Category Archives: Google

The New AI Economy

The convergence of technology leaps, social transformation, and genuine economic needs is catapulting AI (Artificial Intelligence) from its academic roots & decades of inertia to the forefront of business and industry. There has been a growing noise since last couple of years on how AI and its key subsets like Machine Learning and Deep Learning will affect all walks of life. Another phrase “Pervasive AI” is becoming part of our tech lexicon after the popularity of Amazon Echo and Google Home devices.

So what are the key factors pushing this renaissance of AI? We can quickly list them here:

  • Rise of Data Science from the basement to the boardroom of companies. Everyone saw the 3V’s of Big Data (volume, velocity, and variety). Data is called by many names – oxygen, the new oil, new gold, or the new currency.
  • Open source software such as Hadoop sparked this revolution in analytics using lots of unstructured data. The shift from retroactive to more predictive and prescriptive analytics is growing, for actionable business insights. Real-time BI is also taking a front seat.
  • Arrival of practical frameworks for handling big data revived AI (Machine Learning and Deep Learning) which fed happily on big data.
  • Existing CPU’s were not powerful for the fast processing needs of AI. Hence GPU (Graphical Processing Units) offered faster and more powerful chips. NVIDIA provided a positive force in this area. It’s ability to provide a full range of components (systems, servers, devices, software, and architecture) is making NVIDIA an essential player in the emerging AI economy. IBM’s neuromorphic computing project provides notable success in the area of perception, speech and image recognition.

Leading software vendors such as Google have numerous projects on AI ranging from speech and image recognition, language translation, and varieties of pattern matching. Facebook, Amazon, Uber, Netflix, and many others are racing to deploy AI into their products.

Paul Allen, co-founder of Microsoft is pumping $125M into his research lab Allen Institute of AI. The focus is to digitize common sense. Let me quote from today’s New York Times, “Today, machines can recognize nearby objects, identify spoken words, translate one language into another and mimic other human tasks with an accuracy that was not possible just a few years ago. These talents are readily apparent in the new wave of autonomous vehicles, warehouse robotics, smartphones and digital assistants. But these machines struggle with other basic tasks. Though Amazon’s Alexa does a good job of recognizing what you say, it cannot respond to anything more than basic commands and questions. When confronted with heavy traffic or unexpected situations, driverless cars just sit there”. Paul Allen added, “To make real progress in A.I., we have to overcome the big challenges in the area of common sense”.

Welcome to the new AI economy!

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Chaos Engineering

This phrase is new and it originated at Netflix back in 2010. I was listening to Nora Jones, a Netflix engineer at the AWS re-Invent conference few weeks back, where she talked about this. The principle of Chaos goes like this, “Chaos Engineering is the discipline of experimenting on a distributed system in order to build confidence in the system’s capability to withstand turbulent conditions in production.” Distributed systems have too many moving parts and failures can occur at various levels – hard disks can fail, the network can go down, a sudden surge in customer traffic can overload a functional component—the list goes on. All too often, these events trigger outages, poor performance, and other undesirable behaviors. Chaos Engineering is a method of experimentation on infrastructure that brings systemic weaknesses to light. This empirical process of verification leads to more resilient systems, and builds confidence in the operational behavior of those systems.

Netflix moved its operation to the cloud back in 2008. They started some form of resiliency testing since that time. They introduced Chaos Monkey that systematically turned off services in the production systems. Then came Chaos Kong for large scale failures like shutting off a whole data center. Another tool called FIT (Failure Injection Testing) was introduced to test all scenarios between the small (Chaos Monkey) and very large (Chaos Kong). All these experiments culminated into what is called Chaos Engineering, a discipline now used across many large companies such as Google, Amazon, Microsoft, etc.

Applying Chaos Engineering improves the resilience of a system. By designing and executing Chaos Engineering experiments, you will learn about weaknesses in your system that could potentially lead to outages that cause customer harm. You can then address those weaknesses proactively, going beyond the reactive processes that currently dominate most incident response models.

