H2O.ai - Road Ahead - keynote presentation by Sri Ambati

So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it. As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop. Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #datasciencehappiness.

Navdeep Gill @ Galvanize Seattle- May 2016 - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

This document discusses using H2O's random grid search for hyperparameter optimization. It begins with introductions of the presenter and H2O company. It then draws an analogy between a baker's process of making cake and a data scientist's process of building models. The document explains common techniques for hyperparameter optimization including manual search, grid search, and random grid search. It provides evidence that random search performs as well as manual/grid search in less time. Finally, it demonstrates H2O's random grid search API in Python and discusses other useful H2O features.

H2O Deep Water is a tool that integrates distributed deep learning with H2O's machine learning platform. It allows users to build, stack, and deploy deep learning models from libraries like TensorFlow, MXNet, and Caffe through a unified interface. Deep Water inherits properties from H2O like scalability, ease of use, and deployment capabilities. It also makes deep learning more accessible by supporting popular network architectures and allowing easy ensemble of deep models with other H2O algorithms.

Transformation with Data and AI, H2O Open Dallas 2016, Keynote by Sri Ambati, founder @h2o.ai @srisatish - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

Hank Roark's presentation at Galvanize SF, 02.23.16 - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

Joe Chow gave a presentation about machine learning use cases using H2O. He introduced H2O, an open source machine learning platform that works with R, Python, and other languages. He discussed how companies like Telenor and customers in Brazil are using H2O for tasks like predictive modeling. Joe also highlighted current algorithms in H2O and how it is evolving with new features like deep learning integration. The talk provided an overview of H2O and real-world examples of how organizations are applying machine learning with the platform.

Presented at #H2OWorld 2017 in Mountain View, CA. Enjoy the video: https://youtu.be/r9S3xchrzlY. Learn more about H2O.ai: https://www.h2o.ai/. Follow @h2oai: https://twitter.com/h2oai. - - - Abstract: Venkatesh will explore how driverless AI is helping to keep fraudsters at bay. Share results from experiments conducted on large scale payment transaction data. Venkatesh's Bio: Venkatesh is a senior data scientist at PayPal where he is working on building state-of-the-art tools for payment fraud detection. He has over 20+ years experience in designing, developing and leading teams to build scalable server-side software. In addition to being an expert in big-data technologies, Venkatesh holds a Ph.D. degree in Computer Science with specialization in Machine Learning and Natural Language Processing (NLP) and had worked on various problems in the areas of Anti-Spam, Phishing Detection, and Face Recognition.

The document discusses building real-time targeting capabilities at Capital One. It introduces two speakers, Ryan Zotti and Subbu Thiruppathy, and describes challenges around striving for speed in everything. It then covers how to achieve fast model data, training, deployment, and scoring through techniques like using the most up-to-date data, distributed computing in the cloud, automatic model refitting, and response times under 100 milliseconds.

Shirshanka Das and Yael Garten describe how LinkedIn redesigned its data analytics ecosystem in the face of a significant product rewrite, covering the infrastructure changes that enable LinkedIn to roll out future product innovations with minimal downstream impact. Shirshanka and Yael explore the motivations and the building blocks for this reimagined data analytics ecosystem, the technical details of LinkedIn’s new client-side tracking infrastructure, its unified reporting platform, and its data virtualization layer on top of Hadoop and share lessons learned from data producers and consumers that are participating in this governance model. Along the way, they offer some anecdotal evidence during the rollout that validated some of their decisions and are also shaping the future roadmap of these efforts.

The document discusses big data and open source tools and technologies. It provides an overview of key challenges for data leaders, introduces the top 10 big data tools including Apache Spark, R, and Talend Open Studio. It outlines the benefits of open source including low costs, flexibility, and innovation. The document advocates adopting both corporate and open source software using a "bi-modal" approach to support innovative and engineered analytics. It provides a template for a 1-page big data strategy.

