Artificial Intelligence for Business Transformation.
- 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
Vertical is the New Horizontal - MinneAnalytics 2016 Sri Ambati Keynote on AISri Ambati
Data is the only vertical, Machine Learning, bigdata, artificial intelligence
- 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
Intro to Machine Learning with H2O and Python - DenverSri Ambati
Presentation at Comcast Denver 03.01.16
- 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
Democratizing Intelligence - Sri Ambati, CEO & Co-Founder, H2O.aiSri Ambati
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/ZrlJQqNaSMI.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://www.twitter.com/h2oai.
Hank Roark of H2O gives an overview on data science, machine learning, and H2O.
- 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 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
Vertical is the New Horizontal - MinneAnalytics 2016 Sri Ambati Keynote on AISri Ambati
Data is the only vertical, Machine Learning, bigdata, artificial intelligence
- 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
Intro to Machine Learning with H2O and Python - DenverSri Ambati
Presentation at Comcast Denver 03.01.16
- 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
Democratizing Intelligence - Sri Ambati, CEO & Co-Founder, H2O.aiSri Ambati
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/ZrlJQqNaSMI.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://www.twitter.com/h2oai.
Hank Roark of H2O gives an overview on data science, machine learning, and H2O.
- 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 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
SF Big Data Science Meetup 12.15.15
Scripts here: https://github.com/h2oai/h2o-meetups/tree/master/2015_12_15_MessyDataMeetup
- 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.
This in-depth training on H2O Driverless AI was given by Wen Phan on June 28th, 2018. He elaborated on automatic feature engineering, machine learning interpretability, and automatic visualization components of this ground breaking product.
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...Shirshanka Das
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.
Intro to Machine Learning with H2O and AWSSri Ambati
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
H2O Random Grid Search - PyData AmsterdamSri Ambati
Jo-Fai Chow's presentation on Using H2O Random Grid Search for Hyper-Parameters Optimization at PyData Amsterdam 03.12.16
- 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
Transformation, H2O Open Dallas 2016, Keynote by Sri Ambati, Sri Ambati
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
H2O Machine Learning and Kalman Filters for Machine Prognostics - Galvanize SFSri Ambati
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
Drive Away Fraudsters With Driverless AI - Venkatesh Ramanathan, Senior Data ...Sri Ambati
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.
Building Real Time Targeting Capabilities - Ryan Zotti, Subbu Thiruppathy - C...Sri Ambati
A team of data and software engineers and data scientists at Capital One are experimenting with various technologies to enable lightning-fast promotional content that visitors will see when they visit Capital One’s website looking to apply for a credit card. In this presentation we’ll first talk about some of the technologies that we’re exploring such as the Akka-based Play framework, and H2O, a popular open source machine learning library. We will explore our evolution of data science and the H2O tools used to create the groundwork for continuous and automated testing and optimization, with the ability to scale across the entire company. Then conclude with a quick demo followed by a few tips and tricks that we learned along the way. #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
Strata 2016 - Architecting for Change: LinkedIn's new data ecosystemShirshanka Das
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.
Automatic and Interpretable Machine Learning in R with H2O and LIMEJo-fai Chow
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).
Introduction & Hands-on with H2O Driverless AISri Ambati
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
Introduction to Deep Learning and AI at Scale for ManagersDataWorks Summit
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
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.
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.
H2O.ai's Distributed Deep Learning by Arno Candel 04/03/14Sri Ambati
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
SF Big Data Science Meetup 12.15.15
Scripts here: https://github.com/h2oai/h2o-meetups/tree/master/2015_12_15_MessyDataMeetup
- 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.
This in-depth training on H2O Driverless AI was given by Wen Phan on June 28th, 2018. He elaborated on automatic feature engineering, machine learning interpretability, and automatic visualization components of this ground breaking product.
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...Shirshanka Das
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.
Intro to Machine Learning with H2O and AWSSri Ambati
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
H2O Random Grid Search - PyData AmsterdamSri Ambati
Jo-Fai Chow's presentation on Using H2O Random Grid Search for Hyper-Parameters Optimization at PyData Amsterdam 03.12.16
- 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
Transformation, H2O Open Dallas 2016, Keynote by Sri Ambati, Sri Ambati
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
H2O Machine Learning and Kalman Filters for Machine Prognostics - Galvanize SFSri Ambati
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
Drive Away Fraudsters With Driverless AI - Venkatesh Ramanathan, Senior Data ...Sri Ambati
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.
