, such as: quick creation and comparison of forecasting models, backtesting, ML-based models applied to time series forecasting, training forecasting models on multiple time series, producing probabilistic forecasts and integrating external data. We will go over a few toy examples, and see how to address them in a few lines of code. Goals of the talk: Introduce how one can tackle forecasting problems
Obtain great results quickly in few line of codes
Pre-requisites: Basic knowledge of Python
Basic knowledge of data science & machine learning
Key take-aways: Quickly create forecasts with your own data
Compare and select the best models for your tasks
Potentially integrate additional data such as weather forecasts, GDP, … into your forecasts to improve them PUBLICATION PERMISSIONS:
PyData provided Coding Tech with the permission to republish PyData talks CREDITS:
PyData YouTube channel: https://www.youtube.com/c/PyDataTV/videos https://www.youtube.com/watch?v=mWZfHxQLp_4

There’s one specific role that builds a bridge between data science and its practical counterpart, machine learning. Meet the machine learning engineer. In this video we explain: 1) ML engineer’s responsibilities, including:
• Data preparation
• Selecting an algorithm • Model training
• Model deployment and integration with data sources
• Model performance monitoring and evaluation
• Retraining 2) Background and skillset of the ML engineer
3) When you should hire an ML engineer PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=GDCnydDWRnM https://www.youtube.com/watch?v=1xKzPBwBdn8