So what is the difference between Chaos Engineering (experimentation) and testing? In testing, an assertion is made: given specific conditions, a system will emit a specific output. Tests are typically binary, and determine whether a property is true or false. Strictly speaking, this does not generate new knowledge about the system, it just assigns valence to a known property of it. Experimentation generates new knowledge, and often suggests new avenues of exploration. Examples of input for chaos experiments could span from maxing out cpu cores on an Elasticserach cluster to partially deleting kafka topics over a variety of instances to recreate an issue that occured in production. Numerous experiments can be performed to understand system behavior ahead of time and take corrective actions.

At Google, Kripa Krishnan leads a team that constantly breaks the system. So a small team of testers from other big companies have started to work together to share best practices. These folks are currently working on ways to automate some of the tests. “Right now, scale is our problem. We are doing hundreds of tests, but I cannot scale my team to hundreds of people. So we are exploring automating some of this. How do you constantly cause damage so systems are constantly recovering?”

As distributed systems get more complex with thousands of microservices providing various functions, chaos engineering is emerging as a key practice to make these systems more resilient to failure.

Big Data & Analytics – what’s ahead?

Recently I read somewhere this statement – As we end 2017 and look ahead to 2018, topics that are top of mind for data professionals are the growing range of data management mandates, including the EU’s new General Data Protection Regulation that is directed at personal data and privacy, the growing role of artificial intelligence (AI) and machine learning in enterprise applications, the need for better security in light of the onslaught of hacking cases, and the ability to leverage the expanding Internet of Things.

Here are the key areas as we look ahead:

  • Business owners demand outcomes – not just a data lake to store all kinds of data in its native format and API’s.
  • Data Science must produce results – Play and Explore is not enough. Learn to ask the right questions. Visualization of analytics from search.
  • Everyone wants Real Time – Days and weeks too slow, need immediate actionable outcomes. Analytics & recommendations based on real time data.
  • Everyone wants AI (artificial intelligence) – Tell me what I don’t know.
  • Systems must be secure – no longer a mere platitude.
  • ML (machine learning) and IoT at massive scale – Thousands of ML models. Need model accuracy.
  • Blockchain – need to understand its full potential to business – since it’s not transformational, but a foundational technology shift.

In the area of big data, a combination of new and long-established technologies are being put to work. Hadoop and Spark are expanding their roles within organizations. NoSQL and NewSQL databases bring their own unique attributes to the enterprise, while in-memory capabilities (such as Redis) are increasingly being utilized to deliver insights to decision makers faster. And through it all, tried-and-true relational databases continue to support many of the most critical enterprise data environments.

Cloud becomes the de-facto deployment choice for both users and developers. Serverless technology with FaaS (Function as a Service) is getting rapid adoption amongst developers. According to IDC, enterprises are undergoing IT transformation as they rethink their business operations, including how they use information and what technology to deploy. In line with that transformation, nearly 80% of large organizations already have a hybrid cloud strategy in place. The modern application architecture, sometimes referred to as SMAC (social, mobile, analytics, cloud) is becoming standard everywhere.

The DBaaS (database as a service) is still not as widespread as other cloud services. Microsoft is arguably making the strongest explicit claim for a converged database system with its Azure Cosmo DB as DBaaS. Cosmo DB claims to support four data models – key-value, column-family, document, and graph. However, databases have been slower to migrate to the cloud than other elements of computing infrastructure mainly for security and performance reasons. But DBaaS adoption is poised to accelerate. Some of these cloud based DBaaS systems – Cosmo DB, Spanner from Google, and AWS DynamoDB – now offer significant advantages over their on-premise counterparts.

One thing for sure, big data and analytics will continue to be vibrant and exciting in 2018.

Meet the new richest man on earth

This morning Jeff Bezos beat his nemesis from the same town Bill Gates as the richest man on the planet with his worth exceeding $90B. This was due to a huge surge in Amazon’s stock price (over $128 rise) to $1100 plus today. Their 3Q results came out yesterday and Amazon grew its revenue by 34% and profits inched up as well. There were fears that heavy investments in new warehouses and hiring workers would push it to a loss. This year Amazon’s stock started at $750. What a run!

