The document outlines the agenda for a Global AI Night event hosted by Microsoft. The event includes a welcome and keynote, followed by group sessions on using AI in Azure. There are beginner and intermediate tracks on topics like computer vision, machine learning, and deep learning. Speakers include representatives from Microsoft and SafeNet Consulting who will discuss leveraging Azure services and tools to build, train, and deploy AI models across devices and platforms.
Microsoft is working hard to make Artificial Intelligence available to everyone. We not only infuse AI in our products but also give you the platform to build your very own solution, that you are a developer, a citizen data scientist or a hard core data scientist.
The success of any organization in adopting AI to solve real-world problems is dependent on how we empower every developer to be productive using a comprehensive set of AI services, tools and infrastructure. Developers can build intelligent apps of the future by insusing AI, that delivers a unique, differentiated and personalized experience. In this demo and code heavy session, we will demonstrate how easy it is for every developers (without deep AI expertise) to build intelligence into their apps.
Machine Learning and Data Science are the hot technologies everyone is chasing this year, but sharing Machine Learning solutions is more complicated than simple source control. How do you share the process that allowed you to arrive at your solution? How do you effectively communicate between Data Scientists and Developers? How do I make it pretty so that I can present the work to non-technical stakeholders?
This talk answers these questions using Azure Notebooks. We will walk through a real example of a Jupyter Notebook, its features and I how I created it. The topics covered include:
• What are Azure Notebooks? How do they fit into the Azure Ecosystem?
• What is Jupyter? What are it's strengths and weaknesses?
• Mixing code snippets and execution results
• Data Visualization for presentation and analysis
• Markdown for exposition and formatting
• Sharing and Source Control
You'll leave with an understanding of Jupyter and Azure Notebooks and understand how to apply Azure Notebooks to real-world problems.
TARGET AUDIENCE: Developers, Architects, Business Analysts, Data Scientists, Data Developers
Tour de France Azure PaaS 6/7 Ajouter de l'intelligenceAlex Danvy
Nous assisterons probablement à une rupture générationnelle entre les apps avec de l'intelligence artificielle et celles sans. Ces dernières, comme les applications en mode caractères à l'arrivée des interfaces graphiques, auront du mal à perdurer.
Azure met à dispositions 3 approches pour ajouter de l'IA dans une app, avec un niveau de difficulté graduel, de l'outil ne nécessitant aucune compétence particulière à celui dédié aux Data Scientistes.
Microsoft has publicly committed $50 million over 5 years for artificial intelligence projects that support clean water, agriculture, climate, and biodiversity. Join us to learn about APIs that could literally change the way society monitors, models, and ultimately manages Earth’s life support systems.
Meg Mude, Intel - Data Engineering Lifecycle Optimized on Intel - H2O World S...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/cnU6sqd31JU
Developing meaningful AI applications requires complete data lifecycle management. Sourcing, harvesting, labelling and ensuring the conduit to consume data structures and repositories is critical for model accuracy....but, one of the least talked about subjects. Intel’s optimized technologies enable efficient delivery of complete data samples to develop (and deploy) meaningful outcomes. During this session, we’ll review the considerations and criticality of data lifecycle management for the AI production pipeline.
Bio: Meg brings more than 17 years of global product, engineering and solutions experience. She is presently a Solutions Architect with Intel Corporation specializing in Visual Compute and AAI (Analytics and AI) Architecture. She is passionate about the potential for technology to improve the quality of peoples’ lives and humanity on the whole.
Today we’re seeing revolutionary changes in hardware and software that are democratizing machine learning (ML) and making it accessible to any developer or data scientist. Whether you’re new to ML or you’re already an expert, Google Cloud has a variety of tools to help you. Learn the options available and how they support the full machine learning lifecycle for both realtime and batch data.
Introduction to Power Apps for DevelopersTaiki Yoshida
Presentation on introduction to Microsoft Power Apps for developers who doesn't believe in low-code.
This was presented in Glug Tokyo on 17th March 2020.
Microsoft is working hard to make Artificial Intelligence available to everyone. We not only infuse AI in our products but also give you the platform to build your very own solution, that you are a developer, a citizen data scientist or a hard core data scientist.
The success of any organization in adopting AI to solve real-world problems is dependent on how we empower every developer to be productive using a comprehensive set of AI services, tools and infrastructure. Developers can build intelligent apps of the future by insusing AI, that delivers a unique, differentiated and personalized experience. In this demo and code heavy session, we will demonstrate how easy it is for every developers (without deep AI expertise) to build intelligence into their apps.
Machine Learning and Data Science are the hot technologies everyone is chasing this year, but sharing Machine Learning solutions is more complicated than simple source control. How do you share the process that allowed you to arrive at your solution? How do you effectively communicate between Data Scientists and Developers? How do I make it pretty so that I can present the work to non-technical stakeholders?
This talk answers these questions using Azure Notebooks. We will walk through a real example of a Jupyter Notebook, its features and I how I created it. The topics covered include:
• What are Azure Notebooks? How do they fit into the Azure Ecosystem?
• What is Jupyter? What are it's strengths and weaknesses?
• Mixing code snippets and execution results
• Data Visualization for presentation and analysis
• Markdown for exposition and formatting
• Sharing and Source Control
You'll leave with an understanding of Jupyter and Azure Notebooks and understand how to apply Azure Notebooks to real-world problems.
TARGET AUDIENCE: Developers, Architects, Business Analysts, Data Scientists, Data Developers
Tour de France Azure PaaS 6/7 Ajouter de l'intelligenceAlex Danvy
Nous assisterons probablement à une rupture générationnelle entre les apps avec de l'intelligence artificielle et celles sans. Ces dernières, comme les applications en mode caractères à l'arrivée des interfaces graphiques, auront du mal à perdurer.
Azure met à dispositions 3 approches pour ajouter de l'IA dans une app, avec un niveau de difficulté graduel, de l'outil ne nécessitant aucune compétence particulière à celui dédié aux Data Scientistes.
Microsoft has publicly committed $50 million over 5 years for artificial intelligence projects that support clean water, agriculture, climate, and biodiversity. Join us to learn about APIs that could literally change the way society monitors, models, and ultimately manages Earth’s life support systems.