This is a hands-on tutorial for R beginners. I will demonstrate the use of two R packages, h2o & LIME, for automatic and interpretable machine learning. Participants will be able to follow and build regression and classification models quickly with H2O’s AutoML. They will then be able to explain the model outcomes with a framework called Local Interpretable Model-Agnostic Explanations (LIME).

These slides were presented by Marios Michailids and John Spooner at Dive into H2O: London on June 17, 2019. Marios's session can be found here: https://youtu.be/GMtgT-3hENY John's session can be found here: https://youtu.be/5t2zw4bVfsw

Deep Learning and the new wave of AI are inevitably coming to your business area. If you are a manager and if you are trying to make sense of all the buzzwords, this session is four you. We will show you what is Deep Learning in a way that you will understand how it works and how can you apply it. We then expand the scope and apply the deep learning and AI techniques in the Big Data context. You will learn about things that don't work out so well, the risks and challenges in both applying and developing with deep learning and AI technologies. We conclude with practical guidance on how to add the exciting deep learning and AI capabilities to your next project. Outline: - The path to Deep Learning - From machine learning to Deep Learning - But how does it work? - Deep Learning architectures - Deep Learning applications - Deep Learning at scale - Running AI at scale - Deep learning at Scale using Spark - The trouble with AI - Application challenges - Development challenges - How to start your first Deep Learning project

Discover the different AI applications and the different tools for the deep learning workflows to achieve them.

KeyNote #DBInsights" on 7 April. My views on the DBAs fears, doubts and opportunities in the age of DevOps, Cloud, Big Data, Open Source, bi-modal IT, Pizza teams, you name it.

This talk was recorded in London on October 30, 2018. KNIME Analytics Platform is an easy to use and comprehensive open source data integration, analysis, and exploration platform, enabling data scientists to visually compose end to end data analysis workflows. The over 2,000 available modules ("nodes") cover each step of the analysis workflow, including blending heterogeneous data types, data transformation, wrangling and cleansing, advanced data visualization, or model training and deployment. Many of these nodes are provided through open source integrations (why reinvent the wheel?). This provides seamless access to large open source projects such as Keras and Tensorflow for deep learning, Apache Spark for big data processing, Python and R for scripting, and more. These integrations can be used in combination with other KNIME nodes meaning that data scientists can freely select from a vast variety of options when tackling an analysis problem. The integration of H2O in KNIME offers an extensive number of nodes and encapsulating functionalities of the H2O open source machine learning libraries, making it easy to use H2O algorithms from a KNIME workflow without touching any code - each of the H2O nodes looks and feels just like a normal KNIME node - and the data scientist benefits from the high performance libraries and proven quality of H2O during execution. For prototyping these algorithms are executed locally, however training and deployment can easily be scaled up using a Sparkling Water cluster. In our talk we give a short introduction to KNIME Analytics Platform and then demonstrate how data scientists benefit from using KNIME Analytics Platform and H2O Machine Learning in combination by using a real world analysis example. Bio: Christian received a Master’s degree in Computer Science from the University of Konstanz. Having gained experience as a research software engineer at the University of Konstanz, where he developed frameworks and libraries in the fields of bioimage analysis and machine learning, Christian moved on to become a software engineer at KNIME. He now focuses on developing new functionalities and extensions for KNIME Analytics Platform. Some of his recent projects include deep learning integrations built upon Keras and Tensorflow, extensions for image analysis and active learning, and the integration of H2O Machine Learning and H2O Sparkling Water in KNIME Analytics Platform.

This document provides an overview of H2O.ai, an open source in-memory machine learning platform. It describes H2O.ai's product as an in-memory prediction engine, its team of 37 distributed systems engineers doing machine learning, and its headquarters in Mountain View, CA. It also provides details on how to use H2O with R and Python for scalable machine learning on large datasets across distributed systems.

