Showing posts with label Artificial Intelligence. Show all posts
Showing posts with label Artificial Intelligence. Show all posts

Wednesday, 1 September 2021

Accelerate Data Lake on Cisco Data Intelligence Platform with NVIDIA and Cloudera

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The Big Data (Hadoop) ecosystem has evolved over the years from batch processing (Hadoop 1.0) to streaming and near real-time analytics (Hadoop 2.0) to Hadoop meets AI (Hadoop 3.0). These technical capabilities continue to evolve, delivering the data lake as a private cloud with separation of storage and compute. Future enhancements include support for a hybrid cloud (and multi-cloud) enablement.

Cloudera and NVIDIA Partnerships

Cloudera released the following two software platforms in the second half of 2020, which, together, enables the data lake as a private cloud:

◉ Cloudera Data Platform Private Cloud Base – Provides storage and supports traditional data lake environments; introduced Apache Ozone, the next generation filesystem for data lake

◉ Cloudera Data Platform Private Cloud Experiences – Allows experience- or persona-based processing of workloads (such as data analyst, data scientist, data engineer) for data stored in the CDP Private Cloud Base.

Today we are excited to announce that our collaboration with NVIDIA has gone to the next level with Cloudera, as the Cloudera Data Platform Private Cloud Base 7.1.6. will bring in full support of Apache Spark 3.0 with NVIDIA GPU on Cisco CDIP.

Cisco Data Intelligence Platform (CDIP)

Cisco Data Intelligence Platform (CDIP) is a thoughtfully designed private cloud for data lake requirements, supporting data-intensive workloads with the Cloudera Data Platform (CDP) Private Cloud Base and compute-rich (AI/ML) and compute-intensive workloads with the Cloudera Data Platform Private Cloud Experiences — all the while providing storage consolidation with Apache Ozone on the Cisco UCS infrastructure. And it is all fully managed through Cisco Intersight. Cisco Intersight simplifies hybrid cloud management, and, among other things, moves the management of servers from the network into the cloud.

CDIP as a private cloud is based on the new Cisco UCS M6 family of servers that support NVIDIA GPUs and 3rd Gen Intel Xeon Scalable family processors with PCIe Gen 4 capabilities. These servers include the following:

◉ Cisco UCS C240 M6 Server for Storage (Apache Ozone and HDFS) with CDP Private Cloud Base — extends the capabilities of the Cisco UCS rack server portfolio with 3rd Gen Intel Xeon Scalable Processors, supporting more than 43% more cores per socket and 33% more memory than the previous generation.

◉ Cisco UCS® X-Series for CDP Private Cloud Experiences — a modular system managed from the cloud (Cisco Intersight). Its adaptable, future-ready, modular design meets the needs of modern applications and improves operational efficiency, agility, and scale.

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CDIP is designed for hybrid clouds to help customers address the needs of modern apps and extensible data platforms. They can further accelerate their AI/ML and ETL workloads on their data lake with GA of Apache Spark 3.0 enabling GPU-accelerated workloads powered by NVIDIA RAPIDS data science libraries in the CDP Private Cloud Base 7.1.6.

The NVIDIA RAPIDS suite of open-source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. RAPIDS uses NVIDIA CUDA and exposes GPU parallelism to accelerate ETL and machine-learning workloads. NVIDIA RAPIDS Accelerator for Apache Spark leverages GPUs to accelerate data processing in Apache Spark 3.0 using the RAPIDS libraries. This allows users to run existing Apache Applications ten times faster with no code changes.

On the AI/ML side, NVIDIA GPUs integrates with libraries like TensorFlow and PyTorch to accelerate the training of Neural Networks for various use cases, such as Computer Vision and Natural Language processing, on a single GPU node or on multiple nodes, reducing the training time from weeks to days (or hours). This saves our customers valuable time.

The Cisco, NVIDIA, and Cloudera three-way partnership brings our joint customers a much richer data lake experience through solution technology advancements, validated designs, and it all comes with full product support.

Source: cisco.com

Friday, 30 July 2021

Full Stack Observability Driving Customer Experience in a Multi-Cloud Environment

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Application is the Business & Level of Digitalization is the Brand

In our ever-changing world, where the application represents the business itself and the level of digitization it provides is directly related to the perception of the brand; enterprises must ensure they stand differentiated by providing exceptional user experience – both for their customers as well as their employees alike. When the pandemic hit us, expectations by customers and employees initially were driven by empathy, with disruptions to services expected – but 18 months on, today everyone expects the same level of service they got pre-pandemic, irrespective of where people are working from. This drives a higher-level of expectation on the infrastructure and teams alike – towards providing an exceptional digital experience.

It is evident that application services are becoming increasingly distributed and reimagining applications through customer priorities is a key differentiator going ahead. A recent study on Global Cloud adoption by Frost & Sullivan has indicated a 70% jump in multi-cloud adoption in the Financial Services space. This is driven by a renewed focus towards innovation, along with the digitalization and streamlining of the businesses. On average, financial firms have placed more than half of their workloads in the cloud (public or private hosted) and that number is expected to grow faster than other industries over the next five years.

Digital Experience Visibility

In today’s world of applications moving to edge, applications moving to the cloud, and data everywhere – we really need to be able to manage IT irrespective of where we work, as well as where the applications are hosted or consumed from. It’s relatively easy to write up code for a new application; however, the complexity we are solving for in the current real-world scenario is that of deploying that code in today’s heterogenous environment, like that of a bank. Our traditional networks that we currently use to deploy into the data centers, predates cloud, predates SASE, Colo’s, IoT, 5G and certainly predates COVID and working from home.