Building Real Time Targeting Capabilities - Ryan Zotti, Subbu Thiruppathy - C...Sri Ambati
A team of data and software engineers and data scientists at Capital One are experimenting with various technologies to enable lightning-fast promotional content that visitors will see when they visit Capital One’s website looking to apply for a credit card. In this presentation we’ll first talk about some of the technologies that we’re exploring such as the Akka-based Play framework, and H2O, a popular open source machine learning library. We will explore our evolution of data science and the H2O tools used to create the groundwork for continuous and automated testing and optimization, with the ability to scale across the entire company. Then conclude with a quick demo followed by a few tips and tricks that we learned along the way. #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
Strata 2016 - Architecting for Change: LinkedIn's new data ecosystemShirshanka Das
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.
Automatic and Interpretable Machine Learning in R with H2O and LIMEJo-fai Chow
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).
Introduction & Hands-on with H2O Driverless AISri Ambati
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
Introduction to Deep Learning and AI at Scale for ManagersDataWorks Summit
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
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.
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.
H2O.ai's Distributed Deep Learning by Arno Candel 04/03/14Sri Ambati
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
Strata San Jose 2016: Deep Learning is eating your lunch -- and mineSri Ambati
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: DEP Bypassing with ROP ChainsAjin Abraham
Exploit Research and Development Megaprimer
http://opensecurity.in/exploit-research-and-development-megaprimer/
http://www.youtube.com/playlist?list=PLX3EwmWe0cS_5oy86fnqFRfHpxJHjtuyf
Hacking Tizen: The OS of everything - WhitepaperAjin Abraham
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.
Hacking Samsung's Tizen: The OS of Everything - Hack In the Box 2015Ajin Abraham
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).
Abusing, Exploiting and Pwning with Firefox Add-onsAjin Abraham
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 PitfallsSri Ambati
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
Abusing Google Apps and Data API: Google is My Command and Control CenterAjin Abraham
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.
AppSec EU 2016: Automated Mobile Application Security Assessment with MobSFAjin Abraham
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.
Scalable Data Science and Deep Learning with H2Oodsc
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 - Top 10 Data Science Pitfalls - Mark LandrySri Ambati
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
H2O Open New York - Keynote, Sri Ambati, CEO H2O.aiSri Ambati
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 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.
Future of Enterprise PaaS (Cloud Foundry Summit 2014)VMware Tanzu
Keynote delivered by Steve Winkler, Open Cloud Strategy & Dirk Basenach, VP Development at SAP.
There are many approaches to running enterprise applications in the cloud, and SAP has made a strategic choice to leverage the Open Source solution Cloud Foundry for this purpose. This presentation will provide details on SAP’s approach to Open Source in the cloud, focusing on the PaaS layer and showing how Cloud Foundry can be used to extend existing SAP products and to develop entirely new enterprise applications. Moreover, the presentation will show how the SAP HANA in-memory platform and Cloud Foundry will come together to provide an enterprise-grade, real-time open platform in the cloud.
아마존 혁신의 배경 및 Digital Innovation Program 소개 – 김중수, AWS 사업개발 담당/ 김성락, LG 인화원 책...Amazon Web Services Korea
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.
Building Resiliency and Agility with Data Virtualization for the New NormalDenodo
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
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.
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?
Keynote presentation from IBM Solutions Connect 2013 covering topics such as changing business world today and how technologies can help organisations cope with this change and move forward.
Big Data World Singapore 2017 - Moving Towards Digitization & Artificial Inte...Garrett Teoh Hor Keong
Presentation at Big Data World Asia Singapore 2017. A brief introduction to strategies for digitization transformation and introduction to Artificial Intelligence.
Modernize 2018: The Need for Speed - ContentfulOptimizely
The Need for Speed presented by Paul Biggs, Director of Product Marketing, Contentful & Trent McClenahan, Head of Digital and Emerging Businesses Delivery, nib health funds
Companies are facing increasing demands -- and finding increasing opportunities -- to engage customers in new channels and new markets. It's forcing all companies to become digital product companies, as they race to build websites, apps, and devices that support emerging touchpoints along the customer journey. Winning companies are staying ahead by empowering cross-functional teams to ship digital products faster. Learn how content infrastructure plays a critical role in removing common roadblocks faced by the modern, agile team.
ABOUT MODERNIZE 2018:
Be a part of the future. Shape the customer experience with Accenture, Forrester, Qantas, REA, Intrepid and more -- and learn how leading organisations are harnessing their people, processes, and technologies to exceed customer’s expectations.
Visit: https://www.optimizely.com/anz/modernize2018
Modernize Conference 2018 - The Need for Speed - Contentful and nib health fundsPaul Biggs
Presented by Contentful and nib health funds at the Modernize Conference in Sydney Australia on May 16th, 2018.