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. http://docs.0xdata.com/datascience/deeplearning.html - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

In recent years, deep learning has taken the lead in predictive accuracy in many fields of machine learning, and companies are struggling to keep up with the speed of innovation. Arno Candel demonstrates how successful enterprises can augment simple statistical models with more accurate data-driven models to gain a competitive edge. Arno describes how to build smart applications that include data munging, model training and validation, and real-time production deployment—every step is based on open source code (R, Python, Java, Scala, JavaScript, REST) that runs on distributed platforms including Hadoop, Spark, and standard compute clusters. Arno also presents use cases from verticals including insurance, fraud, churn, fintech, and marketing and offers live demos of smart applications on large real-world datasets in distributed clusters. - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

Exploit Research and Development Megaprimer http://opensecurity.in/exploit-research-and-development-megaprimer/ http://www.youtube.com/playlist?list=PLX3EwmWe0cS_5oy86fnqFRfHpxJHjtuyf

This document discusses category theory concepts including functors, exponential functors, currying, and monads. It provides examples of functors such as the identity functor, product functor, and +1 functor. It explains how currying relates exponential functors to function spaces. It also gives examples of functors between categories of integers and rationals. Finally, it defines monads as endofunctors with natural unit and flatten operations.

This document discusses incident tracking using the VERIS framework. It begins by introducing VERIS as an open-source framework for describing security incidents using a common vocabulary to help with detection, response, and data sharing. It then discusses how VERIS can be implemented through either integrating it with an IT ticketing tool, though this requires customization that is difficult, or through a manual custom system, which is not scalable. The document concludes that properly tracking security incidents requires integrating VERIS classifications into an IT ticketing system through programming, unless a custom solution is developed.

Samsung’s first Tizen-based devices are set to launch in the middle of 2015. This paper presents the research outcome on the security analysis of Tizen OS and it’s underlying security architecture. The paper begins with a quick introduction to Tizen architecture and explains the various components of Tizen OS. This will be followed by Tizen’s security model where application sandboxing and resource access control will be explained. Moving on, an overview of Tizen’s Content Security Framework which acts as an in-built malware detection API will be covered. Various vulnerabilities in Tizen will be discussed including issues like Tizen WebKit2 address spoofing and content injection, Tizen WebKit CSP bypass and issues in Tizen’s memory protection (ASLR and DEP). Applications in Tizen can be written in HTML5/JS/CSS or natively using C/C++. As a bonus, an overview of pentesting Tizen applications will also be presented along with some of the security implications. There will be comparisons made to traditional Android applications and how these security issues differ with Tizen.

Tizen is an open source operating system that can run on various devices including smart TVs and IoT devices. It uses a security model that isolates applications using SMACK mandatory access control and enforces content security policies for web applications. The presentation discusses hacking techniques tested against Tizen like exploiting shellshock vulnerabilities, bypassing address space layout randomization protections, and circumventing content security policies. It also provides an overview of methodologies for analyzing Tizen application security like static analysis of manifest and configuration files, decompiling native applications, and network analysis using a proxy. Overall the presentation evaluates the security of Tizen and highlights some implementation issues found.

The paper is about abusing and exploiting Firefox add-on Security model and explains how JavaScript functions, XPCOM and XPConnect interfaces, technologies like CORS and WebSocket, Session storing and full privilege execution can be abused by a hacker for malicious purposes. The widely popular browser add-ons can be targeted by hackers to implement new malicious attack vectors resulting in confidential data theft and full system compromise. This paper is supported by proof of concept add-ons which abuse and exploits the add-on coding in Firefox 17, the release which Mozilla boasts to have a more secure architecture against malicious plugins and add-ons. The proof of concept includes the implementation of a Local keylogger, a Remote keylogger, stealing Linux password files, spawning a Reverse Shell, stealing the authenticated Firefox session data, and Remote DDoS attack. All of these attack vectors are fully undetectable against anti-virus solutions and can bypass protection mechanisms.