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In today’s world cloud is the new data center and internet is the new WAN – thereby removing the concept of an enterprise perimeter and making identity the new perimeter. To provide that seamless experience, IT needs to not just monitor application performance, but also enable application resource monitoring and application dependency monitoring – holistically. This should enable the organization to figure out the business impact of an issue – be that a drop in conversion rate or a degradation in a service, and decide almost proactively if not predictively the kind of resources to allocate towards fixing that problem and curbing the business impact.

Observability rather than Visibility


In today’s world operations are complex with various teams relying on different tools, trying to trouble shoot and support their respective domains. This visibility across individual silos still leaves the organization miles away; left to collate the information and insights via war rooms, only then being able to identify the root cause of a problem. What is required is the ability to trouble shoot more holistically – via a data driven operating model.

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Thus, it is important to use the network as a Central Nervous System and utilize Full Stack Observability to be able to look at visibility and telemetry from every networking domain, every cloud, the application, the code, and everything in between. Then use AI/ML to consume the various data elements in real time, figure out dynamically how to troubleshoot and get to the root cause of a problem faster and more accurately.

A FSO platform’s end goal is to have the single pane of glass, that would be able to:

◉ Ingest anything: any telemetry, from any 3rd party, from any domain, into a learning engine which has a flexible meta data model, so that it knows what kind of data it’s ingesting

◉ Visualize anything: end to end in a unified connected data format

◉ Query anything: providing cross domain analytics connecting the dots, providing closed loop analytics to faster pinpointed root cause analysis – before it impacts the user experience, which is critical

AI to tackle Experience Degradation


AI within an FSO platform is used not just to identify the dependencies across the various stacks of an application, but also to correlate the data, address issues, and right size the resources as they relate to performance and costs across the full life cycle of the application.

It is all about utilizing the Visibility Insights Architecture across a hybrid environment that enables balancing of performance and costs through real time analytics powered by AI. The outcome to solve for is Experience Degradation which cannot be solved individually in each of the domains (application, network, security, infrastructure) but by intelligently taking a holistic approach, with the ability to drill down as required.

Cisco is ideally positioned to provide this FSO platform with AppDynamics™ and Secure App at the core, combined with ThousandEyes™ and Intersight™ Workload Optimizer, providing a true end to end view of analyzing and in turn curbing the Business Impact of any issue in real time. This enables the Infrastructure Operators and the Application Operators of the enterprise, to work closely together, breaking the silos and enable this closed loop operating model that is paramount in today’s heterogenous environment.

Download the report: Agents of Transformation: The Rise of Full Stack Observability, to learn more about Business Observability and the challenges technologists are facing.

Source: cisco.com

Tuesday, 20 October 2020

Collaboration in the Age of AI: How Cisco is Pioneering the Use of AI and Emerging Technology Within Collaboration

Artificial intelligence (AI) has become all the rage. Just the mere mention of it makes us think of hi-tech and some futuristic state that promises simplicity and instant knowledge. According to research from O’Reilly, engagement with artificial intelligence technology grew 58% last year.* Additionally, the global artificial intelligence market is expected to grow nearly 50% in 2020, to a staggering $40.74 billion.** For the collaboration industry, their use brings hope of frictionless and instant connections.

Webex’s Rich History in AI Innovation

Cisco Webex has pioneered the use of AI within collaboration to bring this hope into reality. There is no other vendor in this space with a rich history in AI innovation like Webex.  All for the purpose of helping people to connect like never before from wherever they work, play, or learn. To enable a safe return to the office. And to make decisions about collaboration spaces and office floor plans. From your home to the board room, our, AI, and automation technology provide intelligent experiences and drive changes, that keep everyone safe and productive.

And it isn’t something that will happen in the future. It’s happening today; in fact, we’ve been doing it for years following our strategy to apply AI and Machine Learning (ML) to practical applications in collaboration. We identified the most important areas where machine learning would make a difference in collaboration solutions and have focused our efforts on relationship intelligence, audio & speech technologies, bots & assistant, and computer vision.

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This involved the application of a range of AI-based technologies including:

◉ Conversational AI, a combination of natural language processing, dialog management, and question answering
◉ Wakeword speech technology
◉ Speech To Text (STT) and Text To Speech (TTS)
◉ Speech Transcription and Translation
◉ Noise detection and removal
◉ Face Recognition
◉ People Insights

To support the advanced machine learning techniques used in these technologies several of them were optimized to run on NVIDIA GPU’s. Additionally, these features were deployed in the cloud or directly on client devices in order to provide optimal processing and the best data privacy position for end users.

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2017


After several years of research and development, this was the year Webex first introduced AI that could change the way we meet and interact. For example, two big challenges we noticed back then were how we could reduce distracting noises (sirens, doorbells, dog barking, etc.), and how to present the best view of the conference room for remote participants. As a result, we introduced the following:

◉ Machine learning-based noise detection – Webex used AI to recognize these loud and annoying noises in the background. Once detected, the system prompted you to mute your microphone or suppressed common noises such as typing on a keyboard or rustling papers.

◉ Best overview and Speaker Track camera framing of participants – Video systems in the past were able to detect and zoom in on different speakers using multiple moving cameras. Modern Webex Rooms added intelligence to do this digitally with fixed cameras. They automatically framed up attendees as they talk to provide closeups of where the conversation is happening. This dramatically improved the experience of remote participants.

