How Netflix is Upgrading Their Technology for Better Streaming Quality

In recent years, we have wondered why it’s essential for us to utilize machine learning to improve the quality of streaming. It must be recognized that machine learning and understanding the significance of AI have also emerged as vital because we are living in an era where we already have a solution that is searching for a problem. 

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In this article, we will be discussing how streaming has become quite complicated on Netflix and how AI and machine learning have made the challenges easier and the streaming experience smoother. It will help you determine how you stream Netflix movies with such ease as well. 

Let’s start this conversation with a fact. Netflix currently has over 117M subscribers worldwide and most of those living outside the USA. Now what that essentially means is that Netflix is getting most outfits profit and revenue from a market that is outside the USA. With such important news comes a problem: How do you provide a smooth streaming experience to every user outside the USA? How do you make sure there is no technical glitch? 

Before going further, it has to be said that the engineering effort that goes behind smooth streaming is incredible. There’s a need to install and maintain servers throughout and all across the world for the subscribers to gain access to content for streaming. Even the algorithms need to be exact. And with the interest or the audience expanding and exploding into diverse directions, their behavior is also indicative and varied making the one sock-fit all solutions suboptimal. 

And the situation is even more complex with smartphones or mobile devices because the behavior of these cellular networks is comparatively more volatile and unexpected. In addition, certain markets experience more congestion than others because of the clear distinction between the hardware devices; with that comes unique and varied potential and fidelities of internet connection.

Hence, the need to adapt and fit the fluctuation conditions of the servers to offer high quality and premium streaming service to the users. Netflix is known for observing not just the network and device conditions but also the prospects of new users and markets. This model has left an open position for machine learning and statistical modeling. 

Device Anomaly Detection

Netflix is known for functioning on a variety of devices, including laptops, smart TV and smartphones, and tablets. What this means is that new devices are always entering this so-called streaming ecosystem. In addition, we find many devices put Netflix on continuous updates that can lead to more problems like it would either not start properly or there will be a hindrance in playback. Gradually, the quality of the experience can deteriorate.

How is that? There could be a chain of UI changes that could degrade or make the experience turbulent just generally. 

But why are we discussing this? Well, it’s important because tracking these changes is crucial and difficult; especially if it’s done manually, that can easily become an intensive procedure. Here, it’s best to use a tool for surfacing the problems by alerting frameworks but again that’s a tricky space to be in. One trigger could possibly indicate false positives because of which there could be several manual investigations that can easily be avoided; it could redirect focus and lead to a miscalculation where the real problem can get lost. 

However, we have a history that confirms that the ultimate decision is made by humans as they get to decide if the issue is urgent or not important at all. This model can further be applied to predict the possibility of figuring out the measured conditions required to determine real challenges. Sometimes, despite our confidence, we are unable to determine the root cause of the problem.

So many possibilities: Is it just the fluctuation in device manufacturing? Is this problem faced by a local market or a specified subject or group? To determine this and not get lost in the train of problems, it’s best to use statistical modeling as it helps us pinpoint the root cause via a control on several covariates. 

You can use predictive modeling as well to give significance to the reliability issues of the devices. Usually, the parameter is false-negative and so with time, we can expect substantial efficiency gains for the team of reliability of Netflix. 

What we’re basically trying to put out is that these problems faced by Netflix and its streaming community can easily be solved through statistical modeling and machine learning methodologies. Such technical glitches are easily avoidable. And so, the use of machine learning and AI has become more crucial than ever before. 

In Conclusion

Coming back to the initial fact we shared, the total market of Netflix is over 117M and so it’s fairly challenging to boil down all problems to one. It’s high-dimensional and comes with varied variables as a cause to a minimal problem. Since Netflix caters to a market that is diverse in terms of users, server, location, and devices, it becomes incessantly important for them to bring machine learning and AI into the picture to resolve their duties and/or at least distribute. This has been their core strategy and will continue to be so.