Taking a Look at Binge Times

Reading this book, quite good so far, can it be used as a predictor of future times and Technologies?  Predictably just a 'black swan' event, or more?  More to follow.

Binge Times: Inside Hollywood's Furious Billion Dollar Battle to Take Down Netflix  By Dade Hayes and Dawn Chmielewski

Their intro: 

The first comprehensive account of the biggest wake-up call in the history of the entertainment business: the pivot to streaming. Go inside a disparate group of media and tech companies—Disney, Apple, AT&T/WarnerMedia, Comcast/NBCUniversal and well-funded startup Quibi—as they scramble to mount multi-billion-dollar challenges to Netflix.

After spotting Netflix and the deep-pocketed Amazon Prime Video a decade’s head start, rivals from the tech and start-up realm (Apple, Quibi) and traditional media (Disney, WarnerMedia, NBCUniversal) all decided to move mountains to enter the streaming game. At a cost of billions, each went after their own piece of the market, launching five new services in a seven-month span. And just as the derby was heating up, the coronavirus pandemic arrived, a black-swan event bringing short-term benefits but also stiff challenges.

The battle for streaming supremacy may end up having more than one winner, but the cost and disruption to decades-old business models have also produced a lot of losers. Binge Times reveals the true costs of the vision quest as companies are turned inside-out and repeatedly redraw their org charts and strategic plans. Stretching from Silicon Valley to Hollywood to Wall Street, it is a mesmerizing, character-rich tale of hubris and ambition, as the fate of a century-old industry hangs in the balance. ....  '

Supplemental enhancement PDF accompanies the audiobook.   .... 

Federated Graph Search from Netflix

Netflix has received some well deserved criticism of late.   But there is still Interesting Tech out of Netflix.    

How Netflix Content Engineering makes a federated graph searchable

By Alex Hutter, Falguni Jhaveri and Senthil Sayeebaba

Over the past few years Content Engineering at Netflix has been transitioning many of its services to use a federated GraphQL platform. GraphQL federation enables domain teams to independently build and operate their own Domain Graph Services (DGS) and, at the same time, connect their domain with other domains in a unified GraphQL schema exposed by a federated gateway.

As an example, let’s examine three core entities of the graph, each owned by separate engineering teams:

Movie: At Netflix, we make titles (shows, films, shorts etc.). For simplicity, let’s assume each title is a Movie object.

Production: Each Movie is associated with a Studio Production. A Production object tracks everything needed to make a Movie including shooting location, vendors, and more.

Talent: the people working on a Movie are the Talent, including actors, directors, and so on.  ....

Netflix Digital Experiments with Content Decisions

With lots of interesting links to presentations I am looking at.   How Netflix works with its content has always interested me.   Exploring this:

Netflix at MIT CODE 2020

Netflix Technology Blog

In November, Netflix was a proud sponsor of the 2020 Conference on Digital Experimentation (CODE), hosted by the MIT Initiative on the Digital Economy. As well as providing sponsorship, Netflix data scientists were active participants, with three contributions.

Eskil Forsell and colleagues presented a poster describing Success stories from a democratized experimentation platform. Over the last few years, we’ve been Reimagining Experimentation Analysis at Netflix with an open platform that supports contributions of metrics, methods and visualizations. 

This poster, reproduced below, highlights some of the success stories we are now seeing, as data scientists across Netflix partner with our platform team to broaden the suite of methodologies we can support at scale. Ultimately, these successes support confident decision making from our experiments, and help Netflix deliver more joy to our members!   ... ' 

Netflix is Shelf Selling with AI

 When I first saw the long rows of 'content' on a Netflix selection shelf.   I immediately thought of a supermarket shelf,  which I had much experience with.   This was not alphabetic or random.  And certainly not stationary.  Each time I went back things had changed.    But how?  They were selling to me with a purpose.  Ratings?   Increasing viewers.  With AI.   Part of the idea below; 

Netflix explains how it uses AI to sell you on a show

Machines may have a better sense of what will draw you in.  In Engadget By Jon Fingas, @jonfingas

If Netflix’s decisions on marketing its hundreds of original shows seem highly calculated... that’s probably because they were. Netflix has outlined how it uses AI to market shows and predict their success in ways that conventional box office numbers and Nielsen ratings likely couldn’t match. Effectively, it comes down to finding connections and determining the likely audience sizes.