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2018


The rise of small, agile teams meant that collaboration wasn’t just happening in traditional conference rooms. Every shared space was effectively turning into a huddle room. Whatever space people met in, they wanted the same easy join/start/share experience.  So, our engineers not only made the conference room look even better to remote participants, but also improved the experience of people in those rooms. Webex Rooms systems have a modern hardware architecture that includes NVIDIA GPUs. This architecture allowed us to build sophisticated computer vision applications and bring AI-driven features to market faster. These included:

◉ Face detection and people count – Webex Room devices used computer vision and a collection of sensors to determine how many people are in the room, unlocking powerful room utilization insights for customers

◉ Presenter tracking – By detecting people and faces, Webex Room cameras could automatically follow the active speaker if they paced or moved about the room, so they always stayed in frame

◉ Conversational AI on devices (Webex assistant) – Webex brought to market the first voice-activated assistant to help you call someone, start meetings or share your screen without touching anything

◉ Automated pair and share: The Webex app connected to your Webex video devices wirelessly through ultrasound, and that’s when the magic happened. The proximity sensors in Webex Rooms could detect when you walked into a room, and the prompt on the screen would welcome you by name. And your Webex app could automatically pair to that device in order to share content without ever touching cables or fiddling with remote controls or cables!

2019


As the remote collaboration experience became better, what people wanted next was building a better, more intimate connection to the people they were meeting with on the screen. How could we shave off the 10 minutes of going around the room for introductions, and yet help you feel you already knew everyone you were meeting with?

◉ Relationship intelligence (People Insights) – Webex brought to market People Insights to provide users with comprehensive, real-time business and professional profiles of meeting participants, giving users context and increased insight about the people they meet with…either before the meeting or during the meeting.

◉ Facial recognition with name labels – To go along with facial detection, we launched facial recognition. Adhering to strict data privacy rules, those who opted in for this feature were able to have the camera system recognize their face and then display their name label under their face to all remote participants.

◉ Proactive collaboration assistant – With advancements in natural language abilities, Webex Assistant became even smarter. Previously, it was able to respond when spoken to and carry out actions. But now it could also proactively start a conversation. For example, when it was time for a meeting, Webex Assistant would wake up and ask the user if they want to join.

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2020


This was/is the year where work from home become mainstream, and #RemoteWork started trending on social streams. Working from home presents some unique challenges and it became clear that new innovations were needed to make it easier to work from anywhere, without distraction. This has been a watershed moment for needing AI in collaboration, as more people dealt with all sorts of background and noise distractions.  How did Cisco Webex respond? With intelligent technology for the hybrid workplace:

◉ Background blur AND virtual replacement options across any device or OS – While other vendors offered this, Webex was the first to offer both blurring and virtual background across any device or operating system

◉ Mask-friendly People Counting: Webex Room devices are able to detect & count people regardless of which way they are facing, even if they are wearing masks! This information can now also be used for social distancing alerts based on room capacity.

◉ Noise Removal WITH Speech enhancement – Solving for background noise has become table stakes. With the recent acquisition of BabbleLabs, Webex has taken the technology to reduce meeting interruptions to the next level. This noise removal technology, powered by AI, goes beyond noise suppression by 1) distinguishing speech from background noise, 2) removing background noise in real-time, and 3) enhancing your voice to elevate communication, independent of language.

◉ A personal in-meeting assistant (expanding Webex Assistant in Meetings) – Now you have a personal collaboration assistant in every meeting! The Webex voice assistant expands beyond Room devices, to any Webex meeting, and uses advanced speech recognition and natural language understanding to turn talk into action.

◉ Real-time closed captioning – See what is being said, even if you are in a place that makes it hard to hear what is being said.

◉ Capture action items and highlights – Users can simply tell Webex to highlight certain points in a meeting or to create action items.

◉ Searchable and editable meeting transcript – After the meeting see the transcript, edit it, search within it, and easily share it. It automatically captured for you.

◉ Speaker labeling in transcripts– names are shown on notes, highlights, and transcripts to let you know who said what.

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When working from home, many people are faced with spotty Wi-Fi or bandwidth constrained home networks that just aren’t as robust as corporate networks. We improved Webex performance in such conditions by applying machine intelligence in a few core areas:

◉ Video Super-resolution – When there isn’t enough bandwidth to deliver HD video, Webex intelligently applies adaptive super-resolution. We’re able to deliver HD-like quality even when receiving 360p or lower resolution video.

◉ Region of interest encoding – Webex can intelligently identify the most important regions in a video frame, like a person’s face. When bandwidth is limited, Webex can still deliver high-quality video by making sure that the important parts of the frame look better, whereas other parts like backgrounds might be slightly lower quality.

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◉ AV1 Next-Gen Video Compression – AV1 is a new, next-gen video codec with an extensive toolset that delivers state-of-the-art compression performance. Last summer, in an industry-first demonstration we not only showed live encoding of 720p30 camera video at half the bandwidth of H.264 but also high frame rate share encoded at 1080p30 using around 2/3 of the bitrate of H.264 encoding 720p30, all on a commodity laptop. We’ve been making steady progress on this technology and soon you will see us implement it in Webex meetings, further reducing the amount of bandwidth required for a high-quality experience. 

What About my Data Privacy?


Webex brings powerful artificial intelligence and machine learning to your collaboration experience, at home or at the office, to help to foster relationships, enhance customer interactions, and build high-performance teams across boundaries. But what about data privacy? How are my data privacy rights being protected?

Our AI/ML initiatives are guided by a few core principles:

◉ Don’t retain data if you don’t have to
◉ If you do, keep it for the shortest possible time
◉ Be transparent about data usage
◉ Provide edit and deletion controls
◉ Empower end-users and admins

Sunday, 31 May 2020

Building character towards future success

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Why does someone become a fashion designer? Or a scientist, an investor, or entrepreneur – or all of the above?

I don’t ever recall thinking I wanted to “be” this or that. I just wanted to do something. And then I looked up and discovered I had become something.