Here are the numbers. Revenue soared 34% to a record $43.74B, a first for a non-holiday period, as the internet retail giant spread its ambitions with the acquisition of Whole Foods Market Inc. and widened its lead in cloud computing. Profit increased 1.6% to $256M, despite the costs bulging by 35%, a five-year high. I was surprised to know that Amazon employs 541,900 people, an increase from last quarter’s 382,400. Roughly 87,000 employees were added from Whole Foods. Now Amazon commands some 43.5% of e-commerce sales this year, compared with 38.1% last year.

I remember during the dot.com crash, everyone wrote off Amazon. When they ridiculed Bezos for a no-profit company with a bleak future, he jokingly replied, ” I spell profit as ‘prophet'”. He has come a long way with his prophetic vision and masterful execution.

The best addition to Amazon’s two core businesses (books and e-commerce) was the introduction of AWS as the cloud computing infrastructure back in 2004. First came S3 (simple shared storage) when Bezos convinced start-up companies to rent storage at one-hundredth of the cost of buying from big vendors. Then EC2 (Elastic Computing Cloud) was added and that took off in a big way, especially with capital-starved startups with unpredictable computing needs. Pretty soon, Amazon took the credit of being the ‘father of cloud computing’ beating big incumbents like IBM, HP, etc. Now AWS is a huge business growing fast and bringing in about $16B revenue with over 60% profit. AWS is making a difference to the bottom line. Microsoft is trying hard to catch up with its Azure cloud and so is Google with its GCE (Google Computing Cloud). Today’s AWS is a very rich stack with its own database as a service (Redshift, Dynamo, and Aurora), elastic Map-Reduce, serverless offering with Lambda, and much more.There are predictions that AWS could one day be the biggest business for Amazon.

While the pacific north-west remains to be the home of the richest man on earth, the title shifts to Bezos from Gates.

Serverless, FaaS, AWS Lambda, etc..

If you are part of the cloud development community, you certainly know about “serverless computing”, almost a misnomer. Because it implies there are no servers which is untrue. However the servers are hidden from the developers. This model eliminates operational complexity and increases developer productivity.

We came from monolithic computing to client-server to services to microservices to serverless model. In other words, our systems have slowly “dissolved” from monolithic to function-by-function. Software is developed and deployed as individual functions – a first-class object and cloud runs it for you. These functions are triggered by events which follows certain rules. Functions are written in fixed set of languages, with a fixed set of programming model and cloud-specific syntax and semantics. Cloud-specific services can be invoked to perform complex tasks. So for cloud-native applications, it offers a new option. But the key question is what should you use it for and why.

Amazon’s AWS, as usual, spearheaded this in 2014 with a engine called AWS Lambda. It supports Node, Python, C# and Java. It uses AWS API triggers for many AWS services. IBM offers OpenWhisk as a serverless solution that supports Python, Java, Swift, Node, and Docker. IBM and third parties provide service triggers. The code engine is Apache OpenWhisk. Microsoft provides similar function in its Azure Cloud function. Google cloud function supports Node only and has lots of other limitations.

This model of computing is also called “event-driven” or FaaS (Function as a Service). There is no need to manage provisioning and utilization of resources, nor to worry about availability and fault-tolerance. It relieves the developer (or devops) from managing scale and operations. Therefore, the key marketing slogans are event-driven, continuous scaling, and pay by usage. This is a new form of abstraction that boils down to function as the granular unit.

At the micro-level, serverless seems pretty simple – just develop a procedure and deploy to the cloud. However, there are several implications. It imposes a lot of constraints on developers and brings load of new complexities plus cloud lock-in. You have to pick one of the cloud providers and stay there, not easy to switch. Areas to ponder are cost, complexity, testing, emergent structure, vendor dependence, etc.

Serverless has been getting a lot of attention in last couple of years. We will wait and see the lessons learnt as more developers start deploying it in real-world web applications.

Amazon+Whole Foods – How to read this?

Last Thursday (June 15, 2017), Amazon decided to acquire Whole Foods for a whopping $13.7B ($42 per share, a 27% premium to its closing price). On Friday, stock prices of Walmart, Target, and Costco took a hit downwards, while Amazon shares went up by more than 2%. So why did Amazon buy Whole Foods? Clearly Amazon sees groceries as an important long-term driver of growth in its retail segment. What is funny is that a web pioneer with no physical retail outlet decided to get back to the brick-and-mortar model. Amazon has also started physical bookstores at a few cities. We have come full circle.