Learn how the traditional CMS market is being disrupted by new market dynamics, as all companies are being forced to become digital product companies -- they must adapt and become software-centric in order to keep up with their customers.
Companies are creating cross-functional digital teams to support this new mandate, who are building modern, reusable architectures so they can get to market faster.
Building LLM Solutions using Open Source and Closed Source Solutions in Coher...Sri Ambati
Sandeep Singh, Head of Applied AI Computer Vision, Beans.ai
H2O Open Source GenAI World SF 2023
In the modern era of machine learning, leveraging both open-source and closed-source solutions has become paramount for achieving cutting-edge results. This talk delves into the intricacies of seamlessly integrating open-source Large Language Model (LLM) solutions like Vicuna, Falcon, and Llama with industry giants such as ChatGPT and Google's Palm. As the demand for fine-tuned and specialized datasets grows, it is imperative to understand the synergy between these tools. Attendees will gain insights into best practices for building and enriching datasets tailored for fine-tuning tasks, ensuring that their LLM projects are both robust and efficient. Through real-world examples and hands-on demonstrations, this talk will equip attendees with the knowledge to harness the power of both open and closed-source tools in a coherent and effective manner.
Patrick Hall, Professor, AI Risk Management, The George Washington University
H2O Open Source GenAI World SF 2023
Language models are incredible engineering breakthroughs but require auditing and risk management before productization. These systems raise concerns about toxicity, transparency and reproducibility, intellectual property licensing and ownership, disinformation and misinformation, supply chains, and more. How can your organization leverage these new tools without taking on undue or unknown risks? While language models and associated risk management are in their infancy, a small number of best practices in governance and risk are starting to emerge. If you have a language model use case in mind, want to understand your risks, and do something about them, this presentation is for you!
Dr. Alexy Khrabrov, Open Source Science Community Director, IBM
H2O Open Source GenAI World SF 2023
In this talk, Dr. Alexy Khrabrov, recently elected Chair of the new Generative AI Commons at Linux Foundation for AI & Data, outlines the OSS AI landscape, challenges, and opportunities. With new models and frameworks being unveiled weekly, one thing remains constant: community building and validation of all aspects of AI is key to reliable and responsible AI we can use for business and society needs. Industrial AI is one key area where such community validation can prove invaluable.
Michelle Tanco, Head of Product, H2O.ai
H2O Open Source GenAI World SF 2023
Learn how the makers at H2O.ai are building internal tools to solve real use cases using H2O Wave and h2oGPT. We will walk through an end-to-end use case and discuss how to incorporate business rules and generated content to rapidly develop custom AI apps using only Python APIs.
Applied Gen AI for the Finance Vertical Sri Ambati
Megan Kurka, Vice President, Customer Data Scientist, H2O.ai
H2O Open Source GenAI World SF 2023
Discover the transformative power of Applied Gen AI. Learn how the H2O team builds customized applications and workflows that integrate capabilities of Gen AI and AutoML specifically designed to address and enhance financial use cases. Explore real world examples, learn best practices, and witness firsthand how our innovative solutions are reshaping the landscape of finance technology.
Practitioner's Guide to LLMs: Exploring Use Cases and a Glimpse Beyond Curren...Sri Ambati
Pascal Pfeiffer, Principal Data Scientist, H2O.ai
H2O Open Source GenAI World SF 2023
This talk dives into the expansive ecosystem of Large Language Models (LLMs), offering practitioners an insightful guide to various relevant applications, from natural language understanding to creative content generation. While exploring use cases across different industries, it also honestly addresses the current limitations of LLMs and anticipates future advancements.
Introducción al Aprendizaje Automatico con H2O-3 (1)Sri Ambati
En esta reunión virtual, damos una introducción a la plataforma de aprendizaje automático de código abierto número 1, H2O-3 y te mostramos cómo puedes usarla para desarrollar modelos para resolver diferentes casos de uso.
From Rapid Prototypes to an end-to-end Model Deployment: an AI Hedge Fund Use...Sri Ambati
Numerai is an open, crowd-sourced hedge fund powered by predictions from data scientists around the world. In return, participants are rewarded with weekly payouts in crypto.
In this talk, Joe will give an overview of the Numerai tournament based on his own experience. He will then explain how he automates the time-consuming tasks such as testing different modelling strategies, scoring new datasets, submitting predictions to Numerai as well as monitoring model performance with H2O Driverless AI and R.
AI Foundations Course Module 1 - Shifting to the Next Step in Your AI Transfo...Sri Ambati
In this session, you will learn about what you should do after you’ve taken an AI transformation baseline. Over the span of this session, we will discuss the next steps in moving toward AI readiness through alignment of talent and tools to drive successful adoption and continuous use within an organization.
