cloud2030

Highlights 0 min 37 sec: Introduction of Guest 2 min 28 sec: High level description of the technology Cloud centralization is a problem; it needs to be decentralized Take over 100% or a partial amount of a machine added to the cloud network with special CoreOS based system 5 min 13 sec: Akash Network Donate your idle servers into an available system for usage in a cloud managed by Overclock Labs 2 Components – Blockchain for marketplace / Deployment platform 6 min 52 sec: How does marketplace work; who wants to use this network? Focus is on developers who want to use these systems #1 Reason – Cheaper than standard public clouds ; #2 Reason – Its distributed globally not at fixed known sites 10 min 26sec: Blockchain as decentralized ledger to avoid central store Join the infrastructure without a formal registration on a single database; only listening for bids not publishing what they offer History of the infrastructure is public for customers to evaluate 12 min 31 sec: How do I know where I am pushing my workloads? Can I trust the infrastructure provider? Issues arise with receiving workloads that are unknown to you 16 min 38 sec: What do I do to add a rack of servers into Akash network? What you need to do vs what you should do Management Server, Network Isolation, Monitoring Seasonal Load Model ~ Electric Grid Analogy 20 min 23 sec: Identify Geography and Latency to Customers? 21 min 23sec: What do I ensure I am not getting dropped or pulled into a bidding trap? Workload goes down it automates a bid elsewhere in the network Conditions can be set should you need a long running workload and it is terminated by the host Do you expect providers to monitor machines or do you as a service? 27 min 45 sec: Kubernetes Cluster across providers? Kubernetes Federation Why Kubernetes? Writing our own Kubernetes using Kubernetes 31 min 55 sec: Why not do this with Virtual Machines? Containers makes sense 32 min 40 sec: How long have you been working on this project? Why of the project? DISCO → Scheduler Decentralization is the key Can build a private infrastructure using their system 37 min 01 sec: Wrap-Up

Joining us this week is Syed Zaeem Hosain, CTO and Founder of Aeris from the KeyBanc Emerging Tech Summit. Aeris is a technology partner with a proven history of helping companies unlock the value of IoT. For more than a decade, we’ve powered critical projects for some of the most demanding customers of IoT services. Aeris strives to fundamentally improve businesses by dramatically reducing costs, accelerating time-to-market, and enabling new revenue streams. Built from the ground up for IoT and globally tested at scale, Aeris IoT Services are based on the broadest technology stack in the industry, spanning connectivity up to vertical solutions. As veterans of the industry, we know that implementing an IoT solution can be complex, and we pride ourselves on making it simpler.

Joining us this week is Val Bercovici, Founder & CEO of PencilDATA at KeyBank Emerging Tech Summit. About PencilDATA PencilDATA is a software-as-a-service startup embracing data governance allowing users to see and manipulate data easily. It achieves this with a blockchain ledger that accounts for all data activity, Highlights  0 min 48 sec: Introduction from Val Bercovici o Post – Blockchain in the Cloud by Rob Hirschfeld o Robotic Process Automation (RPA)  4 min 47 sec: What do you use Blockchain for? o Core to the value but is just an enabler  5 min 52 sec: Don't confused Blockchain with ICO/Crypto-currency o Blockchain rollouts in 2018 have paused ~ Gartner quote o HOMEWORK – Podcast with BlockChain Technology Partners o PencilDATA has built the S3 of Blockchains  10 min 08 sec: Blockchain Service or Acting on BC with distributed service? o Useful Zero Trust Solution o Autonomous Vehicle Taxi Service Use Case o Same workflow and processes needed for medical equipment  15 min 59 sec: Why Distributed Ledgers for these Use Cases? o Centralized authorities can be a single point of failure  19 min 51 sec: Data security issues around Veracity & Authentication o Data poison can train AI poorly o Explainable AI ~ Data Reproduce-ability; prove data is valid  25 min 54 sec: SalesForce Use Cases o Live on the SalesForce App Exchange o Launching at DreamForce 2018 with Customer References  27 min 18 sec: How PencilDATA provide value to SalesForce Customers? o SaaS Turnkey Solution that hides the behind the scenes work o Costs for Etherium usage  37 min 14 sec: Industrial IoT Use Case  37 min 31 sec: Wrap-Up