It is your skills and character that makes you who you are. You get to decide on which skills you have. But your character … how does that happen? Three character traits have helped me enormously in my life. I’ll share the secret to how you might be able to amplify these in yourself.

The first trait: Curiosity


My own curiosity meant I was taking risks. I hit bumps in the road, but also had great adventures. College was a huge transition for me; it allowed me to escape my old life. I went to my state’s college, the University of Maryland. It was like a sandbox to me. I was eager to try new things – which in the 1970’s was sometimes dangerous.

As a freshman, I took a job washing dishes in an algal biology lab. It wasn’t very interesting. But the lab tech next door, an older man, was using a crazy-sounding instrument called the Scanning Electron Microscope, or SEM, in the Engineering Dept. After a bit of my pestering, he took me to the SEM lab and let me watch as he looked at specimens. The microscope shot electrons onto the gold coated surface of the specimen, which allowed us to see the specimen in 3D, at a microscopic level.

Soon, I got to know the person who ran the microscope lab in that same Engineering Dept. In passing, he mentioned an upcoming three-day meeting in Chicago, with international scientists gathering to talk about Scanning Electron Microscopy!

I had to go. This was my calling. It was my curiosity talking to me.

I drove for two days, from Maryland to Chicago, and slept on the floor of a friend of a friend’s apartment to attend this meeting. I met scientists from Oxford, Cambridge, and the famed IBM Research Labs, who were involved in groundbreaking work in microscopy. I made friends with all of them, talking about the lectures, their research, and joining the group for meals.

The team from Oxford labs invited me to join them as an intern that summer, where I could work on one of three existing million-volt microscopes and help them build the first Scanning Transmission Electron Microscope, now known as a STEM instrument. I was so curious and fascinated by electron microscopy that I took all the available classes on campus in this area.

And so, by listening to my curiosity, I got the priceless experience of working with top scientists, learning about a groundbreaking new technology, and participating in its development, at one of the most respected universities in the world.

Now, my second life trait: Perseverance


Some years ago, I received a letter from a US Presidential Science Advisor thanking me for a job well done as a consultant. He said, the one thing I should be sure to pass on to my children is my perseverance. He thought it was a rare trait.

Later, as I thought back on it, my first memory with perseverance was in my first job after college. Soon after graduation, I took a job at Johnson & Johnson, establishing an electron microscope lab in one of their subsidiaries. Up to that point, my professional experiences was in government and academic environments.

Moving to a for-profit organization was confusing for me, and I didn’t feel that I fit-in. But then, I saw a notice, on the bulletin board in the lunchroom to participate in a graduate class “on the pharmaceutical industry.”

I took the GRE – which means I took the risk of applying, which led me to a degree. This laid the groundwork for what was to follow. Johnson & Johnson had a tuition reimbursement program for that MBA, but it did not extend to my job level. I was told to wait until I was in a higher position to take the program, when I could use the degree. I fought that suggestion. I talked with the head of HR to lobby for a change in policy. I ended up getting all of my tuition reimbursed. In my six years at Johnson & Johnson, I continued to persevere, and went from Research & Development, to Regulatory Affairs, and then on to finance where I did sales forecasting.

Finally, my third life trait: Innovation


My innate curiosity and asking “what might appear to be dumb” questions to understand my environment would soon open up more opportunities for me.

While at a conference for another employer, I overheard a group of people in their late twenties chatting ahead of me in the registration line. They worked on Wall Street, and I was fascinated by their conversation on industries and markets. Soon, I became close friends with that group and learned a lot from them.

After that chance encounter, I was inspired and started to look for a job on Wall Street. I quickly realized that my varied experiences were an asset in this new fiscal world.

I had no idea what investment banking was, so I met with anyone who would talk with me. As luck would have it, I ran into the Chairman of the Board from my company at a Christmas party. It was awkward to answer the question, “Where do you work?” But taking hold of my courage, I told him I worked for him and was looking for a job.

Unknown to me, over the next few days, he made some calls. Soon I had an interview at a venture capital firm. At first, I was confused because I didn’t know what venture capital was. I decided to take a leap of faith and, after many interviews, got that job.

After four years of working in venture capital, I decided to move to the other side of the table, as I realized that I wanted to be on the creative side of the equation. Over the years, I’ve founded seven companies, all of them with great teams of people. Some succeeded, and some didn’t succeed.

In the middle of that, I went back to school to earn my MFA in fashion design and spent five years running a fashion label. I didn’t realize that, while it is hard for a consumer to shop e-commerce stores and find the right clothes that will look good on them, no-one teaches designers how to fix that problem. I spent several more years experimenting with my label and eventually changed direction again entirely.

You see, prior to earning my MFA, I had owned two successful predictive modeling start-ups, and I saw similarities with the kind of problems we were solving in my eCommerce fashion company and the issues we faced in those two companies. So four-and-a-half years ago, I started up Savitude, an AI technology company, to solve the “fit” and “flatter” problem we had in our eCommerce label.

Reflecting on traits for success


Now in a reflective time of my life, I have wondered, “what has kept me going in this direction?” What keeps me doing this now? The nature of my reflection has changed, and I started to see new patterns. Not long ago, I connected my attraction to early stage startups, inventions, and inventors to my early life. I started to think I could alter my own perception of my surroundings. How and when this started, I don’t know…but I do know why.

To survive my abusive childhood, I created alternate narratives. I searched for the rules on how it could be. I wanted a copy of the “rule book” that I thought everyone was born with.

Eventually, I realized I had to write my own rule book.

If you resonate with even a small part of my story, you too can transfer the energy you spend on questions like “why didn’t I do?” into creative exploration and innovative contemplation.

I am most grateful for the perseverance this exercise has given me. And, perhaps some of you have also found the inner strength to endure difficult times. You can repurpose that strength to navigate a rewarding path.