The method relies on transfer learning, where the the parameters learned from a “source task” improve the performance of a “target task.” In this case, the source tasks are simple: what titles are comparable to a Netflix original, and what kind of viewership can the service expect?

For thematic comparisons, Netflix creates a “similarity map” where AI uses a show’s metadata, tags and summaries (“embeddings” in Netflix’s world) help determine links to other titles. Marketers would know which shows and movies to help describe a coming-of-age comedy, for example.

With audience sizes, the service has an AI model that compares the audience sizes of similar work in a given country. If a drama is likely to fare well in Spain, Netflix might not only ramp up marketing in the region but prepare dubs and subtitles earlier.

The systems are self-supervised, letting them access a much wider range of titles than they would if they were limited to Netflix’s own info.   ... "

How the Internet Spans the Globe

Part of the power of the internet was the fact that there were multiple ways to get from A to B,   but this article makes it seem more vulnerable.  The stats are interesting.   My old wrong impression was that it was mostly transmitted by satellite.   Also the largest user of the internet seems not to be business,  but streaming by Netflix.

How the Internet Spans the Globe
By Logan Kugler

Communications of the ACM, January 2020, Vol. 63 No. 1, Pages 14-16
10.1145/3371411

Undersea cables are responsible for moving data between countries and continents at high speeds, making everything from photo sharing to financial transactions possible. These cables use fiber optics to move data at high speeds to land, where the data is then conveyed via fiber optics to homes and businesses.

Yet, despite the billions of people relying on the data moved by undersea cables, there are only about 380 of them worldwide as of 2019, according to CNN estimates, though they span more than 745,000 miles—or more than three times the distance to the moon.

Given the sheer scope of undersea cables, which often span entire oceans, The New York Times estimates an individual undersea cable project can cost up to $350 million. That is why these cables have historically been laid by global telecommunications firms with the deep pockets and technical expertise necessary to undertake these projects. However, tech companies are increasingly dominating both the use and implementation of undersea cables.

"Some of the main investors in new cables now are Google and Facebook," says Alan Maudlin, research director at TeleGeography, a telecommunications research firm, because tech giants are increasingly the ones using all that undersea bandwidth. .... '

Breaking Things Productively

Have never used Chaos engineering, but the idea is interesting. Second read about the concept:

How to Use Chaos Engineering to Break Things Productively  by  Sam Bocetta in Infoq

More people connected to more servers, increased reliance on complex distributed networks, and a proliferation of apps in development mean more opportunities for data leaks and breaches.

Modern problems require modern solutions, as Amazon found out the hard way. Netflix escaped with minor inconvenience by being prepared.

What did they do differently?

Amazon Web Services (AWS), Amazon's cloud-based platform, experienced an outage on September 20, 2015, that crashed their servers for several hours and affected many vendors. Netflix experienced the issue as a blip because they've been there and done that when they changed their service delivery model. This led their engineering team to craft a unique solution for software production testing.

The solution? Chaos as a preventative for calamity. It's predicated on the idea of failure as the rule rather than the exception, and it led to the development of the first dedicated chaos engineering tools. Otherwise known as the Simian Army, they're called Chaos Monkey, Chaos Kong, and the newest member of the family, Chaos Automation Platform (ChAP).

What Are the Benefits of Chaos Engineering in DevOps?

Focusing only on a network environment and the associated security considerations (because the world of chaos engineering is quite large), we have already seen it as a positive force in an already strong cybersecurity market for improving business risk mitigation, fostering customer confidence, and reducing the workload for IT teams. If you're a business owner, you'll be blessed with happier engineers, reduced risk of revenue loss, and lower maintenance costs.

