Netflix, the OTT supremo, has come a very long way since its inception in 1997 in the capacity of a movie rental enterprise. Today, the company asserts its leading stature with a whopping market share of two hundred million users (globally). Very few among you know that the online video streaming magnate owes much of its success to the practice of the leverage of machine learning even before others could reckon its implication in the global business scenarios.
Netflix stands tall amid a plethora of OTT platforms and video content providers. According to resources, virtually fifty million global audiences tend to binge-watch sports, games, shows as well as movies through this OTT video streaming services podium. Netflix, along with the other video streaming contenders, chooses to amp up their gaming style with a razor-sharp focus on AI, and MACHINE LEARNING.
Machine Learning…. What on earth is that?
Technically, Machine Learning stands for an assemblage of data analysis methods. If you tend to look at the broader spectrum, ML happens to be a crucial part of the cosmos of Artificial Intelligence. Owing to the prowess of this new-age technology wave, commercial systems and enterprises can gain useful insights from the available data. With the help of this incredible technological advancement, it is now even easier to detect transactions, identify user patterns, carry out voice recognition functions, perform image processing, debug issues, execute personalized recommendations etc.
How does Netflix use ML to its advantage?
Netflix has indeed notched the game with artistic brilliance. Be it content creation, curation or UX, ML happens to be at the helm on this platform. Based on the quintessential ML technologies, Netflix is capable of wavering the optimal capacity of end-to-end video streaming solutions. Machine Learning helps Netflix improvise models, design more advanced prototypes, evaluate algorithms and implement experiments to spruce up the services.
Netflix seems to be quite meticulous in the way it chooses to implicate machine learning as well as AI data from the product perspectives. The OTT mogul is ready to do anything to ensure a high probability of the users clicking and watching their shows, games as well as movies. The entity chooses to proceed with relentless A/B tests to study and understand the user preferences. Whatever data is gathered, Netflix tends to use it in a very productive fashion.
The top-notch OTT platform would make sure that it uses the data to streamline the content delivery patterns. It will also focus on the selection of adaptive bitrate, audio encoding as well as video optimization processes.
Netflix thumbnails/Auto creation
Netflix simply nails it. The video streaming platform decks out enthralling graphics related to popular shows and actors to bolster the probability of clicks. The graphics happen to be automatically created by the ML process, initiated by Netflix based on viewer choices. With the aplt implementation of ML, Netflix has garnered a huge database or catalog pertaining to the shows or videos you would love to watch on this platform. Netflix chooses to unify the images, based on user preferences.
The Homepage
The home page of Netflix bears testimony to how efficiently it has brought ML into the game. The moment you bump into the OTT platform, you can get a good view of the home page. Generally, the page happens to be replete with shows which either you have watched or which pop up there as Netflix recommendations. Netflix makes the recommendation of movies, games and shows using the leverage of machine learning.
Netflix recommendation system
The recommendation algorithms, practised at Netflix, are quite strong. Based on this system, users can get personalized suggestions from the platform. Netflix collects the data based on your preferences and uses the same, later on, to help you find content which you will be interested in.
Streaming quality of the OTT platform
Based on user preferences as well as viewership information collected through AI and machine learning, Netflix continues to streamline the streaming video services. Along with the video quality aspects, the platform also uses the data to improve the loading time pertaining to the shows, games and movies. Moreover, it takes leverage of the user data to gauge as well as streamline the architecture of the famed OTT platform.
Conclusion
Based on whatever indication we could gather on the subject, it’s evident that Netflix is motivated enough to leverage the power of machine learning and AI data so that it can successfully work on offering undisturbed video streaming services to its user base all over the world. It is also determined to use the data and ancillary process in sorting out acute problems which the users have faced previously with Netflix. Moreover, it reduces the filtered data to consolidate the recommendation engines with the collaboration centric as well as content centric approaches. There is no denying that today Netflix enjoys an unbeatable status in content recommendation by the power of machine learning and AI.