The traits of curiosity, perseverance, and innovation have helped me enormously. I have had experiences that have made me feel small, and those memories are enduring. But in those times, I have imagined brightness in the dark, interest where was none, and the will to wake up every day, which in turn has given me the power to invent, to patent, to hire, to sell, to deposit money.

Saturday, 28 March 2020

Cisco Announces Kubeflow Starter Pack

Recently the Kubeflow Community released Kubeflow 1.0. Kubeflow brings together features such as TensorFlow, PyTorch, and other machine learning capabilities into a cohesive tool – from data ingestion to inferencing. Cisco is one of the top contributors to Kubeflow, helping to make operationalizing machine learning for large scale deployments easier for everyone. As a result, we are announcing Cisco Kubeflow Starter Pack.

Here are are the major components of Kubeflow 1.0:

Jupyter Notebook


Many data science teams live on Jupyter notebook since it allows them to collaborate and share their projects, with multi-tenant support. Personally, I use it to develop Python code because I like its ability to single step my code, with immediate results. Within the data science context, Jupyter becomes the primary user interface for data scientists, machine learning engineers.

TensorFlow and Other Deep Learning Frameworks


Originally designed to only support TensorFlow, Kubeflow version 1.0 now supports other deep learning frameworks, including PyTorch. These are two of the leading deep learning frameworks that customers are asking about today.

Model Serving


Once a machine learning model is created, the data science team often must create an application or web page to feed new data and execute the trained model.  With Kubeflow, there are built-in capabilities with TFServing enabling models to be used without worrying about the detailed logistics of a custom application.  As you can see in the screen shot below, the data pipeline enables data model to be served.  In fact, the model can be called through a URL.

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Kubeflow Data Pipeline. Note the Deploy Stage for Trained Model Serving

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Kubeflow Model Serving. Note the “Service endpoint” URL where the trained model can be accessed

Other Components


There are many other components to Kubeflow, including integration with other open source projects that enable more advanced model inferencing, such as Seldon Core. The Kubeflow Pipelines platform, currently in beta, allows users to define a machine learning workflow from data ingestion through training and inferencing.

As you can see, Kubeflow is an open source integrated tool chain for data science teams.  At the same time, Kubeflow enables the IT team to manage the infrastructure for the resulting data pipeline.

Cisco Kubeflow Starter Pack


To enable IT teams to work more closely with their data science counterparts, Cisco is introducing the Cisco Kubeflow Starter Pack, which provides IT teams with a baseline set of tools to get started with Kubeflow. The Cisco Kubeflow Starter Pack includes:

     ◉ Kubeflow Installer: Deploys Kubeflow on Cisco UCS and HyperFlex

     ◉ Kubeflow Ready Checker: Checks the system requirements for Kubeflow deployment. It also checks whether the particular prescribed Kubernetes distribution is able to support Kubeflow.

     ◉ Sample Kubeflow Data Pipelines: Cisco will be releasing multiple Kubeflow pipelines to provide data science teams working Kubeflow use cases for them to experiment with and enhance.

     ◉ Cisco Kubeflow Community Support:  Cisco will be providing free community support for Cisco customers who would like to check out Kubeflow.

Wednesday, 25 March 2020

AI for Networking: Separating the Hype from Reality

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Networks support explosive growth in traffic volume, connected mobile and IoT devices, and interconnected applications and microservices needed to deliver required services. Today’s networks generate massive amounts of data that exceed the ability of human operators to manage, much less understand.

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With unprecedented increases in network complexity and scale, AI is no longer just “a nice to have” – it is becoming essential to helping NetOps teams maintain service and network assurance. Network strategists already know this: More than 50% identify AI as a priority investment needed to deliver their ideal network.

AI: What can’t it do?


However, there are also a lot of over-blown expectations. As the engineering lead on AI for networking at Cisco, I often find myself in conversations about very futuristic, and somewhat unrealistic AI-enabled scenarios. It can be quite entertaining – but we also need to remember that today’s AI technology is not a panacea for every networking ailment.

For now, and for the next few years, AI will only help fully automate a limited set of straightforward use cases. In most cases, that require more complex and flexible analysis, AI will simply help human operators make quantifiably better and faster decisions.

AI: What can it do?


So, what can AI help us do today? One of the most common AI techniques, machine learning (ML) offers unique capabilities that operators can use to assure required network performance.

ML algorithms are certainly very powerful, but they also have a reputation of being difficult to design, tune, and adapt to a variety of situations and sometimes have been known to produce results that may be difficult to interpret.

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With Cisco AI Network Analytics, we have created a learning platform that solves issues where ML provides an indisputable and impactful benefit for network operators over existing technologies and approaches. This is possible thanks to the combination of two factors: (1) decades of experience in building some the world’s largest and most advanced networks and (2) deep expertise in ML algorithms that can effectively process networking data.

AI and ML have some useful applications


Let’s look at one of the more useful ML use cases – complex event processing. When applying ML to network telemetry, it is possible to establish dynamic baselines of what constitutes normal operating conditions for a given intent.

For example, the ML model(s) may be used to predict what should be the lower-upper bounds for a given KPI, for example, Wi-Fi on-boarding times. On-boarding refers to the set of complex tasks triggered when a wireless client attempts to join a wireless network.  Joining a network successfully and seamlessly contributes significantly to the Quality of Experience for the end user. Being able to monitor such complex, multidimensional KPIs so as to detect abnormal onboarding times, along with determining potential root causes should an issue occur, is a fundamental task for IT teams.