Customers, whether B2B or B2C, will enjoy greater service availability that's more reliable and less prone to disruptions. Tech teams will be able to reduce failure incidents and gain deeper insight into how their apps work. It will also lead to better design, faster mean time in response to SEVs, and fewer repeat incidences. ....  " 

Netflix: A Fast Growing Threat

Former colleague Bob Herbold talks about fast-growing early innovator Netflix:

Bob's Gutsy Leadership Blog
Netflix: Underestimating a Fast-Growing Threat?

Netflix has always been the innovator who was way out in front of the current market offerings. It was 1997, when only 7% of households had a DVD player, that Netflix launched its “movies on DVDs by mail” service. At that time, virtually all movies watched at home were rented on VHS tapes from one of the 3000 Blockbuster stores. By 2007, Blockbuster had grown to 9000 stores and was clearly on its way to bankruptcy since most people got their movies by mail on DVD’s from Netflix, since over 80% of homes had a DVD player, and renting a movie on a DVD from Netflix avoided the trips to Blockbuster.

It was also 2007 when Netflix once again got out front of the current offerings (its own DVD service) and launched its streaming service, but few people could take advantage of it, since less than 20% of homes had the adequate broadband capacity to stream a movie. Netflix knew that would change quickly and it did and Netflix grew to its current enormous size.

Today, the number of new challengers to the Netflix streaming service is exploding. Direct TV Now, Hulu, Sling and many others offer a variety of options. Amazon, YouTube, and Apple are growing their own streaming services, and with their deep pockets, will be significant threats. Media industry giants Disney and WarnerMedia are also building major streaming services, and both are hellbent on taking significant business from Netflix. Many of these firms are investing very heavily in new programming to compete very directly with Netflix. .... " 

How Python is used at Netflix

An instructive piece in the Netflix tech blog.   Netflix has to reliably delivery large amounts of data, personalized to millions of consumers, reliably and securely.    While gathering information to tailor their sales and marketing to these same consumers.  See how they use Python and development support architectures.  Considerable piece, mostly technical.

Python at Netflix
 Netflix Technology Blog   By Pythonistas at Netflix, coordinated by Amjith Ramanujam and edited by Ellen Livengood

As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members. We use and contribute to many open-source Python packages, some of which are mentioned below. If any of this interests you, check out the jobs site or find us at PyCon. We have donated a few Netflix Originals posters to the PyLadies Auction and look forward to seeing you all there.  ... " 

Turning TV Upside Down

By former colleague Bob Herbold.  On a major change of business model that changes marketing.  We have seen it happen, but what does it mean?

Bob's Gutsy Leadership Blog
Netflix: Turning TV Viewing Upside Down

Completely changing the business model in an industry is not an easy thing to do.  On the other hand, it creates incredible rewards for the innovators.  Netflix is a primary example of this.  It basically put the Blockbusters of the world out of business in the late 1990’s as it introduced DVD’s by mail versus going to a store and renting a VHS tape.  It refined that model in the late 1990’s moving to an “all-you-can-watch” monthly subscription model for its DVD mail service.  The next change occurred in 2007 when it began offering streaming.

In the last few years, it has turned TV viewing habits upside down.  Traditional broadcast channels have schedule programs once a week at a certain time period.  You had to wait until, for example, next Thursday evening at 9:00pm to watch the next episode. Netflix has ignored tradition and offers all episodes at one time; it is called “binge programming.”.... " 

Learning from Failure

Think also, the After Action Review (AAR):

How Coca-Cola, Netflix, and Amazon Learn from Failure  By Bill Taylor

Why, all of a sudden, are so many successful business leaders urging their companies and colleagues to make more mistakes and embrace more failures?

