Benefits MNCs are getting from AI/ML: A case study of Netflix

Have you ever heard the term AI/ML?? These are the most trending words now a days. Everyone is talking about these terms and I think that their is no technical person in entire world who haven’t heard these terms. Every company from a small startup to big MNCs are working on these technologies to grow their business. From Google to Amazon, Facebook to Netflix, Microsoft, Apple develop their own powerful AI systems for different purposes.

Machine Learning models not only process the big data for these companies but also reduces their expenses and increasing their customers. Now a days these companies invest a lot in AI based researches to create new models which serve them better.

I researched a lot on how these companies are benefitted from ML/AI and how emphasizing the enhancement of AI provided to their products make them top notch companies to this generation. Here I am presenting a case study of one of the FAANG company which is one of the largest media service provider and biggest OTT platform in the world named as NETFLIX.

Netflix is an online service that lets you stream thousands of movies and TV shows over the internet. Netflix has apps for pretty much every device under the sun like iPhones, iPads, Apple TVs, cable boxes, refrigerators (probably) and more.

The main technology that make Netflix a top notch company of today’s generation is its AI based Recommendation system. Recommendation system of Netflix provide its members with personalized suggestions to reduce the amount of time and frustration to find something great content to watch. Netflix uses Data Science to cater relevant and interesting recommendations to you.

Now let me tell you first about Recommendation system and how it works using Machine Learning model.

A recommendation system is a platform that provides its users with multiple contents based on their preferences. A recommendation system store the information about the user as an input. This information can be in the form of the past usage of product or the ratings that were provided to the product. It then processes this information to predict how much the user would rate or prefer their product. A recommendation system makes various machine learning Algorithms. Another important role that a recommendation system plays today is to search for similarity between different products.

In the case of Netflix, the recommendation system searches for movies that are similar to the ones you have watched or have liked previously. This is an important method for scenarios that involve cold start. In cold start, the company does not have much of the user data available to generate recommendations. Therefore, based on the movies that are watched, Netflix provides recommendations of the films that share a degree of similarity.

Netflix makes the primary of use Hybrid Recommendation System for suggesting content to its users. Hybrid Recommendation system is combination of celebrative and content based filtering.

Now, the question is how Netflix created such a great Recommendation model and which algorithm it used to create this?

It was a time in 2006 when Netflix wanted to tap into the streaming market, it started off with a competition for movie rating prediction. It provided a prize of $ 1 million to whoever increased the accuracy of their then existing platform ‘Cinematch’ by 10%. At the end of competition, the BellKor team presented their solution that increased the accuracy of prediction by 10.06%. With over 200 work hours and an ensemble of 107 algorithms provided them with this result. Their final model gave an RMSE of 0.8712. For their solution, they made use of K-nearest neighbor algorithm for post-processing of the data.

Then they implemented a factorization model which is popularly known as Singular Value Decomposition (SVD) for providing an optimal dimensional embedding to its users. They also made use of Restricted Boltzmann Machines (RBM) for enhancing the capability of the collaborative filtering model. These two algorithms in the ensemble, SVD and RBM provided them with the best results. A linear combination of these two algorithms reduced the RMSE to 0.88.

However, even after reduction of RMSE and increase in accuracy, Netflix suffered from two major challenges — Firstly, the data that provided during the competition comprised of 100 million movie ratings, as opposed to more than 5 billion ratings that Netflix constituted of. Furthermore, the algorithms were static, meaning that they only dealt with historical data and did not take into account the dynamicity of users adding reviews in real-time. After Netflix overcame these challenges, it made the winning algorithms a part of its recommendation system.

Netflix also using Machine Learning models to improve its streaming services.

Movies and shows are often encoded at different video qualities to support different network and device capabilities. Adaptive streaming algorithms are responsible for adapting which video quality is streamed throughout playback based on the current network and device conditions.

Another area in which statistical models can improve the streaming experience is by predicting what a user will play in order to cache it on the device before the user hits play, enabling the video to start faster and/or at a higher quality. For example, they can exploit the fact that a user who has been watching a particular series is very likely to play the next unwatched episode. By combining various aspects of their viewing history together with recent user interactions and other contextual variables, one can formulate this as a supervised learning problem where they want to maximize the model’s likelihood of caching what the user actually ended up playing, while respecting constraints around resource usage coming from the cache size and available bandwidth. They have seen substantial reductions in the time spent waiting for video to start when employing predictive caching models.

Device anomaly detection

Netflix operates on over millions of devices, ranging from laptops to tablets to Smart TVs to mobile phones to streaming sticks. New devices are constantly entering into this ecosystem, and existing devices often undergo updates to their firmware or interact with changes on our Netflix application. These often go without a hitch but at this scale it is not uncommon to cause a problem for the user experience — e.g., the app will not start up properly, or playback will be inhibited or degraded in some way. In addition, there are gradual trends in device quality that can accumulate over time. For example, a chain of successive UI changes may slowly degrade performance on a particular device such that it was not immediately noticeable after any individual change. Detecting these changes is a challenging and manually intensive process. Alerting frameworks are a useful tool for surfacing potential issues but oftentimes it is tricky to determine the right criteria for labeling something as an actual problem.

By employing predictive modeling to prioritize device reliability issues, they’ve already seen large reductions in overall alert volume while maintaining an acceptably low false negative rate, which they expect to drive substantial efficiency gains for Netflix’s device reliability team.

Solving these problems is central to Netflix’s strategy as we stream video under increasingly diverse network and device conditions. Thus in this way ML/AI makes Netflix a top notch of this generation.

So, This is how Netflix uses Machine Learning model to improve its streaming quality and a great recommendation system which inspires viewers to spent more and more time on Netflix which make it so large.

Thank You 😊😊