Joining us this week is Ash Young, Chief Evangelist of Cachengo and OPNFV Ambassador. Cachengo builds smart, predictive storage for machine learning. NOTE – We had a microphone problem that is solved at the 9 minute 19 second mark of the podcast. Start there if you find the clicking noise an issue

Highlights 1 min 55 sec: Define Cloud from a CloudOps perspective Business Model and an Operations Model 3 min 59 sec: Update from Google Next 2018 event Google is the “Engineer's Cloud” Google's approach vs Amazon approach in feature design/release 9 min 55 sec: Early Amazon ~ no easy button Amazon educated the market as industry leader 12 min04 sec: What is the state of Hybrid? Do we need it? Complexity of systems leads to private, public as well as multiple cloud providers Open source enabled workloads to run on various clouds even if the cloud was not designed to support a type of workload Google's strategy is around open source in the cloud 14 min 12 sec: IBM visibility in open source and cloud market Didn't build cloud services (e.g. open a ticket to remap a VLAN) 16 min 40 sec: OpenStack tied to compete on service components Couldn't compete without Product Managers to guide developers Missed last mile between technology and customer Didn't want to take on the operational aspects of the customer 19 min 31 sec: Is innovation driven from listening to customers vs developers doing what they think is best? OpenStack is seen as legacy as customers look for Cloud Native Infrastructure OpenStack vs Kubernetes install time significance 22 min 44 sec: Google announcement of GKE for on-premises infrastructure Not really On-premise; more like Platform9 for OpenStack GKE solve end user experience and operational challenges to deliver it 26 min 07 sec: Edge IT replaces what is On-Premises IT Bullish on the future with Edge computing 27 min 27 sec: Who delivers control plane for edge? Recommends Open Source in control plan 28 min 29 sec: Current tech hides the infrastructure problems Someone still has to deal with the physical hardware 30 min 53 sec: Commercial driver for rapid Edge adoption 32 min 20 sec: CloudOps building software / next generation of BSS or OSS for telco Meet the needs of the cloud provider for flexibility in generating services with the ability to change the service backend provider Amazon is the new Win32 38 min 07 sec: Can customers install their own software? Will people buy software anymore? Compare payment models from Salesforce and Slack Google allowing customers to run their technology themselves of allow Google to manage it for you 40 min 43 sec: Wrap-Up

Joining us this week is Jason Hoffman, CEO MobiledgeX, a startup “creating a global marketplace for organizations to deliver and drive the business of these edge enabled services and products.” Highlights Joyent – Cloud Computing’s Amiga Technology for You vs Technology for Customer How we Got Here and will Get to Edge Anchoring Edge into Current Needs Humans and Machines Integration – Computer Vision Software and Hardware Support and Aging Issues in House Mixed Reality solves Social issues of Smartphones Reality of Physics in Building the Edge ~ Battery Life is Killer App Network to Data Centric View Switch ~ Edge Data is the S3 of Cloud Lack of Understanding in Early Web Infrastructure vs Email Understanding Lack of Evolution in Hardware Provisioning - Evil Firmware Horrible Parenting Advice

VM Brasseur from Open Source Initiative along with Rob Hirschfeld and Stephen Spector talk about the license announcement made by Redis Labs to add Common Clause (https://redislabs.com/community/licenses/) to some of their software. Also discussed is the limited success of the Open Core business model.