Meg Mude, Intel - Data Engineering Lifecycle Optimized on Intel - H2O World S...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/cnU6sqd31JU
Developing meaningful AI applications requires complete data lifecycle management. Sourcing, harvesting, labelling and ensuring the conduit to consume data structures and repositories is critical for model accuracy....but, one of the least talked about subjects. Intel’s optimized technologies enable efficient delivery of complete data samples to develop (and deploy) meaningful outcomes. During this session, we’ll review the considerations and criticality of data lifecycle management for the AI production pipeline.
Bio: Meg brings more than 17 years of global product, engineering and solutions experience. She is presently a Solutions Architect with Intel Corporation specializing in Visual Compute and AAI (Analytics and AI) Architecture. She is passionate about the potential for technology to improve the quality of peoples’ lives and humanity on the whole.
Today we’re seeing revolutionary changes in hardware and software that are democratizing machine learning (ML) and making it accessible to any developer or data scientist. Whether you’re new to ML or you’re already an expert, Google Cloud has a variety of tools to help you. Learn the options available and how they support the full machine learning lifecycle for both realtime and batch data.
Introduction to Power Apps for DevelopersTaiki Yoshida
Presentation on introduction to Microsoft Power Apps for developers who doesn't believe in low-code.
This was presented in Glug Tokyo on 17th March 2020.
Forecast 2012: Rapid Fire Panel #5: Cloud Enterprise Best Practices. Moderator: Ben Kepes, Analyst, Diversity
How Do We Chart a Path to “Best Practice” Cloud Implementation?
Dealing with the cultural issues
Open vs Closed
Working out a coherent strategy to move to the cloud
Management and monitoring for a cloudy world
Cloud integration – delivering consistency to end users
How do we know when we get there?
(Microsoft Azure Certification Training: https://www.edureka.co/microsoft-azur...)
This Edureka "Azure Machine Learning” tutorial will give you a thorough and insightful overview of Microsoft Azure ML Studio and also help you understand the fundamentals of machine learning.
Following are the offering of this tutorial:
1. What Is Machine Learning
2. Machine Learning Fundamentals
3. Azure ML Studio
4. Demo: Building a model with Azure ML Studio
Check out our Playlists: https://goo.gl/A1CJjM
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
TechKnow Fiesta 2021 - Powered by Amazon Web Service in September 2021TechknowFiesta
The Data Tech Labs understand the key to any organization's success story is employee engagement. Presenting TechKnow Fiesta 2021– Skill up to Scale Up with AWS
H2O Machine Learning with KNIME Analytics Platform - Christian Dietz - H2O AI...Sri Ambati
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.
Cloud computing is changing the way we access and consume computing resource. The purpose of this presentation is to impress upon the audience that cloud computing is not something "isolated" and "esoteric" affecting only some people or some companies or some industries. Cloud computing is a plenary resource
Cloud computing is a phenomenon affecting all of us and more importantly Benefiting each one of us .This presentation will show this with illustrations
In this talk we will share the idea of developing self guiding application that would provide the most engaging user experience possible using crowd sourced knowledge on a mobile interface. We will discuss and share how historical usage data could be mined using machine learning to identify application usage patterns to generate probable next actions. #h2ony
- 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
Introducción al Machine Learning AutomáticoSri Ambati
¿Cómo puede llevar el aprendizaje automático a las masas? Los proyectos de Machine Learning con la búsqueda de talento, el tiempo para construir e implementar modelos y confiar en los modelos que se construyen.
¿Cómo puede tener varios equipos en su organización para crear modelos de ML precisos sin ser expertos en ciencia de datos o aprendizaje automático?
¿Se pregunta sobre los diferentes sabores de AutoML?
H2O Driverless AI emplea las técnicas de científicos expertos en datos en una aplicación fácil de usar que ayuda a escalar sus esfuerzos de ciencia de datos. La inteligencia artificial Driverless permite a los científicos de datos trabajar en proyectos más rápido utilizando la automatización y la potencia de computación de vanguardia de las GPU para realizar tareas en minutos que solían tomar meses.
Con H2O Driverless AI, todos, incluyendo expertos y científicos de datos junior, científicos de dominio e ingenieros de datos pueden desarrollar modelos confiables de aprendizaje automático. Esta plataforma de aprendizaje automático de última generación ofrece una funcionalidad única y avanzada para la visualización de datos, la ingeniería de características, la interpretabilidad del modelo y la implementación de baja latencia.
H2O Driverless AI hace:
* Visualización automática de datos
* Ingeniería automática de funciones a nivel de Grandmaster
* Selección automática del modelo
* Ajuste y capacitación automáticos del modelo
* Paralelización automática utilizando múltiples CPU o GPU
* Ensamblaje automático del modelo
*automática del Interpretaciónaprendizaje automático (MLI)
* Generación automática de código de puntuación
¿Quieres probarlo tú mismo? Puede obtener una prueba gratuita aquí: H2O Driverless AI trial.
Venga a esta sesión y descubra cómo comenzar con el Aprendizaje automático automático con AI sin conductor H2O, y cree modelos potentes con solo unos pocos clics.
¡Te veo pronto!
Acerca de H2O.ai
H2O.ai es una empresa visionaria de software de código abierto de Silicon Valley que creó y reimaginó lo que es posible. Somos una empresa de fabricantes que trajeron al mercado nuevas plataformas y tecnologías para impulsar el movimiento de inteligencia artificial. Somos los creadores de, H2O, la principal plataforma de aprendizaje de ciencia de datos de fuente abierta y de aprendizaje automático utilizada por casi la mitad de Fortune 500 y en la que confían más de 14,000 organizaciones y cientos de miles de científicos de datos de todo el mundo.
When Face-to-Face Training Isn't An Option: 7 Tips for Remote Online TrainingCloudShare
Watch the webinar at this link: https://www.cloudshare.com/when-face-to-face-training-isnt-an-option-7-tips-remote-training
Learn 7 ways to continue being productive and ensure business ROI for virtual remote training in times of uncertainty, such as the coronavirus outbreak or others when travel and face to face meetings can't take place.
Microsoft Azure - Planning your move to the cloudScott Cameron
Cloud computing trends and drivers and how IaaS, PaaS and SaaS address business needs, allow organizations to scale quickly and flexibly and how Microsoft does "Cloud."