To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course
To find the Youtube video about this presentation: https://youtu.be/K1Cl3x3rd8g
Speaker:
Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)
AI Foundations Course Module 1 - An AI Transformation JourneySri Ambati
The chances of successfully implementing AI strategies within an organization significantly improve when you can recognize where your organization is on the maturity scale. Over this course, you will learn the keys to unlocking value with AI which include asking the right questions about the problems you are solving and ensuring you have the right cross-section of talent, tools, and resources. By the end of this module, you should be able to recognize where your organization is on the AI transformation spectrum and identify some strategies that can get you to the next stage in your journey.
To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course
To find the Youtube video about this presentation: https://youtu.be/PJgr2epM6qs
Speakers:
Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)
Ingrid Burton (H2O.ai - CMO)
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
20. H2O.ai
Machine Intelligence
H2O Use Cases: Available Videos and Talks
Auto Insurance
UBI Telematics
Commercial Insurance
Risk Analytics
Financial Services
Customer Insights
Digital Marketing
Consumer Behavior
Pawan Divarkarla
Data and Analytics
Business Leader
“H2O is an enabler in
how people are thinking
about data.”
Conor Jensen
Analytics Director
“Advanced analytics
was one of the key
investments we
decided to make.”
Brendan Herger
Data Scientist
“H2O is the best solution to to
iterate very quickly on large
datasets and produce meaning
models.”
Satya Satyamoorthy
Director, Software Dev
"I am a big fan of open
source. H2O is the best fit in
terms of cost as well as ease
of use and scalability and
usability.”
Play Video Play Video Play Video Play Video
Progressive Zurich Capital One Nielsen
21. H2O.ai
Machine Intelligence
H2O Use Cases: Available Videos and Talks
Digital Marketing
Marketing Optimization
Healthcare
Advanced Alert Monitoring
Financial Services
Customer Churn
Insurance
Product Recommendation
Prateem Mandal
Technical Lead Architect
“H2O gave us the capability
to do Big Modeling.
There is no limit to scaling in
H2O.”
Taposh Dutta Roy
Data & Science Manager
Machine Learning to Save
Lives
Julian Bharadwaj
Data Scientist
Solving Customer Churn
with Machine Learning
Vishal Bamba
VP, Strategy & Architecture
Transamerica Product
Recommendation Platform
Play Video Play Video Play Video Play Video
Marketshare Kaiser PayPal Transamerica
22. FLEET TELEMATICS: PREVENTIVE MAINTENANCE
PROBLEM
• Fleet telematics—analyze maintenance
records and vehicle performance
• Make predictions on when to do
preventive maintenance. Couldn’t scale.
Took days to create models
“Annual Savings are $7M”
– Member Technical Staff
Leading Telecom Operator
CUSTOMER SERVICE: AVOIDABLE TRUCK ROLL
•High volume of support calls, large scale
systems for 10Ms of customers, 100Ms
devices. Prevent “care avoidable” costs.
•Paid technicians are deployed onsite to
resolve issues that could have been resolved
over the phone
World’s Largest Cable and
Broadcasting Company
39. Data Product and Smart Applications!
Listen Learn, not Rule
Rules
On Data
UX
Learn
From Data
Design API API
Design
API
Design
API
Design
learn
MicroServices
feedback loops
40. Data Product and Smart Applications!
App Store
Rules
On Data
UX
Learn
From Data
Design API API
Design
API
Design
API
Design
learn
Cloud Native
41. Data Dependencies cost more than
C0de Dependencies
Data Products need new Tools!
ML pipeline jungles.
Configuration Debt
A changin’ world makes data products unstable
42. Interpretation.
Signal is the API
Make sense of the Math
Telling Stories.
Data Driven Decision Making Takes Courage!
47. People
IOT
Cloud
Data
ML
Design, DevOps, Data Scientists, Data Engineers, App Devs
App Store
$,$$$,$$$ $$, $$$,$$$
Business Transformation
Steam
Operationalize Data Science
fast
accurate
governed
H2O
63. Business Transformation Units
CTO
Design Thinker
Data Engineer
Data Scientist
DevOps & Cloud
Financial Wiz
Program Mgr
Marketing
Salesmanship
Dreamer
Domain Scientist
64. Business Transformation Units
CTO
Design Thinker
Data Engineer
Data Scientist
DevOps & Cloud
Financial Wiz
Program Mgr
Marketing
Salesmanship
Dreamer
Domain Scientist
IRR
67. We shall not cease from exploration,
And the end of all our exploring
Will be to arrive where we started
And know the place for the first time.
T. S. Eliot said that.