In May, right after he became CEO of Coca-Cola Co., James Quincey called upon rank-and-file managers to get beyond the fear of failure that had dogged the company since the “New Coke” fiasco of so many years ago. “If we’re not making mistakes,” he insisted, “we’re not trying hard enough.”

In June, even as his company was enjoying unparalleled success with its subscribers, Netflix CEO Reed Hastings worried that his fabulously valuable streaming service had too many hit shows and was canceling too few new shows. “Our hit ratio is too high right now,” he told a technology conference. “We have to take more risk…to try more crazy things…we should have a higher cancel rate overall.”

Even Amazon CEO Jeff Bezos, arguably the most successful entrepreneur in the world, makes the case as directly as he can that his company’s growth and innovation is built on its failures. “If you’re going to take bold bets, they’re going to be experiments,” he explained shortly after Amazon bought Whole Foods. “And if they’re experiments, you don’t know ahead of time if they’re going to work. Experiments are by their very nature prone to failure. But a few big successes compensate for dozens and dozens of things that didn’t work.”

The message from these CEOs is as easy to understand as it is hard for most of us to put into practice. I can’t tell you how many business leaders I meet, how many organizations I visit, that espouse the virtues of innovation and creativity. Yet so many of these same leaders and organizations live in fear of mistakes, missteps, and disappointments — which is why they have so little innovation and creativity. If you’re not prepared to fail, you’re not prepared to learn. And unless people and organizations manage to keep learning as fast as the world is changing, they’ll never keep growing and evolving. .... " 

Chaos Engineering in Practice

Don't remember ever hearing of Chaos Engineering, but had done some engineering in Chaotic, non predictable situations.  It should be noted that this is not about chaos theory, in a mathematical sense, but rather testing and adjusting complex systems.   Nora Jones in InfoQ describes it in practice.

Free 81 page Book on the topic via O'Reilly,

Where they describe it and its development and use by Netflix:

Building Confidence in System Behavior Through Experiments.

" .... With so many interacting components, the number of things that can go wrong in a distributed system is enormous. You’ll never be able to prevent all possible failure modes, but you can identify many of the weaknesses in your system before they’re triggered by these events. This report introduces you to Chaos Engineering, a method of experimenting on infrastructure that lets you expose weaknesses before they become a real problem.

Members of the Netflix team that developed Chaos Engineering explain how to apply these principles to your own system. By introducing controlled experiments, you’ll learn how emergent behavior from component interactions can cause your system to drift into an unsafe, chaotic state.  .... "

How might this be integrated with forms of process modeling.  like BPM?  Could the testing be applied to a process model?

Storytelling in Netflix

Still a very limited set of examples.  The idea of branching interactive storytelling has been tried for a long time without success.     I like trying it again, but don't have great expectations.

Netflix Storytelling
There’s no shortage of movies and shows for you to binge on Netflix, but sometimes, even in the midst of all these options, boredom pervades. After all, you can almost always predict the endings of entertainment these days, and sometimes, plots are so similar you feel like you’ve seen everything already. But now, Netflix may have a solution for you. It’s called Interactive Storytelling, and it’s Netflix’s way of letting you choose your own adventure.

On Tuesday, the streaming platform announced its first “interactive ‘branching’ narrative episodes Puss in Book: Trapped in an Epic Tale and Buddy Thunderstruck: The Maybe Pile, where Netflix members are in control of how the stories unfold.” Heralded as the melding of Netflix engineers’ technical prowess and Hollywood creatives’ imaginative direction, this new kind of content promises a “new world of storytelling possibilities.”

Noting that content creators often wish to tell nonlinear stories, Netflix hopes that its new Interactive Stories will allow creatives to “roam, try new things, and do their best work.” And of course, please a few viewers along the way, too.

“We’ve done extensive research and talked to lots of kids and parents, collecting qualitative data to better understand if this is something viewers will like,” Carla Engelbrecht Fisher, director of product innovation at Netflix, wrote in a blog post.    .... " 

Visualization Insight Tools for Netflix

In the Netflix Techblog, some good details.