Near realtime AI deployment with huge data and super low latency - Levi Brack...Sri Ambati
Published on Nov 2, 2018
This talk was recorded in London on October 30th, 2018 and can be viewed here: https://youtu.be/erHt-1yBuUw
Session: Travelport is a leading travel commerce platform that has truly huge data and many complex needs in terms of processing, performance and latency. This talk will demonstrate how we were able to harness big data technologies, H2O and cloud integration to deploy AI at scale and at low latency. The talk to cover practical advice taken from our AI journey; you will learn the successful strategies and the pitfalls of near real-time retraining ML models with streaming data and using all opensource technologies.
Bio: As principal data scientist at Travelport, Levi Brackman leads a team of data scientists that are putting ML model into production. Prior to Travelport, Levi spent most of his career in the start-up world. He founded and led an organization that created innovative educational software applications and solutions used by high schools and youth organizations in the USA and Australia. Levi earned a PhD in the quantitative social sciences under the supervision of one the world's leading educational psychologists. He earned master’s degree from University College London and is author of a business book published in eight languages that was a bestseller in multiple countries. A native of North London (UK) Levi is married and has five children and now lives in Broomfield, Colorado.
Krish Swamy + Balaji Gopalakrishnan, Wells Fargo - Building a World Class Dat...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/VAW2eDht7JA
Bio: Krish Swamy is an experienced professional with deep skills in applying analytics and BigData capabilities to challenging business problems and driving customer insights. Krish's analytic experience includes marketing and pricing, credit risk, digital analytics and most recently, big data analytics and data transformation. His key experiences lie in banking and financial services, the digital customer experience domain, with a background in management consulting. Other key skills include influencing organizational change towards a data and analytics driven culture, and building teams of analytics, statisticians and data scientists.
Bio: Balaji Gopalakrishnan has over 15 years experience in the Machine Learning and Data Science space. Balaji has led cross functional data science and engineering teams for developing cutting-edge machine learning and cognitive computing capabilities for insurance fraud and underwriting, telematics, multi-asset class risk, scheduling under uncertainty, and others. He is passionate about driving AI adoption in organizations and strongly believes in the power of cross functional collaboration for this purpose.
Virtualization to Cloud Evolution “IT as a Service”Mohamed Shorbagy
The presentation is about how to start your career in the Cloud covering the process of going from physical to virtual to Cloud and finish with the definitions of what is a Cloud. So, if you interested to be a part of this field and take your first steps towards it, you are welcome in the "Inevitable Cloud" community,Join us on:
http://www.facebook.com/TheInevitableCloud
http://www.facebook.com/groups/InevitableCloud/
http://www.linkedin.com/company/2990722?trk=tyah
Thanks,
Inevitable Cloud Community
M365VM - Project Cortex: AI Powered Knowledge Network for the EnterpriseJoel Oleson
Project Cortex: AI Powered Knowledge Network
What is Project Cortex? In this session we’ll deconstruct the new Microsoft Knowledge Network and dive into new demos just published by Microsoft. The more you understand AI the more you can understand the power of machine learning and machine teaching for business process automation for tagging, workstreams, digital transformation unlocking new scenarios never before available. AI for the masses. Democratizing AI. AI for the people!
Introduction to the cloud native computing foundationJayesh Sharma
Introducing the cloud native approach to development, containers, and the microservices architecture.
Also covered:
✅ What is CNCF?
✅ Prominent projects under CNCF.
✅ How to contribute? ⭐
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit-baidu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Dr. Ren Wu, former distinguished scientist at Baidu's Institute of Deep Learning (IDL), presents the keynote talk, "Enabling Ubiquitous Visual Intelligence Through Deep Learning," at the May 2015 Embedded Vision Summit.
Deep learning techniques have been making headlines lately in computer vision research. Using techniques inspired by the human brain, deep learning employs massive replication of simple algorithms which learn to distinguish objects through training on vast numbers of examples. Neural networks trained in this way are gaining the ability to recognize objects as accurately as humans.
Some experts believe that deep learning will transform the field of vision, enabling the widespread deployment of visual intelligence in many types of systems and applications. But there are many practical problems to be solved before this goal can be reached. For example, how can we create the massive sets of real-world images required to train neural networks? And given their massive computational requirements, how can we deploy neural networks into applications like mobile and wearable devices with tight cost and power consumption constraints?
In this talk, Ren shares an insider’s perspective on these and other critical questions related to the practical use of neural networks for vision, based on the pioneering work being conducted by his former team at Baidu.
Note 1: Regarding the ImageNet results included in this presentation, the organizers of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) have said: “Because of the violation of the regulations of the test server, these results may not be directly comparable to results obtained and reported by other teams.” (http://www.image-net.org/challenges/LSVRC/announcement-June-2-2015)
Note 2: The presenter, Ren Wu, has told the Embedded Vision Alliance that “There was some ambiguity with the rules. According to the ‘official’ interpretation of the rules, there should be no more than 52 submissions within a half year. For us, we achieved the reported results after 200 tests total within a half year. We believe there is no way to obtain any measurable gains, nor did we try to obtain any gains, from an 'extra' hundred tests as our networks have billions of parameters and are trained by tens of billions of training samples.”
In this opportunity I spoke for almost 4 hours -with a lunch in between- about the analytics solutions on azure and it's tool for machine learning and cognitive services. I introduced the automated machine learning on Azure with some demos in real time.
Forecast 2012: Rapid Fire Panel #5: Cloud Enterprise Best Practices. Moderator: Ben Kepes, Analyst, Diversity
How Do We Chart a Path to “Best Practice” Cloud Implementation?
Dealing with the cultural issues
Open vs Closed
Working out a coherent strategy to move to the cloud
Management and monitoring for a cloudy world
Cloud integration – delivering consistency to end users
How do we know when we get there?
(Microsoft Azure Certification Training: https://www.edureka.co/microsoft-azur...)
This Edureka "Azure Machine Learning” tutorial will give you a thorough and insightful overview of Microsoft Azure ML Studio and also help you understand the fundamentals of machine learning.