In this instance, Machine Learning (ML) allows for computing models used to predict the upper and lower bounds of the KPIs for on-boarding. KPIs falling outside a prediction as provided by the ML model would be considered “abnormal” for that unique network involved, and thus would be candidates for raising an alarm (that is, an alarm based on a learned bound, not based on a static value).

The figure below shows a predicted “band” (shown in green) of normal values for the percentage of failed onboarding sessions. As we can see, at some point the percentage of failed onboarding sessions (blue line) became abnormal (falling outside the green band), considering a number of network variables involved, as analyzed by the ML algorithm in use. This departure from normal to abnormal behavior for this network is denoted by the red section of the time-line in the diagram shown.

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Predicted range of normal values for the percentage of failed onboarding sessions

A second ML use case that has a lot of potential is correlated insights. ML can provide deeper insights and visibility into the operation of the network and even help predict when an anomalous condition is likely to occur in the future.

A third important use case would be root-causing. In some cases, an ML algorithm may be able to detect anomalies with associated root causing, whereas in other situations more than one ML algorithm may be used in conjunction with anomaly detection to provide root causing.

IBN and AI as disrupters


AI and advanced networking technologies like IBN are disrupting how things are done, especially for networking operations. Testing of new applications can be done in minutes instead of weeks. Troubleshooting gets significantly easier when an assurance engine identifies root causes and recommends fixes. In fact, when armed with powerful dashboards that offer actionable insights, a future network operator may only need to look in a handful of places, as opposed to plowing through heaps of possible causes.

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The intent-based networking (IBN) vision is that network teams will simply define the required behavior, and the network will know how to continuously align itself with what the business needs.

Wednesday, 29 January 2020

The Not-So-New Role of the Engineer in Complex Change: Master of Transitions

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The Age of Intelligence is here, and Cisco is in the midst of a transition — again. This transition is driven primarily not by AI and machine learning, but by the voices of our customers and their need to consume technology in new ways and digitally transform their businesses. While Cisco established itself in 1984 in the midst of a technology revolution, the need to continue evolving hasn’t slowed one bit.

Challenges Everywhere


In the 1980s, Cisco’s key product was the AGS Multi-Protocol Router, and alone it could solve a host of customer challenges. Today, our efforts to solve those challenges and provide the type of experience they demand has given way to multi-vendor and cross-architectural (multi-domain) solutions. These solutions are comprised of dozens of products and architectures across an array of companies.

The cloud has not alleviated the situation, as was promised early on. In reality, cloud has created additional complexity. Most customers are not only growing their business on-premises but also contending with the requirements of a hybrid-cloud environment. Interoperability between technologies and vendors adds yet another layer of challenges to be solved.

Security is paramount as no part of a corporate infrastructure can be left unprotected. The proliferation of personal devices into corporate IT also presents a new set of challenges. The mobile nature of today’s workforce requires wireless/mobility services that not only connect seamlessly, but also demand the same speed and reliability of hardwired devices. As corporate infrastructures continue to expand, the ability to manage multiple converged technology stacks has created even more complexity in the data center. The collision between software developers and network administrators creates challenges on how each side can complement each other to provide the best possible business solution for a customer or employer.

Clearly these are busy times! The questions I often hear from customers, are “This is crazy! Who can I trust to work with and figure this out? Who will put my best interests first and help me start down a path leading to my ultimate success?”

To make it work, you need people who thrive on complexity, problem-solving and change: the engineer.

Your Trusted Advisor: Systems Engineer


There has never been a better time to be a systems engineer (SE). With continuous change, it’s a good thing engineers thrive on complexity, and are comfortable being uncomfortable. Also, it’s a great thing that engineers at Cisco and our partners think about change in the context of customers and their ultimate experience. In fact, we hear from our customers who consistently note the Cisco SE as the individual they have the highest level of trust in to help them navigate these challenging waters. When customers are surveyed, they reference phrases like “put their interests first,” “honest/forthcoming,” and “Trusted Advisor.” When I speak live with customers it’s much of the same.

At Cisco within the global SE community we use a slogan to describe who we are, which I think captures things perfectly: “Masters of Transition since 1984.” That transition is alive 35 years later, and our systems engineering community is applying its skills very much as it did in the 1980s.

Have You Met Your Field Engineer?


While much of helping customers harness technology, and how it applies to their business, falls to the SE ranks, another group is becoming as critical to Cisco as the success of our customers. Field engineers (FE) have the deepest level of knowledge within technology disciplines across multiple vendors, help customers extract the value of the technology they’ve purchased, and work directly with customers to help train their employees to incorporate technology into the fabric of their work. The FE is the truest practitioner of technology expertise that exists within our industry. In short, if the FE isn’t successful then neither is the customer, partner, or Cisco. When customers ask, “who will see this entire project through with me”? I have a simple reply, “have you met your field engineer?”

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Ultimately responsible for ensuring the customer is able to not only fully extract the value of the technology purchased, the FE also assures that customers are comfortable integrating it into their existing or new business. The FE is truly where the rubber hits the road, so to speak in terms of customer receiving — and benefiting — from the technology they have acquired. With this responsibility it’s no wonder why Cisco is investing significantly in our reseller and partner FE community so that our customers are not just purchasing technology, they are activating, adopting and benefiting.

Driving Success Forward


Threats are everywhere. Outages can potentially cost millions of dollars. Change windows are harder to secure. Technology updates bombard organizations non-stop on a daily basis. Your engineering teams carry the full weight and burden of how business can (and should) realize the benefits of Cisco technology.

With complexity at its highest, pace of change at its quickest, and threats lurking around every corner, this is without a doubt a new Age of Intelligence, and engineers can lead the way forward.

Wednesday, 22 January 2020

Artificial Intelligence Translational Services Use Cases in Cisco Contact Centers

Artificial Intelligence and Translational Services


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The world is flattening; thus, with the business becoming increasingly global, the existing language barriers demand new solutions across vertical markets, especially when dealing with a company to a consumer.

