Artificial Intelligence Archives

DRC 2017 recap: “Our Journey to a Growing Developer Program”, Susie Wee – Cisco

by David I | Apr 14, 2017 | APIs, Developer Community, Developer Community Interactions, Developer Outreach, Developer Relations, DevRelate, Evans Data, Internet of Things, Programming, Women in Computing |

Susie Wee, VP and CTO of DevNet Innovations at Cisco Systems, gave a presentation at our recent 13th Annual Evans Data Developer Relations Conference. The following is a recap of her session, “Our Journey to a Growing Developer Program”. [David I note: the graphics used in this blog post were not part of the presentation slide deck]

Susie started her session by asking the audience a few questions to understand who was attending and what they wanted to get out of her talk. A great way to start any presentation in order to make any “course corrections” to help the audience.

Cisco DevNet APIs

Susie mentioned that the Cisco DevNet developer network started about 3 years ago. Before that time Cisco had a series of APIs and SDKs for developers but no real developer program and community. What Cisco had was more of a partner community to resell Cisco products. Certifications were offered for the partners. You could ask a couple of questions about the early outreach to developers: why does Cisco have a developer program and isn’t Cisco a networking hardware company?

She explained that Cisco DevNet is a developer community and an innovation ecosystem. Technologies that are available to developers include: Internet of Things, Software Defined Networking, Cloud computing, Collaboration technologies (many developers will recognize Cisco Jabber), Security solutions, Data Center offerings, DevOps solutions, Services and Open Source.

As part of Susie’s talk and also the main focus on the upcoming DevNet Create Conference (May 23-24, 2017 in San Francisco, CA), one of the main themes follows the sentence template of “Where Applications Meet xxx”. Developers who build applications should be able to easily fill in the “xxx” with some of the following: Infrastructure, Things (IoT), Places, People, Design, Architecture, Microservices, Deployment, Security, Analytics, etc. Between the apps that are developed there are interfaces to connect those apps to, well, everything! That is part of what Cisco provides beyond their traditional networking solutions.

Susie explained how Cisco DevNet focuses on helping developers:

Learn – Learn the latest Cisco API & technologies with guided Learning Tracks
Code – Use Sandbox Remote Labs: free 24×7 hosted labs for integrating and working with Cisco Technologies and explore sample Code on Github
Inspire – explore DevNet Creations to see the latest creations from developer community members and contribute your own
Connect – Connect with Cisco engineers and developer community members in multiple ways: using the DevNet Communities of Interest, at upcoming Cisco and DevNet Events, take part in the Cisco Innovation Challenge (2017 TBA), visit Cisco Innovation Centers to accelerate your opportunities, read the Latest DevNet Blog posts, and enhance your skills and meet developers at the DevNetZone at Cisco Live global events.

She mentioned that DevNet has more than 415,000 members, who work in more than 24,000 companies, provides 252 learning labs, provides 80 active APIs and more than 170 yearly developer outreach events.

Key to the success of Cisco DevNet are a laser focus on solving three key challenges: how to operate as a developer program, provide a clear value proposition for developers, and continue to grow a fiercely loyal developer community.

One of the stories that Susie mentioned was how DevNet attached itself onto the popular Cisco Live conferences that are help throughout the world. They put together all of their developer learning materials and created a DevNet zone on the side of the main conference. Attendees walked past the area and started telling their friends that there are cool learning labs over in this corner of the conference area. The buzz started to spread among attendees that there was a lab where you could develop software to integrate with Cisco technologies. John Chambers and his Cisco management team stopped by and saw what was happening in the DevNet theater and hands on lab. Now, at Cisco Live, the DevNet zone is the busiest section – Cool!

DevNet – 5 Lessons Learned

Susie shared the 5 lessons that they’ve learned during DevNet’s journey:

chalkboard-1927332_640

5) Operate like a startup and build up your developer credibility

4) Play to your strengths and build a technically talented “extended” team

3) Make your developer members heroes inside their companies and also in their communities

2) Help your team be wildly successful and ensure that your community has a heart

1) Innovate, Innovate, Innovate.

Innovate or Be Left Behind

Developers have to solve big problems. A developer program’s mission is to help developers build innovative solutions for their companies and their customers. Your developer program has to continue to provide innovative features, content and tools that will help your developer members create innovative applications. Our industry moves forward, fast. Developers move forward, fast. If your developer program does not innovate to keep up with developer needs, your company and your developer program will be left in the dust.

Thank you, Susie Wee and Cisco, for being a part of our 13th Annual Evans Data Developer Relations Conference.