Following are the offering of this tutorial:
1. What Is Machine Learning
2. Machine Learning Fundamentals
3. Azure ML Studio
4. Demo: Building a model with Azure ML Studio
Check out our Playlists: https://goo.gl/A1CJjM
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
TechKnow Fiesta 2021 - Powered by Amazon Web Service in September 2021TechknowFiesta
The Data Tech Labs understand the key to any organization's success story is employee engagement. Presenting TechKnow Fiesta 2021– Skill up to Scale Up with AWS
H2O Machine Learning with KNIME Analytics Platform - Christian Dietz - H2O AI...Sri Ambati
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.
Cloud computing is changing the way we access and consume computing resource. The purpose of this presentation is to impress upon the audience that cloud computing is not something "isolated" and "esoteric" affecting only some people or some companies or some industries. Cloud computing is a plenary resource
Cloud computing is a phenomenon affecting all of us and more importantly Benefiting each one of us .This presentation will show this with illustrations
In this talk we will share the idea of developing self guiding application that would provide the most engaging user experience possible using crowd sourced knowledge on a mobile interface. We will discuss and share how historical usage data could be mined using machine learning to identify application usage patterns to generate probable next actions. #h2ony
- 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
Introducción al Machine Learning AutomáticoSri Ambati
¿Cómo puede llevar el aprendizaje automático a las masas? Los proyectos de Machine Learning con la búsqueda de talento, el tiempo para construir e implementar modelos y confiar en los modelos que se construyen.
¿Cómo puede tener varios equipos en su organización para crear modelos de ML precisos sin ser expertos en ciencia de datos o aprendizaje automático?
¿Se pregunta sobre los diferentes sabores de AutoML?
H2O Driverless AI emplea las técnicas de científicos expertos en datos en una aplicación fácil de usar que ayuda a escalar sus esfuerzos de ciencia de datos. La inteligencia artificial Driverless permite a los científicos de datos trabajar en proyectos más rápido utilizando la automatización y la potencia de computación de vanguardia de las GPU para realizar tareas en minutos que solían tomar meses.
Con H2O Driverless AI, todos, incluyendo expertos y científicos de datos junior, científicos de dominio e ingenieros de datos pueden desarrollar modelos confiables de aprendizaje automático. Esta plataforma de aprendizaje automático de última generación ofrece una funcionalidad única y avanzada para la visualización de datos, la ingeniería de características, la interpretabilidad del modelo y la implementación de baja latencia.
H2O Driverless AI hace:
* Visualización automática de datos
* Ingeniería automática de funciones a nivel de Grandmaster
* Selección automática del modelo
* Ajuste y capacitación automáticos del modelo
* Paralelización automática utilizando múltiples CPU o GPU
* Ensamblaje automático del modelo
*automática del Interpretaciónaprendizaje automático (MLI)
* Generación automática de código de puntuación
¿Quieres probarlo tú mismo? Puede obtener una prueba gratuita aquí: H2O Driverless AI trial.
Venga a esta sesión y descubra cómo comenzar con el Aprendizaje automático automático con AI sin conductor H2O, y cree modelos potentes con solo unos pocos clics.
¡Te veo pronto!
Acerca de H2O.ai
H2O.ai es una empresa visionaria de software de código abierto de Silicon Valley que creó y reimaginó lo que es posible. Somos una empresa de fabricantes que trajeron al mercado nuevas plataformas y tecnologías para impulsar el movimiento de inteligencia artificial. Somos los creadores de, H2O, la principal plataforma de aprendizaje de ciencia de datos de fuente abierta y de aprendizaje automático utilizada por casi la mitad de Fortune 500 y en la que confían más de 14,000 organizaciones y cientos de miles de científicos de datos de todo el mundo.
When Face-to-Face Training Isn't An Option: 7 Tips for Remote Online TrainingCloudShare
Watch the webinar at this link: https://www.cloudshare.com/when-face-to-face-training-isnt-an-option-7-tips-remote-training
Learn 7 ways to continue being productive and ensure business ROI for virtual remote training in times of uncertainty, such as the coronavirus outbreak or others when travel and face to face meetings can't take place.
Microsoft Azure - Planning your move to the cloudScott Cameron
Cloud computing trends and drivers and how IaaS, PaaS and SaaS address business needs, allow organizations to scale quickly and flexibly and how Microsoft does "Cloud."
Near realtime AI deployment with huge data and super low latency - Levi Brack...Sri Ambati
Published on Nov 2, 2018
This talk was recorded in London on October 30th, 2018 and can be viewed here: https://youtu.be/erHt-1yBuUw
Session: Travelport is a leading travel commerce platform that has truly huge data and many complex needs in terms of processing, performance and latency. This talk will demonstrate how we were able to harness big data technologies, H2O and cloud integration to deploy AI at scale and at low latency. The talk to cover practical advice taken from our AI journey; you will learn the successful strategies and the pitfalls of near real-time retraining ML models with streaming data and using all opensource technologies.
Bio: As principal data scientist at Travelport, Levi Brackman leads a team of data scientists that are putting ML model into production. Prior to Travelport, Levi spent most of his career in the start-up world. He founded and led an organization that created innovative educational software applications and solutions used by high schools and youth organizations in the USA and Australia. Levi earned a PhD in the quantitative social sciences under the supervision of one the world's leading educational psychologists. He earned master’s degree from University College London and is author of a business book published in eight languages that was a bestseller in multiple countries. A native of North London (UK) Levi is married and has five children and now lives in Broomfield, Colorado.
Krish Swamy + Balaji Gopalakrishnan, Wells Fargo - Building a World Class Dat...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/VAW2eDht7JA
Bio: Krish Swamy is an experienced professional with deep skills in applying analytics and BigData capabilities to challenging business problems and driving customer insights. Krish's analytic experience includes marketing and pricing, credit risk, digital analytics and most recently, big data analytics and data transformation. His key experiences lie in banking and financial services, the digital customer experience domain, with a background in management consulting. Other key skills include influencing organizational change towards a data and analytics driven culture, and building teams of analytics, statisticians and data scientists.
