Have you ever gotten a notification from Netflix that started with ‘You might like’? Or have you noticed that Netflix ranks its content for you? Do you know that no two active Netflix accounts have the same movie recommendations?
If you have wondered why this is so, then we have got covered! The streaming giant Netflix relies on Python for better streaming and as for its recommendation system. Apart from this, we look at how Netflix manages the traffic of 148 million users in 190 countries efficiently and how Python is used through the ‘full content lifecycle.’
There are different aspects of Machine Learning involved, too, and we shall explore it in this article. It is going to be quite insightful, so let’s begin!
Learning of Blog
- Introduction to Netflix
- Open Connect
- Demand Engineering
- Information Security
- Personalization Algorithm
- Machine Learning
- Animation and VFX
This blog post would come as a bonus to any python training course and is based on a blogpost by Netflix developers itself.
Introduction to Netflix
Well, Netflix has made such a name for itself that it does not need any introduction, but here are some facts that might interest you further in its functioning. It is an American video-on-demand company headquartered in Los Gatos, California. It has emerged to be the biggest of its kind in the last two decades. In 2019, Netflix had an ‘eye-catching’ revenue of 20.5 billion dollars. Netflix released how it uses the popular programming language, Python, for its seamless working in the backdrop of the PyCon conference. It has been using cutting edge technologies, spending a whopping 1.3 billion dollars on technology and development in 2018. According to a senior software engineer at Netflix, Python is an intuitive language that has allowed its engineers to solve problems in the networking space. So now, let’s move on to look at how Netflix uses Python.
The most important part of the platform is Open Connect, which is the Content Delivery Network or CDN that Netflix uses, and it comes into the picture on pressing the ‘play’ button. CDN requires several infrastructural software systems, and these tools are primarily built on Python, and so are models and hardware components. CDN functions for video streaming begin after the recommendation system has done its work.
The demand engineering domain of Netflix brings flexibility, capacity operations, traffic distributions, fleet efficiency, and regional failovers to the cloud. It is also Python-based. Netflix uses Jupyter Notebooks along with ‘nteract’ for analyzing operational data and detecting regression tools using the prototype of visualization tools. It allows a Python expert at Netflix to manage tasks like logging, publishing, archiving, and cloning notebooks. The tools like NumPy and SciPy perform numerical analysis and Flask API as a wrapper around synchronization tools.
Here is a list of operations in which the Information Security team at Netflix is employing Python:
- Automation of security
- Classification of risk
- Identification of vulnerabilities
- Protection of SSH resources
- Permission tuning of Identify and Access Management
- Generation of TLS Certificates
- Detection of Sensitive Data
Netflix also provides a batch framework called Prism, which helps security engineers measure risk factors in the source code.
Netflix’s Core team is using Python for alerting and statistical analytical work. They have developed a time series correlated system and a distributed worker system that parallelizes a large amount of analytical work for quick results. This can be used both inside and outside the team. Python here is used to automate thousands of related signals at the rise of a problem. Apart from this, Python’s use in visualization, data exploration, and cleaning is varied. Also, Netflix can interact with other platform services by building Python libraries.
For a broad personalized Machine Learning Infrastructure, higher-level libraries of Python are used. The included processes are data accessing, model evaluation, feature extraction, fact-logging, and publishing. As a part of python programming certification, one comes across algorithms such as NumPy, Matplotlib, SciPy, pandas, and so on. On an advanced level, algorithms such as Keras, PyTorch, and TensorFlow help in deep neural networks and XGBoost in gradient boosting. All these are crucial for recommendation algorithms.
Machine Learning Infrastructure
Netflix applies machine learning to several use cases apart from personalization. Most of these applications use Metaflow- a Python framework for execution of the projects from prototype to the production stage. The framework is parallelized and optimized in Python to fetch data, handle data points, and computation.
Animation and NVFX
Netflix’s Animations and Visual Effects are based on Python as it is the standard for all the major applications. Along with Maya and Nuke integrations, Netflix also develops its shotgun tools in Python. The purpose of doing this is an extremely active development community and the availability of many third-party libraries for problem-solving.
As mentioned above, Netflix uses Jupyter Notebooks for a range of reasons. Python is the primary language when it comes to deployment of kernel specifications and different prototype interactions with Jupyter’s ecosystem. It is also deployed in balancing service. Notebooks are event-driven platforms that define conditions to filter events and even actions to react or route them. The outcome is segregated and visible microservices.
We have reached the end of this article, and by the discussion, it can be interpreted that Python is the driving force of Netflix. If you wish to be a part of an organization like Netflix or just driven to learn more about the language that is being used in nearly every edge of business, then go for a python certification and start using it for yourself.