Additional Information

Cisco DevNet – https://developer.cisco.com/

DevNet Create Conference (May 23-24, 2017 in San Francisco, CA)

Susie Wee’s session live stream replay is available on Facebook at https://www.facebook.com/ciscodevnet/videos/1962907540605184/

Session Title: DevNet: Fostering innovation where applications meet infrastructure

Session Description: How did a networking company start behaving like a software company and build a thriving developer community? How is DevNet achieving scale by engaging a broader internal and external community? The mission of Cisco DevNet is to provide developers with the tools, resources and code they need to create innovative, network-enabled solutions. But it’s more than just the technologies – DevNet is fostering innovation to help developers create seriously cool stuff. Join Susie Wee as she shares the successes, challenges and lessons learned in building a successful joint developer and innovation program, as well as what’s next for the DevNet community.

SusieWee

Susie Wee – VP and CTO of DevNet Innovations at Cisco Systems

Bio:

Susie is the Vice President and Chief Technology Officer of DevNet Innovations at Cisco Systems. She is the founder of DevNet, Cisco’s developer program for infrastructure and application developers, which catalyzes innovation by the developer ecosystem. DevNet covers the breadth of Cisco’s portfolio including networking, cloud, data center, security, collaboration and IoT. The innovations from DevNet improve end user experience, the operational experience and developer experience with the network. Under her leadership, the DevNet community has grown to over 400,000 developers in less than three years.

Prior to her current role, Susie was the Vice President and Chief Technology and Experience Officer of Cisco’s Collaboration Technology Group where she was responsible for driving innovation and experience design in Cisco’s collaboration products and software services, including unified communications, telepresence, web and video conferencing, and cloud collaboration. Before joining Cisco, Susie was the founding Vice President of Experience Software Business and CTO at Hewlett Packard, and Lab Director at HP Labs. Susie was the co-editor of the JPSEC standard for the security of JPEG-2000 images. She was formerly an associate editor for the IEEE Transactions on Circuits, Systems and Video Technology and IEEE Transactions on Image Processing. While at HP Labs, Susie was a consulting assistant professor at Stanford University where she co-taught a graduate-level course on digital video processing.

Susie received Technology Review’s Top 100 Young Innovators award, ComputerWorld’s Top 40 Innovators under 40 award, the Red Dot Design Concept award for augmented collaboration, the INCITs Technical Excellence award, the Women in Technology International Hall of Fame award, and was on the Forbes Most Powerful Women list. She is an IEEE Fellow for her contributions in multimedia technology and has over 50 international publications and 57 granted patents. Susie received her B.S., M.S., and Ph.D. degrees in electrical engineering and computer science from the Massachusetts Institute of Technology.

Machine and Deep Learning SDKs, Tools, Frameworks and Systems

by David I | Jan 12, 2017 | APIs, Big Data, Deep Learning, Developer Relations, DevRelate, Machine Learning |

We’ve seen the rise of multiple big data solutions in the past few years. Building on top of the volume, variety and velocity of data, we’ve seen the growing need for automating business decisions based on the knowledge coming from online systems, sensors and connected devices. In order to take advantage of this wealth of data we’re seeing the rapid rise of a wide range of machine and deep learning SDKs, tools, frameworks, systems, services, and libraries. This blog post highlights some of the available machine learning and deep learning SDKs available from leading platform vendors, hardware vendors, researchers, and open source projects. It’s a great time to be a software engineer and to have all of these technologies provided by developer relations programs.

Machine and Deep Learning SDKs

Here are a few of the many machine learning and deep learning SDKs, tools, frameworks, systems, services, and libraries that are available for developers to use in their cognition-based, big data driven applications. I’ve divided the list up into services/systems, frameworks, libraries and tools (although the distinctions are arbitrary as some provide both a service along with an API, SDK or framework).

Services/Systems

IBM Watson – https://www.ibm.com/watson/
Amazon Machine Learning – https://aws.amazon.com/machine-learning/
Google Cloud Prediction API – Prediction’s cloud-based machine learning and pattern recognition tools can help analyze your data to add various features to your applications – https://cloud.google.com/prediction/
Veles – Open Source (by Samsung) distributed platform for rapid Deep Learning app development – https://velesnet.ml/
H20 AI – open source machine learning platform for enterprises – http://www.h2o.ai/
Apache PredictionIO – open source Machine Learning Server built for developers and data scientists to create predictive engines for any machine learning task – http://predictionio.incubator.apache.org/

Frameworks

Apache Singa – deep learning framework (in the Apache incubator) – https://singa.incubator.apache.org/en/index.html
Caffe Deep Learning Framework – http://caffe.berkeleyvision.org/
Oryx2 – Lambda architecture on Apache Spark, Apache Kafka for real-time large scale machine learning – http://oryx.io/
Neon (Nervana Systems) – Python based Deep Learning Framework – https://github.com/NervanaSystems/neon
Torch – Scientific Computing Machine Learning framework/algorithms – http://torch.ch/
PyTorch – A deep learning framework that puts Python first. In an early-release Beta.- http://pytorch.org/
Accord.net – .NET machine learning framework combined with audio and image processing libraries completely written in C# – http://accord-framework.net/
MOA (Massive Online Analysis) – open source framework for data stream mining – http://moa.cms.waikato.ac.nz/