Bio: Balaji Gopalakrishnan has over 15 years experience in the Machine Learning and Data Science space. Balaji has led cross functional data science and engineering teams for developing cutting-edge machine learning and cognitive computing capabilities for insurance fraud and underwriting, telematics, multi-asset class risk, scheduling under uncertainty, and others. He is passionate about driving AI adoption in organizations and strongly believes in the power of cross functional collaboration for this purpose.
Virtualization to Cloud Evolution “IT as a Service”Mohamed Shorbagy
The presentation is about how to start your career in the Cloud covering the process of going from physical to virtual to Cloud and finish with the definitions of what is a Cloud. So, if you interested to be a part of this field and take your first steps towards it, you are welcome in the "Inevitable Cloud" community,Join us on:
http://www.facebook.com/TheInevitableCloud
http://www.facebook.com/groups/InevitableCloud/
http://www.linkedin.com/company/2990722?trk=tyah
Thanks,
Inevitable Cloud Community
M365VM - Project Cortex: AI Powered Knowledge Network for the EnterpriseJoel Oleson
Project Cortex: AI Powered Knowledge Network
What is Project Cortex? In this session we’ll deconstruct the new Microsoft Knowledge Network and dive into new demos just published by Microsoft. The more you understand AI the more you can understand the power of machine learning and machine teaching for business process automation for tagging, workstreams, digital transformation unlocking new scenarios never before available. AI for the masses. Democratizing AI. AI for the people!
Introduction to the cloud native computing foundationJayesh Sharma
Introducing the cloud native approach to development, containers, and the microservices architecture.
Also covered:
✅ What is CNCF?
✅ Prominent projects under CNCF.
✅ How to contribute? ⭐
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit-baidu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Dr. Ren Wu, former distinguished scientist at Baidu's Institute of Deep Learning (IDL), presents the keynote talk, "Enabling Ubiquitous Visual Intelligence Through Deep Learning," at the May 2015 Embedded Vision Summit.
Deep learning techniques have been making headlines lately in computer vision research. Using techniques inspired by the human brain, deep learning employs massive replication of simple algorithms which learn to distinguish objects through training on vast numbers of examples. Neural networks trained in this way are gaining the ability to recognize objects as accurately as humans.
Some experts believe that deep learning will transform the field of vision, enabling the widespread deployment of visual intelligence in many types of systems and applications. But there are many practical problems to be solved before this goal can be reached. For example, how can we create the massive sets of real-world images required to train neural networks? And given their massive computational requirements, how can we deploy neural networks into applications like mobile and wearable devices with tight cost and power consumption constraints?
In this talk, Ren shares an insider’s perspective on these and other critical questions related to the practical use of neural networks for vision, based on the pioneering work being conducted by his former team at Baidu.
Note 1: Regarding the ImageNet results included in this presentation, the organizers of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) have said: “Because of the violation of the regulations of the test server, these results may not be directly comparable to results obtained and reported by other teams.” (http://www.image-net.org/challenges/LSVRC/announcement-June-2-2015)
Note 2: The presenter, Ren Wu, has told the Embedded Vision Alliance that “There was some ambiguity with the rules. According to the ‘official’ interpretation of the rules, there should be no more than 52 submissions within a half year. For us, we achieved the reported results after 200 tests total within a half year. We believe there is no way to obtain any measurable gains, nor did we try to obtain any gains, from an 'extra' hundred tests as our networks have billions of parameters and are trained by tens of billions of training samples.”
In this opportunity I spoke for almost 4 hours -with a lunch in between- about the analytics solutions on azure and it's tool for machine learning and cognitive services. I introduced the automated machine learning on Azure with some demos in real time.
Big Data Advanced Analytics on Microsoft Azure 201904Mark Tabladillo
This talk summarizes key points for big data advanced analytics on Microsoft Azure. First, there is a review of the major technologies. Second, there is a series of technology demos (focusing on VMs, Databricks and Azure ML Service). Third, there is some advice on using the Team Data Science Process to help plan projects. The deck has web resources recommended. This presentation was delivered at the Global Azure Bootcamp 2019, Atlanta GA location (Alpharetta Avalon).
Introduction to Machine learning and Deep LearningNishan Aryal
Overview of Machine Learning and Deep Learning. Brief introduction to different types of BI Reporting tools like Power BI, SSMS, Cortana, Azure ML, TenserFlow and other tools.
Meetup Toulouse Microsoft Azure : Bâtir une solution IoTAlex Danvy
Un tour d'horizon des solutions disponibles chez Microsoft pour bâtir une solution IoT. Il est question de Microsoft Azure bien-sûr, mais pas seulement. Windows, Machine Learning, Bots, OCF/AllJoyn, Hololens
How Azure helps to build better business processes and customer experiences w...Maxim Salnikov
Artificial Intelligence is not the future, it is NOW. Cloud technology empowers developers and technology leaders to benefit from AI effectively and responsibly with the models and tools they need. In this session, we go through the portfolio of Azure AI services and run some demos to showcase how AI can improve daily life, safety, productivity, accessibility, and business outcomes.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
Join Joseph Sirosh, Corporate Vice President of the Cloud AI Platform, for a deep dive into the AI platform and exciting AI use cases. Joseph will showcase how every developer can infuse intelligence into their applications and create amazing new experiences with AI. In this exciting overview, you will learn about the application of AI technologies in the cloud. We will help you understand how to add pre-built AI capabilities like object detection, face understanding, translation and speech to applications. We will show how developers can build Cognitive Search applications that understand deep content in images, text and other data. We will also show how the platform can be used to build your own custom AI models for predictive applications and how to use the Azure platform to accelerate machine learning. Joseph will also show how companies assemble end-to-end systems of intelligence using the rich variety of data and application development services on Azure.
Bay Area Azure Meetup - Ignite update sessionNills Franssens
Slidedeck used for the Bay Area Azure Meetup. Microsoft released a ton of new services and updates at Ignite in September. Let’s take some time together to walk through a highlight of the updates and new services announced. We will start by going over the updates in the infrastructure and applications space – and finish off the evening with the novelties in the data and AI area.