Top 10 Data Science Practitioner Pitfalls Meetup with Erin LeDell and Mark Landry on 09.09.15 - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

This document outlines security vulnerabilities in Firefox add-ons and demonstrates proof of concept exploits. It discusses how Firefox add-ons have full privileges without sandboxing, allowing exploits like keyloggers and downloading executables. Attack techniques to spread malicious add-ons like social engineering and tabnabbing are described. Mitigations include updating Firefox, using antivirus software, and disabling session restoring. The document aims to demonstrate weaknesses to motivate the Firefox team to improve add-on security.

This presentation is about abusing Google Apps to implement various attacks that ranges from Hostless Phishing to setting up a Botnet’s Command & Control Center.

Mobile Application market is growing like anything and so is the Mobile Security industry. With lots of frequent application releases and updates happening, conducting the complete security analysis of mobile applications becomes time consuming and cumbersome. In this talk I will introduce an extendable, and scalable web framework called Mobile Security Framework (https://github.com/ajinabraham/YSO-Mobile-Security-Framework) for Security analysis of Mobile Applications. Mobile Security Framework is an intelligent and automated open source mobile application (Android/iOS) pentesting and binary/code analysis framework capable of performing static and dynamic analysis. It supports Android and iOS binaries as well as zipped source code. During the presentation, I will demonstrates some of the issues identified by the tool in real world android applications. The latest Dynamic Analyzer module will be released at OWASP AppSec. Attendees Benefits * An Open Source framework for Automated Mobile Security Assessment. * One Click Report Generation and Security Assessment. * Framework can be deployed at your own environment so that you have complete control of the data. The data/report stays within the organisation and nothing is stored in the cloud. * Supports both Android and iOS Applications. * Semi Automatic Dynamic Analyzer for intelligent application logic based (whitebox) security assessment.

The era of Big Data has passed, and the era of sensory overload – that is, the proliferation of sensor data – is upon us. The challenge today is how to create the next generation of business and consumer applications that transform how we interact with sensors themselves. Applications need to learn from every user interaction and data point and predict what can happen next. The future depends on Machine Learning, as much as it depends on the data itself, to change the way we interact with these systems. In this talk, we explain H2O’s scalable distributed in-memory math architecture and its design principles. The platform was built alongside (and on top of) both Hadoop and Spark clusters and includes interfaces for R, Python, Scala, Java, JavaScript and JSON, along with its interactive graphical Flow interface that make it easier for non-engineers to stitch together complete analytic workflows. We outline the implementation of distributed machine learning algorithms such as Elastic Net, Random Forest, Gradient Boosting and Deep Learning. We will present a broad range of use cases and live demos that include world-record deep learning models, anomaly detection tools and approaches for Kaggle data science competitions. We also demonstrate the applicability of H2O in enterprise environments for real-world customer production use cases. By the end of this presentation, you will know how to create your own machine learning workflows on your data using R, Python (iPython Notebooks) or the Flow GUI.

H2O World 2015 - Mark Landry Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

Completed presentation that I took to the UK the first time I went and closed 80% of the clients I visited.

Ajin Abraham presents the Mobile Security Framework, an open source tool for automating security analysis of Android and iOS mobile applications. It performs static analysis on application binaries and source code to detect vulnerabilities. It also includes dynamic analysis capabilities like monitoring network traffic, system calls and application data during runtime. The tool is hosted locally and does not send any data to the cloud. The talk demonstrates the tool's static and dynamic analysis features and provides examples of vulnerabilities it has discovered in real world applications. Future plans are discussed to add additional testing capabilities and improve the tool. Users are encouraged to download, test and contribute to the open source project.