Libraries

TensorFlow – open source library for Machine Learning (from Google) – https://www.tensorflow.org/
Apache Spark MLlib – library of algorithms for Spark – http://spark.apache.org/mllib/
Microsoft Azure ML – https://studio.azureml.net/
Microsoft Distributed Machine Learning Toolkit (DMTK) – http://www.dmtk.io/
Intel BigDL – BigDL is a distributed deep learning library for Apache Spark; users can write their applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters. – https://github.com/intel-analytics/BigDL
DeepLearning4j – commercial-grade, open-source, distributed deep-learning library written for Java and Scala – https://deeplearning4j.org/
Microsoft Cognitive Toolkit – trains deep learning algorithms to learn like the human brain – https://www.microsoft.com/en-us/research/product/cognitive-toolkit/
Theano – python deep learning library – http://deeplearning.net/software/theano/
Brainstorm – Fast, flexible and fun neural networks. – https://github.com/IDSIA/brainstorm
mlPack – scalable machine learning library – http://mlpack.org/
Shogun – open source machine learning toolbox provides a wide range of unified and efficient Machine Learning (ML) methods – https://github.com/shogun-toolbox/shogun
OpenNN – open source class library written in C++ programming language which implements neural networks – http://www.opennn.net/

Tools/SDKs

Intel Deep Learning SDK – Tools to accelerate deep learning training and deployment – https://software.intel.com/en-us/deep-learning-sdk
NVIDIA Deep Learning SDK – tools and libraries for designing and deploying GPU-accelerated deep learning applications – https://developer.nvidia.com/deep-learning-software

Machine Learning Courses

Udacity: Introduction to Machine Learning – https://www.udacity.com/course/intro-to-machine-learning–ud120
Udacity: Deep Learning by Google – Take machine learning to the next level (free course) – https://www.udacity.com/course/deep-learning–ud730
edX: Machine Learning for Data Science and Analytics – https://www.edx.org/course/machine-learning-data-science-analytics-columbiax-ds102x-0
Open Classroom: Machine Learning by Andrew Ng – http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning
Udacity: Machine Learning Engineer Nanodegree – https://www.udacity.com/course/machine-learning-engineer-nanodegree–nd009
Udacity: Artificial Intelligence Engineer Nanodegree – https://www.udacity.com/ai

Evans Data’s AI and Big Data Developer Research Report 2016 V2

This report focuses on tools, methodologies, and concerns related to implementing machine learning, deep learning, image recognition, pattern recognition and other forms of artificial intelligence as well as efficiently storing, handling, and analyzing large datasets and databases from a wide range of sources. Artificial intelligence is permeating software development in many ways and many industries, which necessitates a thorough knowledge of how developers are doing this. Big Data, often related, is also becoming a reality for more and more companies; this report provides valuable insight into developer opinions on these topics.

This volume includes research and analysis covering topics such as Perceptions of the AI and Big Data Landscapes, AI & Big Data Developer Demographics, Decision-Making for AI & Big Data, Barriers and Challenges for Data Analytics, AI Concept and Approaches, Conversational Systems & Virtual Assistants, Real-Time Events & Time Series Processing, Big Data & IoT, Collaboration in Big Data & Data Science, Advanced Analytics Tools and Services, Databases & Data Warehousing, Hadoop, Parallelism & Big Data, Operating Systems & Languages, and Tools Used for AI & Big Data.

You can take a look at the AI and Big Data Developer Research report table of contents and sample report pages at http://www.evansdata.com/reports/viewRelease.php?reportID=37

Does your Developer Relations Program provide a Machine or Deep Learning SDK, Tool, Library, Framework or Service?

If you’re looking for additional Machine Learning frameworks, libraries, and software you can check out the “Awesome Machine Learning” curated list of resources on GitHub. You’ll find additional resources for a wide range of programming languages.

Would it be cool if your developer relations program used AI to support your community members? Using AI and bot technology to answer common questions or point developers in the right direction?

Am I missing a machine learning or deep learning library, framework, SDK, tool, service, system or API that your developer relations program provides? Send me an email if I am missing one or more.

David Intersimone “David I”
Vice President of Developer Communities
Evans Data Corporation
davidi@evansdata.com
Blog: https://www.devrelate.com/blog/
Skype: davidi99
Twitter: @davidi99

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