ChatGPT and not only: how can you use the power of Generative AI at scaleMaxim Salnikov
Join this session to get all the answers about how ChatGPT and other LLM models can be applied to your current or future project. We'll start by putting in order all the terms - OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc. - and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
Advanced Analytics and Artificial Intelligence - Transforming Your Business T...David J Rosenthal
Recent advances in AI have incredible potential and they are already fundamentally changing our lives in ways we couldn’t have imagined even five years ago. And yet, AI is also probably one of the least understood technological breakthroughs in modern times. Come to this event to learn about breakthrough advances in AI and the power of the cloud, and how Microsoft provides a flexible platform for you to infuse intelligence into your own products and services. Microsoft empowers you to transform your business, uniquely combining AI innovation with a proven Enterprise platform, deriving intelligence from a wide range of data relevant to your business no matter where it lives.
Similar to Global ai night sept 2019 - Milwaukee (20)
Why do most machine learning projects never make it to productionCameron Vetter
Machine Learning is quickly becoming a ubiquitous technology and expected skill of development teams. In 2019 a stunning 87% of Data Science projects never made it into production. What will you do to make sure that your ML and Data Science projects succeed?
This talk will focused on digging deeper into that static and help to explain what goes wrong, why it goes wrong, and what I do to mitigate these issues.
You’ll leave with a deep understanding of ML project failures, and some advice on how to improve your projects chance of success. You also will learn how to fail quickly in ML model development and pivot towards a path of success.
Ml.net machine learning for .net developers!Cameron Vetter
Machine Learning is quickly becoming a ubiquitous technology and expected skill of development teams. Python has dominated this space with all of the best libraries and tooling. As a .NET developer, you need to not only understand the terminology and techniques, but also learn a new language. ML.NET provides an alternative allowing you to do some Machine Learning in C#.
This talk will be code heavy, focused on showing as many demos as possible showing how these tools can be used effectively to bring Machine Learning to your .NET team. The topics covered will include:
• Brief machine learning terminology primer
• Primer on ML.NET and how it compares to other popular tools
• Demos of Classification, Regression, Deep Learning Image Classification, and Clustering
You'll leave with an understanding of ML.NET, how ML .NET can be used in your applications, and some exposure to the structure of working ML.NET solutions.
The cloud has become table stakes for modern software architecture. Why do we still architect as though we are targeting our own data centers and try to force it to fit into cloud infrastructure?
This talk will focus on patterns and antipatterns to architect for the cloud, using Microsoft Azure as an example. We will cover:
Architectural Patterns
Design Principals
Cloud Design Patterns
Best Practices
Performance Antipatterns
You’ll leave with an understanding of how to architect for the cloud, along with my recommendations on how to think cloud-first.
Mixed reality the second generation is all about uxCameron Vetter
Mixed Reality has made a big splash in the last few years with real products arriving from Microsoft and Magic Leap. The dust has settled, and the second generation of hardware and development tools from Microsoft are coming out this year. Where does this leave us? Join Cameron for an examination of the UX of HoloLens 2. In this talk, he will cover what has changed, what user interactions will look like for this hardware, and talk about the UX challenges that this platform will provide.
• A review of the HoloLens 1 as a baseline.
• An introduction to the HoloLens 2 and its features.
• A review of what companies are currently doing with HoloLens.
• Demos of the out-of-the-box UX in HoloLens 2, as well as the story of how they were researched and designed.
• The tools in use for designing Mixed Reality experiences.
• UX challenges for Mixed Reality applications.
• Recommendations on how you should prepare... UX will be pivotal to Mixed Reality's success!
You'll leave with an overview of the UX needs of this generation of Mixed Reality hardware, and an understanding of what adaptions will be needed to design user experiences in Mixed Reality.
Integrating Machine Learning Capabilities into your teamCameron Vetter
Machine Learning is here today and is quickly becoming an expected skill of development teams. As a technical leader on your team, you need to not only help your team learn how to do machine learning, but also select the right tools, integrate the tools into your tool chain, and understand how to deploy and version machine learning models.
This talk answers these questions using the Microsoft stack as an example. We will walk through my approach to integrating Machine Learning into a team. The topics covered include:
• Where to start, while minimizing investment and risk.
• The spectrum of tools from off the shelf to handcrafted.
• Packaging and deploying your model.
• Integrating your model into your system.
• Other considerations and risks.
You'll leave with my perspective on how to introduce a team to machine learning and how I recommend integrating machine learning into your software development toolkit.
TARGET AUDIENCE: Senior Developers, Architects, Technical Leaders
An Introduction to Artificial Neural NetworksCameron Vetter
Do you want to predict customer behavior? Evaluate the content of a photo or sound? Detect Fraud? Feed usage data back into your algorithms to improve them automatically? All of these things are being done today using Neural Networks for Machine Learning.
This talk will cover the technologies used to create Neural Networks and give an introduction to the basics of why they work, the different types, and how they are being applied to today's business problems. The topics covered include:
• Artificial Neural Networks
• Convolutional Neural Networks
• Self Organizing Maps
• Recurrent Neural Networks
• AutoEncoders
You'll leave with an understanding of Neural Network terminology and basic concepts, and understand how these neural networks can be applied to real world problems.
TARGET AUDIENCE: Anyone interested in driving innovation
Do you want to predict customer behavior? Evaluate the content of a photo or sound? Detect Fraud? Feed usage data back into your algorithms to improve them automatically? All of these things are being done today using Neural Networks for Machine Learning.
This talk will cover how to use the GPU power of Azure to train a Neural Network and how to turn that Neural Network into a REST service hosted in Azure. The topics covered include:
• Brief overview of Neural Networks
• Azure Batch AI
• Azure Data Science Virtual Machines
• Python in Azure Web Apps
You'll leave with an understanding of how to use Azure to train and host your neural networks.
Using a Service Bus for Microservice CommunicationCameron Vetter
Choosing a microservice architecture isn’t a silver bullet. With microservices you’ve exchanged the problems of monolithic applications for a new set of problems. One of the biggest problems is effective, concise communication between services without accidental coupling. In this talk I will show how a Service Bus can be used to solve this problem.
This talk will use Mass Transit, RabbitMQ, Azure Service Bus, and C# to cover:
• Service Bus Design Patterns
• Message Transports
• Why not REST?
• Eventual Consistency
• Scalability and Reliability
You’ll leave with an understanding of how to effectively use a Service Bus for Microservice communication to avoid tight coupling between services and see demos of these concepts.
