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Elly Garnand
How do I sign into netflix on a computer with my Xfinity account? I want to browse and view programs and mark things to view later, but I can't sign directly into Netflix. I found an older post, but the link it references no longer works and just goes back to the Forums Home Page.
That sort of worked, thanks. I didn't exactly find settings with my User Name & Password but I did find what email address was registered under Help. So I was then able to do a password reset with Netflix. using that email address.
if you subscribe through your Xfinity subscription, go to the x1 Netflix app, and go into settings and account. you will find a menu item where you can get a username/password to access on other devices from that menu.
I pulled this chapter together from dozens of sources that were at times somewhat contradictory. Facts on the ground change over time and depend who is telling the story and what audience they're addressing. I tried to create as coherent a narrative as I could. If there are any errors I'd be more than happy to fix them. Keep in mind this article is not a technical deep dive. It's a big picture type article. For example, I don't mention the word microservice even once :-)
Given our discussion in the What is Cloud Computing? chapter, you might expect Netflix to serve video using AWS. Press play in a Netflix application and video stored in S3 would be streamed from S3, over the internet, directly to your device.
Another relevant factoid is Netflix is subscription based. Members pay Netflix monthly and can cancel at any time. When you press play to chill on Netflix, it had better work. Unhappy members unsubscribe.
The client is the user interface on any device used to browse and play Netflix videos. It could be an app on your iPhone, a website on your desktop computer, or even an app on your Smart TV. Netflix controls each and every client for each and every device.
Everything that happens before you hit play happens in the backend, which runs in AWS. That includes things like preparing all new incoming video and handling requests from all apps, websites, TVs, and other devices.
In 2007 Netflix introduced their streaming video-on-demand service that allowed subscribers to stream television series and films via the Netflix website on personal computers, or the Netflix software on a variety of supported platforms, including smartphones and tablets, digital media players, video game consoles, and smart TVs.
Netflix succeeded. Netflix certainly executed well, but they were late to the game, and that helped them. By 2007 the internet was fast enough and cheap enough to support streaming video services. That was never the case before. The addition of fast, low-cost mobile bandwidth and the introduction of powerful mobile devices like smart phones and tablets, has made it easier and cheaper for anyone to stream video at any time from anywhere. Timing is everything.
Building out a datacenter is a lot of work. Ordering equipment takes a long time. Installing and getting all the equipment working takes a long time. And as soon they got everything working they would run out of capacity, and the whole process had to start over again.
The long lead times for equipment forced Netflix to adopt what is known as a vertical scaling strategy. Netflix made big programs that ran on big computers. This approach is called building a monolith. One program did everything.
What Netflix was good at was delivering video to their members. Netflix would rather concentrate on getting better at delivering video rather than getting better at building datacenters. Building datacenters was not a competitive advantage for Netflix, delivering video is.
It took more than eight years for Netflix to complete the process of moving from their own datacenters to AWS. During that period Netflix grew its number of streaming customers eightfold. Netflix now runs on several hundred thousand EC2 instances.
The advantage of having three regions is that any one region can fail, and the other regions will step in handle all the members in the failed region. When a region fails, Netflix calls this evacuating a region.
The header image is meant to intrigue you, to draw you into selecting a video. The idea is the more compelling the header image, the more likely you are to watch a video. And the more videos you watch, the less likely you are to unsubscribe from Netflix.
The first thing Netflix does is spend a lot of time validating the video. It looks for digital artifacts, color changes, or missing frames that may have been caused by previous transcoding attempts or data transmission problems.
A pipeline is simply a series of steps data is put through to make it ready for use, much like an assembly line in a factory. More than 70 different pieces of software have a hand in creating every video.
The idea behind a CDN is simple: put video as close as possible to users by spreading computers throughout the world. When a user wants to watch a video, find the nearest computer with the video on it and stream to the device from there.
In 2007, when Netflix debuted its new streaming service, it had 36 million members in 50 countries, watching more than a billion hours of video each month, streaming multiple terabits of content per second.
At the same time, Netflix was also devoting a lot of effort into all the AWS services we talked about earlier. Netflix calls the services in AWS its control plane. Control plane is a telecommunications term identifying the part of the system that controls everything else. In your body, your brain is the control plane; it controls everything else.
In 2011, Netflix realized at its scale it needed a dedicated CDN solution to maximize network efficiency. Video distribution is a core competency for Netflix and could be a huge competitive advantage.
The number of OCAs on a site depends on how reliable Netflix wants the site to be, the amount of Netflix traffic (bandwidth) that is delivered from that site, and the percentage of traffic a site allows to be streamed.
Within a location, a popular video like House of Cards is copied to many different OCAs. The more popular a video, the more servers it will be copied to. Why? If there was only one copy of a very popular video, streaming the video to members would overwhelm the server. As they say, many hands make light work.
Right now, up to 100% of Netflix content is being served from within ISP networks. This reduces costs by relieving internet congestion for ISPs. At the same time, Netflix members experience a high-quality viewing experience. And network performance improves for everyone.
What may not be immediately obvious is that the OCAs are independent of each other. OCAs act as self-sufficient video-serving archipelagos. Members streaming from one OCA are not affected when other OCAs fail.
Machine learning is behind chatbots and predictive text, language translation apps, the shows Netflix suggests to you, and how your social media feeds are presented. It powers autonomous vehicles and machines that can diagnose medical conditions based on images.
With the growing ubiquity of machine learning, everyone in business is likely to encounter it and will need some working knowledge about this field. A 2020 Deloitte survey found that 67% of companies are using machine learning, and 97% are using or planning to use it in the next year.
Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.
Some data is held out from the training data to be used as evaluation data, which tests how accurate the machine learning model is when it is shown new data. The result is a model that can be used in the future with different sets of data.
Successful machine learning algorithms can do different things, Malone wrote in a recent research brief about AI and the future of work that was co-authored by MIT professor and CSAIL director Daniela Rus and Robert Laubacher, the associate director of the MIT Center for Collective Intelligence.
Supervised machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurate over time. For example, an algorithm would be trained with pictures of dogs and other things, all labeled by humans, and the machine would learn ways to identify pictures of dogs on its own. Supervised machine learning is the most common type used today.
Reinforcement machine learning trains machines through trial and error to take the best action by establishing a reward system. Reinforcement learning can train models to play games or train autonomous vehicles to drive by telling the machine when it made the right decisions, which helps it learn over time what actions it should take.
Natural language processing is a field of machine learning in which machines learn to understand natural language as spoken and written by humans, instead of the data and numbers normally used to program computers. This allows machines to recognize language, understand it, and respond to it, as well as create new text and translate between languages. Natural language processing enables familiar technology like chatbots and digital assistants like Siri or Alexa.
Neural networks are a commonly used, specific class of machine learning algorithms. Artificial neural networks are modeled on the human brain, in which thousands or millions of processing nodes are interconnected and organized into layers.
In an artificial neural network, cells, or nodes, are connected, with each cell processing inputs and producing an output that is sent to other neurons. Labeled data moves through the nodes, or cells, with each cell performing a different function. In a neural network trained to identify whether a picture contains a cat or not, the different nodes would assess the information and arrive at an output that indicates whether a picture features a cat.
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