Joining us this week is VM (Vicky) Brasseur from Open Source Initiative. Highlights 1 min 20 sec: Types of Companies Supported Companies don’t know why they want to do open source 3 min 23 sec: Can’t Abandon Software into Community Communities don’t instantly form around your software 4 min 22 sec: Is Marketing “Evil” in Open Source Marketing has become associated with advertising which is invasive 5 min 19 sec: Define Marketing Discovering the needs of the market and build to support it Technologists don’t really understand marketing 6 min 17 sec: Issues of Marketing in Open Source What is your goal in using open source? 8 min 15 sec: Everyone has their own Goal in Open Source There is no bullet point list of how to do open source 9 min 29 sec: Issues of Accessing Projects and Support for Free Open source done a poor job of talking about value being provided People assume big projects are paid for by large companies so they take advantage 13 min 19 sec: Difficult to Tell New User’s End State as Communities Invest in Helping them Learn the Technology Some communities set the bar too high for user participation Open source does a bad job in documentation typically 17 min 9 sec: What about CI/CD Systems? Not a significant issue today in open source for contributors 19 min 21 sec: How help People Build Empathy for Users and Maintainers and Impact when Corporations are involved 21 min 29 sec: How Communities Manage Competitive Companies Working Together Foundations play a key role in managing these issues Open source is not corporate! The vast majority of open source is not corporate based Is there less concern for the tiny project’s vs the large corporate projects? Substantial lack of awareness of this in user community 25 min 04 sec: Price to a Community when a Customer Pulls out and Uses Corporate Version of Solution? Should companies pay for open source software they grab from community? Many ways to contribute other than money 27 min 56 sec: Business Continuity Issues in Open Source Free and open source software needs to be part of business continuity plans Vendor software solutions are equally risky as open source solutions 30 min 36 sec: Define Copyleft 4 Freedoms of Copyleft from Richard Stallman Can never change the license of Copyleft software 32 min 43 sec: Open Core Model Analogy of a restaurant with more tables than it can serve Proprietary products with open source APIs / SDKs

Rob Hirschfeld and Greg Althaus discuss their recent experiences with customers in truly understanding what provisioning is and what it is not. They even compare where the word provision comes from.

Joining us this week is Mathew Lodge, SVP of Products & Marketing of Anaconda. Highlights • 2 min 57 sec: What does Anaconda do? o Help data scientists be productive & enterprise AI / Data Science • 3 min 36 sec: How do you interact with Anaconda? o About 2.5 million downloads a month of Anaconda Distribution o Install binary packages for data science to Python • 5 min 55 sec: Who are data scientists? o Data wrangling and understanding • 9 min 12 sec: Data Science as a verb o Understand how to turn data into actionable insight • 10 min 47 sec: How learn to use the tools? Community! o Community around Anaconda open source to share packages, etc • 13 min 26 sec: How does Anaconda change as AI/Machine Learning improve? o Python is standard language with R close behind for data science • 14 min 58 sec: Reproducibility in results o 16 min 01 sec: Model training issue? • 17 min 16 sec: Parking lot on Sam Charrington’s AI Bias Podcasts o TWiML & AI - https://twimlai.com/ • 17 min 43 sec: Training models for limited sets of data for reliability in Edge o Answer by example of Google ImageNet o 20 min 14 sec: Optimizations to reduce processing requirements  Hey Siri example on how iPhone works o 22 min 03 sec: Do models improve over time? Transfer learning • 22 min 30 sec: Accelerative Learning in AI o Fashion example of layering learning o Issues around lack of data for training • 26 min 01 sec: Portability of models via Anaconda • 26 min 48 sec: Cloud Native Model of AI (no longer 2004) o Moved on from Java and distributed computing to Kubernetes o 29 min 05 sec: Giving up data locality (Hadoop) & specialized hardware? o 32 min 42 sec: Cloud model gives private and public options • 34 min 23 sec: How Anaconda play into the Cloud Native data science model? o Data scientists interested in data problems not cloud architecture o Data science as a Service o Kubernetes & Docker installed for you by Anaconda • 38 min 05 sec: WRAP UP o Anaconda Con Videos