Augmented Reality - Let’s Make Some Holgrams! (Developer Version)Cameron Vetter
Do you want to explore Mars? Model a building on your conference room table? Play Minecraft in your living room? All of these things are being done today using Augmented Reality. Augmented Reality has shifted from mobile app gimmick to an immersive UXD experience that is unrivaled.
This talk will use the HoloLens and Unity 3D to give a crash course in AR concepts and the code that makes them a reality. This includes:
Real World Understanding (Spatial Mapping, Plane Finding, Spatial Understanding)
Holograms (Billboarding, Placement, Physics)
Control (Gaze, Cursor, Gestures, Voice)
Persistence (World Anchors)
You'll leave with an understanding of HoloLens terminology and techniques, by watching a real HoloLens app get built step-by-step using each of these AR concepts.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
3. Agenda
6:00 pm - Welcome / Food
6:15 pm - Keynote
6:30 pm - Group Session
A lap around AI in Microsoft Azure
7:00 pm - Workshop Beginner Track 1
Creating applications that can see, hear, speak or understand
7:00 pm - Workshop Intermediate Track 1
Train machine learning models using automated machine learning
8:00 pm - Workshop Beginner Track 2
Is that wine good or bad?
8:00 pm - Workshop Intermediate Track 2 -
Crash course on building and accelerating deep learning solutions
4. Thank You to
our Sponsor!
SafeNet specializes in being partners in your
success. SafeNet currently focus on Custom
Application Development, Cloud Consulting
Services, and Data & Analytics.
5. Cameron Vetter
Principal Cloud Consultant
SafeNet Consulting
Software Development is my passion. I have 20 years of experience using
Microsoft tools and technologies to develop software. I have experience in
many roles including Development, Architecture, Infrastructure,
Management, and Leadership roles. I've worked for some of the largest
companies in the world and for small local companies getting a breadth of
experience in different Corporate Cultures. Currently, I am the Principal
Cloud Architect at SafeNet Consulting, where I get to do what I love...
Architect, Design, and Develop great software! I currently focus on
Microservices, SOA, Azure, Cognitive Toolkit, and Kubernetes.
6. Ryan Bennett
Managing Director
SafeNet Consulting
I have been a software engineer for 10 years and have worked as a
consultant for six. I have worked at nearly 20 clients building anything from
enterprise data warehouses to a startup app for filmmaking. I also am a
founder and instructor at a not-for-profit organization that teaches full-
stack web development to high school students during the summer.
7. Welcome from Microsoft
Join Henk Boelman, Amy Boyd, Seth Juarez, and Eric Boyd for a warm welcome
to the Global AI Night 2019, a dialogue about the latest and greatest from Azure
AI, interesting behind the scenes stories, and some exciting news ahead.
9. 3.9$ TGlobal business value derived
from AI in 2022 will reach
“Forecast: The Business Value of Artificial Intelligence, Worldwide, 2017-2025”, Gartner, April 2018.
Decision
support
Virtual
agents
Decision
automation
Smart
products
3.9$ T
10. Machine Learning on Azure
Domain specific pretrained models
To reduce time to market
Azure
Databricks
Machine
Learning VMs
Popular frameworks
To build advanced deep learning solutions
TensorFlowPytorch Onnx
Azure Machine
Learning
LanguageSpeech
…
SearchVision
Productive services
To empower data science and development teams
Powerful infrastructure
To accelerate deep learning
Scikit-Learn
PyCharm Jupyter
Familiar Data Science tools
To simplify model development
Visual Studio Code Command line
CPU GPU FPGA
From the Intelligent Cloud to the Intelligent Edge
11. Infuse apps with powerful, pre-trained AI models
Customize easily and tailor to your needs
Vision
Speech
Language
Bing
Search
…
Computer Vision | Video Indexer | Face | Content Moderator
Speech to Text | Text to Speech | Speech Translation | Speaker Recognition
Text Analytics | Spell Check | Language Understanding | Text Translation | QnA Maker
Big Web Search | Video Search | Image Search | Visual Search | Entity Search |
News Search | Autosuggest
12. Familiar Data Science tools
Choose any python development environment
And improve data science productivity
PyCharm Jupyter Visual Studio Code Command lineZeppelin
Interactive widgets for Jupyter Notebooks Azure Machine Learning for Visual Studio Code extension
13. Build advanced deep learning solutions
Use your favorite machine learning
frameworks
without getting locked into one framework
ONNX
Community project created by Facebook and Microsoft
Use the best tool for the job. Train in one framework
and transfer to another for inference
TensorFlow PyTorch Scikit-Learn
MXNet Chainer Keras
14. Frameworks Azure
Create Deploy
Services
Devices
Azure Machine Learning services
Ubuntu VM
Windows Server 2019 VM
Azure Custom Vision Service
ONNX Model
Windows devices
Other devices (iOS, etc.)
Announcing ONNX Runtime open source
15. +
To empower data science and development teams
Develop models faster with automated machine learning
Use any Python environment and ML frameworks
Manage models across the cloud and the edge.
Prepare data clean data at massive scale
Enable collaboration between data scientists and data engineers
Access machine learning optimized clusters
Azure Machine Learning
Python-based machine learning service
Azure Databricks
Apache Spark-based big-data service
16. Bring AI to everyone with an end-to-end, scalable, trusted platform
Built with your needs in mind
Support for open source frameworks
Managed compute
DevOps for machine learning
Simple deployment
Tool agnostic Python SDK
Automated machine learning
Seamlessly integrated with the Azure Portfolio
Boost your data science productivity
Increase your rate of experimentation
Deploy and manage your models everywhere
17. Leverage your favorite deep learning frameworks
AZURE ML SERVICE
Increase your rate of experimentation
Bring AI to the edge
Deploy and manage your models everywhere
TensorFlow MS Cognitive Toolkit PyTorch Scikit-Learn ONNX Caffe2 MXNet Chainer
AZURE DATABRICKS
Accelerate processing with the fastest Apache Spark engine
Integrate natively with Azure services
Access enterprise-grade Azure security
18. From the Intelligent Cloud to the Intelligent Edge
Train and deploy Train and deploy
Deploy
Track models in production
Capture model telemetry
Retrain models
19. Accelerate deep learning
General purpose machine
learning
D, F, L, M, H Series
CPUs
Optimized for flexibility Optimized for performance
GPUs FPGAs
Deep learning
N Series
Specialized hardware
accelerated deep learning
Project Brainwave
22. Machine Learning on Azure
Domain specific pretrained models
To reduce time to market
Azure
Databricks
Machine
Learning VMs
Popular frameworks
To build advanced deep learning solutions
TensorFlowPytorch Onnx
Azure Machine
Learning
LanguageSpeech
…
SearchVision
Productive services
To empower data science and development teams
Powerful infrastructure
To accelerate deep learning
Scikit-Learn
PyCharm Jupyter
Familiar Data Science tools
To simplify model development
Visual Studio Code Command line
CPU GPU FPGA
From the Intelligent Cloud to the Intelligent Edge
Gartner predicts that, in 2022, the global business value derived from AI will be $3.9 trillion.