Mobile Security Framework is an open source automated mobile application testing framework capable of static and dynamic analysis of Android and iOS apps. It performs various analyses including permission analysis, API monitoring, and HTTP traffic analysis. The framework supports analyzing APK, IPA, and source code. It is available on GitHub and includes demos of static analysis on sample APK and IPA files. The presentation also describes a mobile security CTF challenge involving two Android apps, GETSECRET and SENDSECRET, that can be solved by analyzing the apps with Mobile Security Framework or other reversing techniques.

Keynote for H2O first Community Event for AI Open Source Cancer and Open Source Health Data. - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

This document discusses bronchoscopy procedures and areas for improvement. Bronchoscopy involves inserting a viewing tube into the lungs to evaluate and treat lung diseases. It allows diagnosis but not direct viewing of lung tissue. The procedures can involve biopsy or bronchial washing to extract samples. Improvement areas include more ergonomic and lightweight x-ray protection, smaller endoscopes, reusable tools, and efficient sterilization methods. A proposed solution is an electromagnetic system that moves a probe in the lungs guided by preoperative and intraoperative images for better navigation and smaller, more maneuverable tools.

This presentation was made on June 16, 2020. A recording of the presentation can be viewed here: https://youtu.be/khjW1t0gtSA AI is unlocking new potential for every enterprise. Organizations are using AI and machine learning technology to inform business decisions, predict potential issues, and provide more efficient, customized customer experiences. The results can enable a competitive edge for the business. H2O.ai is a visionary leader in AI and machine learning and is on a mission to democratize AI for everyone. We believe that every company can become an AI company, not just the AI Superpowers. We are empowering companies with our leading AI and Machine Learning platforms, our expertise, experience and training to embark on their own AI journey to become AI companies themselves. All companies in all industries can participate in this AI Transformation. Tune into this virtual meetup to learn how companies are transforming their business with the power of AI and where to start. About Parul Pandey: Parul is a Data Science Evangelist here at H2O.ai. She combines Data Science , evangelism and community in her work. Her emphasis is to spread the information about H2O and Driverless AI to as many people as possible, She is also an active writer and has contributed towards various national and international publications.

This document discusses the role of data, algorithms, and people in driving transformation. It emphasizes that code and software are changing the world, and that data ecosystems and alliances will be important going forward. Open source is presented as a way to defend community through code and products. The document also stresses the importance of building ecosystems rather than just products, treating data science as a team sport, and using storytelling in conjunction with data.

“AGI should be open source and in the public domain at the service of humanity and the planet.”

This document discusses the future of enterprise platforms as a service (PaaS). It notes that profound changes in areas like big data, cloud, social, and mobile technologies are transforming business models. The document advocates rethinking the future to optimize resources, use big data to advantage, and run businesses in real-time. It presents SAP's HANA platform and PaaS strategy, including SAP's expectations and contributions in supporting the Cloud Foundry open source project to help create an industry-wide ecosystem for deploying applications.

Pivotal transforms how organizations build software through a focus on customer outcomes, developer productivity, and operating across any cloud or infrastructure. It provides a platform that enables continuous learning, discipline and open critique, and partnerships with major cloud providers. Enterprises across industries are transforming their software development with Pivotal's approach. Orange France developed a new application on Pivotal Cloud Foundry in 1/3 the usual time by continuously iterating based on user feedback.

발표영상 다시보기: https://youtu.be/bYAwozhvg6k Amazon에서는 제품 개발 단계에서 고유의 혁신 메커니즘인 Working Backward(거꾸로 일하기)라는 문화를 가지고 있습니다. 아마존의 제품 개발 방식을 배우려는 AWS 고객사들을 위한 워크샵 및 교육을 통해 어떻게 이러한 문화를 실제로 도입하는지 살펴 보고, 이를 직접 상품 기획 등에 실제 적용한 고객 사례를 소개해드립니다.