Improving Netflix’s Operational Visibility with Real-Time Insight Tools
By Ranjit Mavinkurve, Justin Becker and Ben Christensen

For Netflix to be successful, we have to be vigilant in supporting the tens of millions of connected devices that are used by our 40+ million members throughout 40+ countries. These members consume more than one billion hours of content every month and account for nearly a third of the downstream Internet traffic in North America during peak hours.

From an operational perspective, our system environments at Netflix are large, complex, and highly distributed. And at our scale, humans cannot continuously monitor the status of all of our systems. To maintain high availability across such a complicated system, and to help us continuously improve the experience for our customers, it is critical for us to have exceptional tools coupled with intelligent analysis to proactively detect and communicate system faults and identify areas of improvement. 

In this post, we will talk about our plans to build a new set of insight tools and systems that create grea er visibility into our increasingly complicated and evolving world.  .... " 

Netflix, Analytics and What You Watch

In FutureStartup: Correspondent Bill Franks chief scientist at Teradata, talks about his analytics experiences with Netflix.    Further thoughts: " ... Teradata’s Franks said that if it wanted to, Netflix could almost be a stand-alone analytics firm.  ... “It is getting blurry out there as to what companies are in,” said Franks. “AT&T is not just a phone provider. It is providing TV, and then has its own data. Nike is now manufacturing high-tech electronics and housing data in its data center, not just doing knitted sportswear. Companies can commercialize the use of their data outside of their core business. It is a fascinating time, and a lot of companies will be morphing and twisting.” ... " 

Netflix and Data Rich Analytics

There is always opportunity for data rich environments.

How Data Analytics Is Shaping What You Watch
When Netflix decided to go into the entertainment-producing business by commissioning the streaming series “House of Cards,” there was a lot of data to be crunched before the first download ever took place, said Dave Hastings, Netflix’s director of product analytics, data science and engineering. ... " 

Netflix Technology

Interesting GigaOm piece on Netflix use of technology, especially the relationship between recommender technology and content.  I am a fairly recent user of Netflix.  The recommendations are fine, but despite all the advanced technology claims,  the connections seem simplistic.  More problematic is that content has become very fragmented among services, so it is rare that I can find exactly what I want.

3D Printer Startups Video

Makerbot and other low cost 3D Printer companies are examined in a Netflix video exclusive. Called 'Print the Legend'.  A familiar look at the atmosphere of startups, before and after they become successful.  If they do.  This may give you the impression that most startups are successful.  Not the case.

Amazon Bundles Books

Amazon is Bundling. It has been talked about and tested  for a while, and I received an offer yesterday.  To pay a fixed monthly price for full online access to any number of a group of 600K books.   It's called bundling, and similar to what Netflix does.  Bundling can add the value to the seller of removing statistical purchase variation created my consumer behavior.  Would like to see those analytics.  At first when you read it, it sounds like you get access to the whole library, but no, not all publishers are participating, and like Netflix, what you would really like or need is often not included.   And my interests are eclectic.  Not participating for now.

Netflix Using Distributed Neural Nets

In the enterprise we actively used artificial neural nets to perform various scoring functions for analytical learning methods.  They were particularly useful for anchoring other analytical methods in ensemble solutions.  Here an article about how Netflix is using nets to do deep learning.  Very intriguing, extensive and technical piece.  It ultimately boils down to how you aim to distribute the learning you aim to achieve.  Something to learn from.

Previously in this blog on neural nets. 

Simplify Outdated Corporate Processes

In the HBR, where registration for full text is now required.  Yes, simplify is always good to consider.   " ... rebuilding your processes from the ground up with a focus on simplicity is a powerful way to improve your competitiveness and energize your staff. Netflix, for example, eliminated all the administrative labor and financial expense that went into managing paid time-off. As described in Patty McCord’s recent HBR article, the company got rid of the standard policies and tracking system and instead simply relied on employees’ judgment: ...  "