Decision support and augmentation will account for $1.7T.
Virtual agents will account for $1T.
Smart products will account for $624B.
Decision automation systems will account for $546B.
<Transition>: $3.9T. Let’s break that down.
Our approach to ML frameworks is simple.
We give customers the flexibility to choose their deep learning framework, without getting locked one framework.
To help with this we’ve created a community project, ONNX, in partnership with Facebook that allows customers to train in one framework and use another one for inference
Now, let me move to the ML services on Azure
Custom vision
Then next layer in the stack is the services that these frameworks run on. We have two main services to help customers of all types do machine learning.
Azure Machine learning is a Python-based machine learning service. It’s can be accessed from any Python development environment. With automated machine learning capabilities, data scientists can build models faster. DevOps for machine learning enables data scientists and developers to enhance productivity with experiment tracking, model management and monitoring, integrated CI/CD, and machine learning pipelines. Models then can be deployed and managed in the cloud, on-premises and the edge.
Azure Databricks is an Apache Spark-based big-data service with Azure Machine Learning integration. It also has interactive notebooks that enable collaboration between data scientists and data engineers. Azure Databricks enables data scientists coming from a big data and Spark based background to prep and clean data and develop machine learning models using the language of their choice.
Azure Databricks & Azure Machine Learning work together nicely together and these two services enable data scientists of all types build and train machine learning models faster.
Azure Machine Learning Services empowers you to bring AI to everyone with an end-to-end, scalable, trusted platform.
Boost your data science productivity
Python pip-installable extensions for Azure Machine Learning that enable data scientists to build and deploy machine learning and deep learning models
Now available for Computer Vision, Text Analytics and Time-Series Forecasting.
Increase your rate of experimentation
Rapidly prototype on your desktop, then easily scale up on virtual machines or scale out using Spark clusters
Proactively manage model performance, identify the best model, and promote it using data-driven insights
Collaborate and share solutions using popular Git repositories.
Deploy and manage your models everywhere
Use Docker containers to deploy models into production faster in the cloud, on-premises, or at the edge
Promote your best performing models into production and retrain them when their performance degrades
Azure Machine Learning Services are built with your needs in mind, providing:
GPU-enabled virtual machines
Low-latency predictions at scale
Integration with popular Python IDEs
Role-based access controls
Model versioning
Automated model retraining
(Optional: other services)
Azure Machine Learning Workbench integrates with ONNX models
Work with your ONNX models from Visual Studio Code Tools for AI.
Build deep learning models and call services straight from your favorite IDE easier with Azure Machine Learning services built right in.
Create a seamless developer experience across desktop, cloud, or at the edge.
AI Toolkit for Azure IoT Edge
MMLSpark is an open-source Spark package that enables you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets by using deep learning and data science tools for Apache Spark.
Azure Machine Learning Services seamlessly integrates with the rest of the Azure portfolio.
<Transition>: Azure Machine Learning Services allows you to deploy models to many different production environments.
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure. Designed in collaboration with the founders of Apache Spark, Azure Databricks combines the best of Spark and Azure to help customers accelerate innovation.
With Azure Databricks, you can:
Accelerate data processing with the fastest Spark engine
Innovate faster, thanks to native integration with services like PBI, Azure SQL DW, Cosmos DB and Blob Storage
Protect your data with enterprise-grade Azure security.
Azure Machine Learning services enables you to:
Bring the power of AI to the IoT edge
Increase your rate of experimentation by rapidly prototyping on your desktop, then easily scaling up on VMs or scaling out on Spark clusters
Deploy models into production faster in the cloud, on-premises, or at the edge
Promote your best performing models into production and retrain them when their performance degrades
These Azure services also empower you to leverage your favorite deep learning frameworks for AI development, including:
TensorFlow
The Microsoft Cognitive Toolkit
PyTorch
Scikit-Learn
ONNX
Caffe2
MXNet
Chainer
<Transition>: Let’s dive into each of these services in a little more detail, starting with Azure Databricks.
Let’s move to the last part of our Machine Learning portfolio. We are the only company that offers the ability to deploy and manage models, whether in the cloud, on-premises, or even the Edge. This is extremely valuable in disconnected scenarios, where predictions have to be made on the Edge, without connectivity to the cloud. With IoT deployments becoming more widespread, we are well positioned to help our customers innovate with AI wherever they want.
For machine learning and deep learning, you need powerful hardware
We have the most comprehensive AI infrastructure
From general purpose CPUs to specialized HW (FPGAs)
FPGA offer lowest cost inferencing. Lower than Google’s TPUs. You are also not locked into one framework.
We also have the most comprehensive set of GPU options so customers can choose the right one for their project. (best price/performance)
Let me move to the last part of our ML portfolio.
Microsoft Research has made significant breakthroughs in the AI categories of Vision, Speech and Language
In fact, we were the first to reach parity with humans in object recognition, speech recognition, machine translation and machine reading comprehension
These breakthroughs are not enough by themselves. You’ve been a critical part of driving adoption of AI with our customers.
Thank you!
Because of your efforts we have driven significant momentum in FY18.
1M developers using Azure Cognitive Services
Over 300k developers using Azure Bot Service
Great adoption of our machine learning offerings both first party and third party
In FY19, we are looking to accelerate our growth.
<Transition>: Now, at this point, you’re probably wondering, “how do I get started?”