As a data scientist, training your machine learning model is only a part of providing a solution for the client. Besides generating and cleaning the data, selecting and tuning the algorithms, you also need to deliver and deploy your results so that it is usable in production. This is a large field in itself with constantly evolving tools and standards. In this post, my goal is to present a practical guide on how to do this using the currently available state of the art tools and best practices. We are going to build a system which can serve as a starting point for your deployment tasks, regardless of the actual machine learning problem itself! Instead of a minimal app barely scratching the surface of the used tools, I aim to introduce best practices and demonstrate advanced features, so that you don't have to learn the hard way. Learning from your own mistakes is nice, but thinking ahead and not committing those mistakes is much better. To create our deployment-ready application, we will use two tools as our main building blocks: Docker and FastAPI.

Dependency Injection is an in-depth guide to the current best practices for using the DI pattern-the key concept in Spring and the rapidly-growing Google Guice. It explores Dependency Injection, sometimes called Inversion of Control, in fine detail with numerous practical examples. Developers will learn to apply important techniques, focusing on their strengths and limitations, with a particular emphasis on pitfalls, corner-cases, and best practices. This book is written for developers and architects who want to understand dependency Injection and successfully leverage popular DI technologies such as Spring, Google Guice, PicoContainer, and many others. The book explores many small examples of anchor concepts and unfolds a larger example to show the big picture. Written primarily from a Java point-of-view, this book is appropriate for any developer with a working knowledge of object-oriented programming in Java, Ruby, or C#.

Intelligent personal assistants have the real potential to transform our daily lives in the nearest future. At least this is what Gartner says in its report on the Top 10 Strategic Technology Trends for 2017. For businesses, this means an excellent opportunity to refine their offers and improve customer experience, providing smarter and more effective ways to handle routine tasks. The great thing about Intelligent apps is that they can become integrated with almost every area of a customer's life. Over the last few years, more and more smart connected devices have been hitting the market, and all these gadgets are usually augmented with digital conversational interfaces.

Let's say it again: Python is a high-level programming language with dynamic typing and dynamic binding. I would describe it as a powerful, high-level dynamic language. Many developers are in love with Python because of its clear syntax, well structured modules and packages, and for its enormous flexibility and range of modern features. In Python, nothing obliges you to write classes and instantiate objects from them. If you don't need complex structures in your project, you can just write functions. Even better, you can write a flat script for executing some simple and quick task without structuring the code at all. At the same time Python is a 100 percent object-oriented language.