Amazon grocery business has focussed on Amazon Fresh subscription service so far to deliver online food orders. Amazon will eventually use the stores to promote private-label products, integrate and grow its AI powered Echo speakers, boost prime membership and entice more customers into the fold. Hence this acquisition is the start of a long term strategy. Amazon is known for its non-linear thinking. Just see how it started a brand new business with AWS about 12 years back and now it is a $14B business with a 50%+ margin. It commands a powerful leadership position in the cloud computing business and competitors like Microsoft Azure or Google’s GCE are trying hard to catch up.

The interesting thing to ponder is how the top tech companies are spreading their tentacles. This was a front-page article in today’s WSJ. Apple, a computer company that became a phone company, is now working on self-driving cars, TV programming, and augmented reality. It is also pushing into payments territory challenging the banks. Google parent Alphabet built Android which now runs most PC devices. It ate the maps industry; it’s working on internet-beaming balloons, energy-harvesting kites, and self-driving technologies. Facebook is creating drones, VR hardware, original TV shows, and even telepathic brain computers. Of course Elon Musk brings his tech notions to any market he pleases – finance, autos, energy, and aerospace.

What is special about Amazon is that it is willing to work on everyday problems. According to the author of the WSJ article, this may be the smarter move in the long run. While Google and Facebook have yet to drive significant revenue outside their core, Amazon has managed to create business after business that is profitable, or at least not a drag on the bottom line. The article ends with cautionary note, “Imagine a future in which Amazon, which already employs north of 340,000 people worldwide, is America’s biggest employer. Imagine we are all spending money at what’s essentially the company store, and when we get home we’re streaming Amazon’s media….”

With few tech giants controlling so many businesses, are we comfortable to get all our goods and services from the members of an oligopoly?

Secret of Sundar Pichai’s success

I watched Sundar Pichai’s recent interaction with the students at I.I.T. (Indian Institute of Technology) Kharagpur, India, where he graduated back in 1993. Besides our common country of birth, I had never heard of Sundar until his rapid rise at Google a few years back. I have never met him or listened to him at conferences. So this was the first time, I had a chance to listen to his remarks and his answers to many questions from the audience of 3500 students at his alma mater earlier this week.

Growing up not far from I.I.T. Kharagpur, I was very aware of this institution. It was the first I.I.T. in India established during the 1950s. Other I.I.T’s like at Kanpur, Delhi, Mumbai and Chennai came later. These were the original 5 Indian Institute of Technologies. Lately many new ones have been added.

Sundar did his undergraduate studies in Metallurgy (study about metals). Then how did he switch from that into software? That was one of the questions from a student. He said that he loved Fortran language during his student days and that love for programming continued. The message he was giving was for everyone to pursue their own interest & passion. He mentioned that unlike in India, students at US universities sometimes do not decide their majors, way into their 3rd or 4th year of studies. Sundar’s passion was to build products that would impact a very large number of global users. During his interview at Google, he was asked what he thought of Gmail, which he had never seen nor used. Then the fourth interviewer actually showed it to him. Subsequently, he gave his opinion to the remaining 3 interviewers on what he thought was wrong with Gmail and how to improve it. He emphasized time and again the need to step out of the comfort zone and get an all rounded experience. Today’s students need not be afraid to take some risks and be willing to fail.

Besides technical leadership, Sundar possesses an amazing quality; egoless-ness, so rare to find in Silicon Valley executive community. He said that he truly believes in empowering his team and letting them execute with full trust. This is easier said that done, based on my experience at IBM and Oracle. Large organizations suffer from ego-driven leadership causing great amount of friction and anguish. Sunder’s rise at Google was due to his amazing ability to get teams to work very effectively. From Search, he went to manage Chrome, then he was given Android. His ability to work thru the complexities of products, fiefdoms, and internal rivalries was so evident that he was elevated to the CEO position so quickly. Humility is his hallmark combined with clarity of vision and efficient execution.

He made an interesting comment about the vision at Google. Larry Page said that the moonshot projects are worthwhile because the bar is so high (no competition). Even if you fail, you are still ahead with your knowledge and experience.

It was fun listening to Sundar’s simple and honest answers & remarks.