Watch: https://bit.ly/327z8UM While the impact of COVID-19 is uniform across organisations in the region, a lot of how the organisation can recover from the impact and strive in the market would depend on their resiliency and business agility. An organisation’s data management strategy holds the key, as they tackle the challenges of siloed data sources, optimising for operational stability, and ensuring real time delivery of consistent and reliable information, irrespective of the data source or format. Join this session to hear why large organisations are implementing Data Virtualization, a modern data integration approach in their data architecture to build resiliency, enhance business agility, and save costs. In this session, you will learn: - How to deliver clear strategy for agile data delivery across the enterprise without pains of traditional data integration - How to provide a robust yet simple architecture for data governance, master data, data trust, data privacy and data access security implementation - all from single unified framework - How to deploy digital transformation initiatives for Agile BI, Big Data, Enterprise Data Services & Data Governance

This document discusses how Perficient, an IT consulting firm, can help clients integrate big data into their organizations at lower total costs. It provides an overview of Perficient's services and solutions expertise in areas like business intelligence, customer experience, enterprise resource planning, and mobile platforms. The document also profiles Perficient with details on its history, locations, colleagues, and partnership model. Finally, it outlines an agenda for an event on balancing innovation and costs with big data, including discussions on PowerCenter Big Data Edition and what customers are doing with Informatica and big data.

Chet Kapoor's opening keynote address at I Love APIs London 2016. Like the three industrial revolutions before it, the fourth brings technology advances and culture change as people adapt to live and work in new ways. The promise is huge and the need to move fast and adapt quickly to change is paramount.

Experience and commerce interweave to drive results for today's leading brands. See what Adobe has to say about this exciting acquisition.

This document provides summaries of various topics related to SAP Leonardo and emerging technologies. It discusses how SAP Leonardo can help organizations optimize their processes using intelligent technologies like the Internet of Things, machine learning, analytics, blockchain, and big data. Specific examples are provided of how these technologies have been applied in industries like utilities, industrial machinery, and public sector to improve asset management, increase stability and throughput, and better understand issues like drug abuse crises. The benefits of SAP Leonardo to organizations include integrating emerging technologies, assessing equipment reliability, improving shipment tracking, and adding new technologies to help businesses stay competitive.

The innovation provided by the Cloud Foundry community aligns very well with innovation occurring inside SAP, and both are gaining significant market momentum. Learn about SAP’s involvement with Cloud Foundry, its PaaS strategy built on SAP HANA Cloud Platform, and its commitment to the open source approach overall, in this 2014 Cloud Foundry Summit presentation by Dirk Basenach and Steve Winkler.

Software is changing the way traditional business operate. People now have smartphones in their pockets - a supercomputer that is 25,000 times more powerful and the minicomputers of the 1960s. This is changing people's behaviour and how people shop and use services. The organisational structure created in the 20th century cannot survive when new digital solution are being offered. Software is changing the way traditional business operate. People now have smartphones in their pockets - a supercomputer that is 25,000 times more powerful and the minicomputers of the 1960s. This is changing people's behaviour and how people shop and use services. The organisational structure created in the 20th century cannot survive when new digital solution are being offered. The hierarchical structure of these established companies assumes high coordination cost due to human activity. But when the coordination cost drops The organisational structure that companies in the 20th century established was based on the fact that employees needed to do all the work. The coordination cost was high due to the effort and cost of employees, housing etc. Now we have software that can do this for use and the coordination cost drops to close-to-zero. Another thing is that things become free. Consider Flickr. Anybody can sign up and use the service for free. Only a fraction of the users get pro account and pay. How can Flickr make money on that? It turns out that services like this can. Many businesses make money by giving things away. How can that possibly work? The music business has suffered severely with digital distribution of content. Should musicians put all their songs on YouTube? What is the future business model for music?

This document provides information about David Cutler and his business consulting services. It summarizes his expertise in areas like marketing, technology, innovation delivery, and digital transformation. It also lists clients he has worked with, partnerships, and details of the services offered, such as ideation, prototyping, development, and scaling of digital products and solutions.