Europe, with its 24 official different languages, is certainly posing some extra challenges to those companies delivering services across countries part of the union, and that’s nothing considering that there are more than 200 languages spoken on the continent.

The language barrier is undoubtedly and historically adding complexity to international business, and this is especially true when we consider Contact Centers and the high-quality customer experience they have to deliver in business-to-consumer services. While in business to business, there is a de facto international language, which is English. If there are consumers involved, that’s no longer an option —companies have to deal with the many languages spoken across countries.

Narrowing the Call Center Gap


With new generations of new consumers speaking their mother tongue language when calling a contact center, the translation problem will not disappear anytime soon. We should even expect the problem to become further challenging because of the increasing immigration of people. In 2017 2.4 million immigrants entered the EU from non-EU countries, and a total of 22.3 million people (4.4 %) of the 512.4 million people living in the EU on 1 January 2018 were non-EU citizens*

While these new immigrants will learn the local languages, they need to access services, especially public services, and this is quite a challenge, in particular for public administrations. In theory, a Contact Center could afford these challenges employing multilanguage agents or more agents. Still, it’s rather clear that this is far to be an optimal solution, and the associated costs are not negligible.

Apart from that, we are not talking about supporting two or three different languages, but rather a multitude of idioms; to depict the complexity of such a model, consider the challenges that this poses to a European contact center service in terms of WorkForce Management and Optimizations. When delivering a satisfied Customer Experience, it’s no longer just a matter of the number of agents we need each hour of the day, of the week, and the week of the month, but rather how many different languages they can speak — an authentic nightmare.

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The Growing Need for Multilanguage Agents


It is also happening quite often that the multilanguage agents might be good at speaking two or three languages but not necessary at writing those. Therefore, the challenge is even higher for Digital Contact Centers.

Recent advances in speech technology and Natural Language Understanding (NLU) have the potential to transform today’s challenges into new opportunities. Artificial Intelligence, integrated into Cisco Cognitive Contact Centers, could deliver an excellent solution to business problems like those described above. For example, a digital Cisco Cognitive Contact Centers could leverage Google AI DialogFlow capabilities to provide a Chat Translation Assistance Service, literally able to remove the language complexity and costs from the “Contact Center Work Force Optimization equation.” Let’s see how in the following proof of concept example:

Watch the Video:


This is the logical architecture used in the video.

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Another use case we may want to consider as a proof of concept is when traditional audio-only contact centers are located in a country abroad, where the cost of labor is cheaper. There are agents able to speak the required language even if they aren’t mother tongue. For example, this is the case for North African French-speaking contact centers, Est European Italian contact centers, and many more.

In cases like these, Cisco Cognitive Contact Centers powered by Artificial Intelligence could deliver an Audio Transcription and Translation Agent Assistance Service meant to assist the agent in dealing with foreign languages in a more natural, quicker, and more productive way. Let’s see how in the following proof of concept example:

Watch the video:


This is the logical architecture used in the video.

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Transforming Customer Experience with Contact Center AI


The Contact Center business is going through a series of significant changes driven by the technology innovation, the raise of socials, and the new consumption models that are being evaluated by most of the companies.

From a technology angle, there is very little doubt that the advent of Artificial Intelligence is transforming traditional call centers into Cognitive Call Centers. This arrival is turning an IT cost into a business strategy tool to increase Customer Experience, achieving higher customer service levels and quality, increasing the productivity of agents, and even lifting their traditional role to the new one: customer ADVISORS and CONSULTANTS.

Cisco has a portfolio of on-premise, hybrid, and cloud contact center solutions. That covers the undergoing migration to Cloud and the demand for a versatile, open, consistent architecture across on-premises, hybrid, and cloud solutions able to grant a smooth transition to the broad base of existing customers and at the same time allowing a consistent innovation adding digital channels and artificial intelligence.

Thursday, 21 November 2019

The Importance of the Network in Detecting Incidents in Critical Infrastructure

The network plays a key role in defending critical infrastructure and IoT. The devices that we are connecting drive our business, enabling us to make smarter decisions and gain greater efficiency through digitization. But how do we ensure those connected devices are acting as intended? From an industrial operations perspective, we need to know that plant operations are nominal, irrespective of cyber threat. The network is well positioned to assist us in detecting misbehaving devices.

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Network telemetry for visibility


In order to have assurance of business operations, it is critical to have visibility and awareness into what is occurring on the network at any given time. Network telemetry offers extensive and useful detection capabilities which can be coupled with dedicated analysis systems to collect, trend and correlate observed activity. In the security world we can infer much from network telemetry, from malware behaviour and reconnaissance, to data exfiltration. It is even possible to infer to some extent what is contained in encrypted traffic. Not only can we use this traffic for detection, but also for investigation. Having a historical record of communication also assists with investigating incidents. We can see, for example, what other hosts may have talked to a command and control server, or we can look at any lateral movement from a host.

The first step is to collect Netflow, which is a unidirectional sequence of packets with some common properties that pass through a network device. These collected flows are exported to an external device, the NetFlow collector. Network flows are highly granular; for example, flow records include details such as IP addresses, packet and byte counts, timestamps, Type of Service (ToS), application ports, input and output interfaces.

Exported NetFlow data is used for a variety of purposes, including enterprise accounting and departmental chargebacks, ISP billing, data warehousing, network monitoring, capacity planning, application monitoring and profiling, user monitoring and profiling, security analysis, and data mining for marketing purposes.

For most network devices (including many ruggedized devices used in OT environments), Netflow is simply an option you can turn on sending this data to a Netflow collector. Lower-end switches may not have this option; however, a span port can send traffic to a Netflow Sensor to accomplish this task. Gathering network telemetry visibility is the first step for organisations. The next steps are to utilise tools that can analyse the traffic and look for behavioural anomalies. For more advanced use cases, Encrypted Traffic Analytics (ETA) offers insights into encrypted traffic as well.

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Accelerating detection through smarter tooling


The problem of scale in IoT, is also evidenced in security incident detection and response, where we have more traffic to review, and accordingly, more events. We need tools to help us, and Machine Learning (ML) and Artificial Intelligence (AI) based tooling are important technologies, particularly when it comes to network behaviour. Devices, as opposed to humans, tend to have very defined behaviour, so leveraging ML and AI to observe and baseline this behaviour offers high fidelity alert sources.


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Leveraging context for better results


To really accelerate detection and lower our median time to detect, we need all our tools to work together. We discussed network context and understanding what a device policy should be, at scale. What if we could leverage that same information to assist with detection? Understanding contextual information and what a device’s policy should be, can help increase fidelity of behavioural alerts. Investigators also benefit from having this information integrated into their tools, which helps speed investigations.

Thursday, 26 September 2019

The Artificial Intelligence Journey in Contact Centers

I would like to share some thoughts pulled together in discussions with developers, customer care system integrators and experts, along one of the many possible journeys to unleash all the power of Artificial Intelligence (AI) into a modern customer care architecture.

First of all, let me emphasize what the ultimate goal of Artificial Intelligence is:

“Artificial intelligence is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”

AI in the Contact Center


In a Customer Care (CC) environment, one of the primary objectives of any AI component is to support and assist agents in their work and potentially even replace some of them, so we should ask ourselves  how far are we from such a goal today? Let me answer by posting here a recent demo on stage from Google:

Google Duplex: A.I. Assistant Calls Local Businesses To Make Appointments – YouTube

Seen it? While clearly Google Duplex is still in its infancy, I think it’s evident that a much wider application range is behind the corner and we are not too far away since the moment where a virtual agent controlled by an AI engine will be able to replace the real agents in many contexts and with a natural conversation and flow like humans would have with each other.

“Gartner projects that by 2020, 10% of business-to-consumer first level engagement requests will be taken by VCAs… today that is less than 1%….”

Google Duplex Natural Technology


The interesting piece, which leading players such as Cisco (Cognitive Collaboration Solutions) will be able to turn into an advantage for their Contact Center architecture, is related to the way Google Duplex works. It is essentially made of the 3 building blocks:

1. The incoming audio goes into an Automatic Speech Recognition (ASR) able to translate audio into text
2. A Recurrent Neural Network (RNN) formulates a text answer based on the input text
3. Other inputs and finally a Text to Speech (TTS) converts the answer back in audio using speech synthesis technology (Wavenet)

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For those of you dealing with customer care it’s rather clear how such an architecture would fit very well into an outbound contact center delivering telemarketing campaigns: this is the way Google is already positioning the technology in combination with their Google assistant.

A part the wonderful capabilities offered by the front and back end text to audio converters, the intelligence of the system is in the Recurrent Neural Network (RNN) able to analyze the input text and context, understand it and formulate a text answer in real time, de facto emulating the complex behavior and processes of human beings.

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The most of the CHAT BOTs used today in CC are not even close to this approach as they are doing the dialogue management in a traditional way, managing flows with certain rules, similar to a complex IVR, struggling with the complexity of natural language. Duplex (Tensorflow) or other solutions, such as open sources communities in the world of AI developers (Rasa Core), are adopting neural networks, properly trained and tuned in a specific context, to offer incredibly natural dialogue management. The RNN needs training, which was in the case of Duplex done using a lot of phone conversations data in a specific context as well as features from the audio and the history of the conversation.

CISCO and AI in Contact Centers


Some of the unique approaches explained above could make the customer care solutions of those vendors able to adopt them quite innovative and well perceived, especially when there is an underlying, solid and reliable architectural approach as a foundation. In news out last year, Cisco announced a partnership with Google:

“Give your contact center agents an AI-enhanced assist so they can answer questions quicker and better. …. we are adding Google Artificial Intelligence (AI) to our Cisco Customer Journey Solutions … Contact Center AI is a simple, secure, and flexible solution that allows enterprises with limited machine learning expertise to deploy AI in their contact centers. The AI automatically provides agents with relevant documents to help guide conversations and continuously learns in order to deliver increasingly more relevant information over time. This combination of Google’s powerful AI capabilities with Cisco’s large global reach can dramatically enhance the way companies interact with their customers.”

This whole AI new paradigm demands further thoughts:

1. The most of AI technology is OPEN SOURCE so the value is not in the code itself but more in the way it is made part of a commercial solution to meet customer needs, especially when it is based on an architectural approach, such as the Cisco Cognitive Collaboration..

2. The above point is further driven by the fact that it is difficult to build a general-purpose AI solution, as it will always need customizations according to the specific context of a customer or another. The use cases change and the speed of innovation is probably faster than in the mobile devices’ world, so it is difficult to manage this via a centralized, traditional R&D. This fits more into a community approach made of developers, system integrators and business experts such as the Cisco Ecosystems.

3. Rather then coding AI software, the winning factor is the ability of vendors like Cisco to leverage an environment made of Ecosystem partners, System Integrators and Customer experts to surround and enrich the core Customer Care architecture offerings.

The availability of ecosystem partners, able to package specific CONVERSATIONAL engines, specialized for certain contexts and the role of system integrators, able to combine those engines into the Cisco Customer Care architecture to meet the customer needs, are KEY CISCO competitive advantages in the coming AI revolution.