H2O AutoML roadmap - Ray Peck
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Ray Peck from H2O.ai talks about the roadmap for the upcoming AutoML product in 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
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- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
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Deep Water brings the latest and greatest in the Deep Learning space all under the H2O hood. Use Tensorflow, Mxnet & Caffe all from standard H2O interface's including R, Python & Flow. Also deploy your models easily using the H2O platform.
- 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
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Scalable and Automatic Machine Learning with H2O
H2O is widely used for machine learning projects. A TechCrunch article, published in January 2017 by John Mannes, reported that around 20% of Fortune 500 companies use H2O.
Talk 1: Introduction to Scalable & Automatic Machine Learning with H2O
In recent years, the demand for machine learning experts has outpaced the supply, despite the surge of people entering the field. To address this gap, there have been big strides in the development of user-friendly machine learning software that can be used by non-experts. Although H2O and other tools have made it easier for practitioners to train and deploy machine learning models at scale, there is still a fair bit of knowledge and background in data science that is required to produce high-performing machine learning models.
In this presentation, Joe will introduce the AutoML functionality in H2O. H2O's AutoML provides an easy-to-use interface which automates the process of training a large, comprehensive selection of candidate models and a stacked ensemble model which, in most cases, will be the top performing model in the AutoML Leaderboard.
Talk 2: Making Multimillion-dollar Baseball Decisions with H2O AutoML and Shiny
Joe recently teamed up with IBM and Aginity to create a proof of concept "Moneyball" app for the IBM Think conference in Vegas. The original goal was to prove that different tools (e.g. H2O, Aginity AMP, IBM Data Science Experience, R and Shiny) could work together seamlessly for common business use-cases. Little did Joe know, the app would be used by Ari Kaplan (the real "Moneyball" guy) to validate the future performance of some baseball players. Ari recommended one player to a Major League Baseball team. The player was signed the next day with a multimillion-dollar contract. This talk is about Joe's journey to a real "Moneyball" application.
Bio : Jo-fai (or Joe) Chow is a data scientist at H2O.ai. Before joining H2O, he was in the business intelligence team at Virgin Media in UK where he developed data products to enable quick and smart business decisions. He also worked remotely for Domino Data Lab in the US as a data science evangelist promoting products via blogging and giving talks at meetups. Joe has a background in water engineering. Before his data science journey, he was an EngD research engineer at STREAM Industrial Doctorate Centre working on machine learning techniques for drainage design optimization. Prior to that, he was an asset management consultant specialized in data mining and constrained optimization for the utilities sector in the UK and abroad. He also holds an MSc in Environmental Management and a BEng in Civil Engineering.
Near realtime AI deployment with huge data and super low latency - Levi Brack...
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.
H2O Driverless AI Workshop
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.
Driverless AI Hands-on Focused on Machine Learning Interpretability - H2O.ai
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/axIqeaUhow0.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
Usage of AI and machine learning models is likely to become more commonplace as larger swaths of the economy embrace automation and data-driven decision-making. While these predictive systems can be quite accurate, they have been treated as inscrutable black boxes in the past, that produce only numeric predictions with no accompanying explanations. Unfortunately, recent studies and recent events have drawn attention to mathematical and sociological flaws in prominent weak AI and ML systems, but practitioners usually don’t have the right tools to pry open machine learning black-boxes and debug them. This presentation introduces several new approaches to that increase transparency, accountability, and trustworthiness in machine learning models. If you are a data scientist or analyst and you want to explain a machine learning model to your customers or managers (or if you have concerns about documentation, validation, or regulatory requirements), then this presentation is for you!
Driverless AI - Intro + Interactive Hands-on Lab
Enjoy the webinar recording here: https://youtu.be/Lll1qwQJKVw.
Driverless AI speeds up data science workflows by automating feature engineering, model tuning, ensembling, and model deployment.
In this presentation, Arno Candel (CTO, H2O.ai), gives a quick overview and guide attendees through an interactive hands-on lab using Qwiklabs.
Driverless AI turns Kaggle-winning recipes into production-ready code and is specifically designed to avoid common mistakes such as under or overfitting, data leakage or improper model validation. Avoiding these pitfalls alone can save weeks or more for each model, and is necessary to achieve high modeling accuracy.
With Driverless AI, everyone can now train and deploy modeling pipelines with just a few clicks from the GUI. Advanced users can use the client/server API through a variety of languages such as Python, Java, C++, go, C# and many more. To speed up training, Driverless AI uses highly optimized C++/CUDA algorithms to take full advantage of the latest compute hardware.
For example, Driverless AI runs orders of magnitudes faster on the latest Nvidia GPU supercomputers on Intel and IBM platforms, both in the cloud or on-premise. There are two more product innovations in Driverless AI: statistically rigorous automatic data visualization and interactive model interpretation with reason codes and explanations in plain English. Both help data scientists and analysts to quickly validate the data and models.
Lambda Architecture and open source technology stack for real time big data
Concepts & Techniques “Thinking with Lambda”
Case studies in Practice using Lambda architecture
Lambda architecture for real time big data
Lambda Architecture in Real-time Big Data Project
Concepts & Techniques “Thinking with Lambda”
Case study in some real projects
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Quantifying Genuine User Experience in Virtual Desktop Ecosystems
Data Con LA 2020
When users complain about slowness in their virtual application or desktop, User Experience becomes a subjective measurement, or a feeling of how well the infrastructure is performing. This talk will focus on the objective measurement and what that looks like for your business.
Takeaways:
*Attendees will learn the method for monitoring User Experience for virtual apps and desktops.
*Attendees will learn the do's and don'ts of monitoring for User Experience in the virtual world.
*Attendees will gain a sense of importance of monitoring UX for their business cases when purchasing a monitoring solution like eG Enterprise.
Typical Audience:
Architects, engineers, managers, end-user solutions experts that work in the virtual desktop space such as Citrix, Horizon, DaaS, and more.
Speaker
Wendy Howard, Eg Innovations, Technical Consultant
Forget becoming a Data Scientist, become a Machine Learning Engineer instead
Data Con LA 2020
Description
Machine learning is an essential skill in today's job market. But when it comes to learning Machine Learning, beginners get lot of conflicting advice. I have been teaching ML for software engineers for years. In this talk
*I will dis-spell some of the myths surrounding machine learning
*give you solid, tangible plan on how to go about learning ML
*and give you good pointers to start from
*and steer you away from common mistakes
Speaker
Sujee Maniyam, Elephant Scale, Founder, Principal instructor
Machine Learning Interpretability - Mateusz Dymczyk - H2O AI World London 2018
This talk was recorded in London on Oct 30, 2018 and can be viewed here: https://youtu.be/p4iAnxwC_Eg
The good news is building fair, accountable, and transparent machine learning systems is possible. The bad news is it’s harder than many blogs and software package docs would have you believe. The truth is nearly all interpretable machine learning techniques generate approximate explanations, that the fields of eXplainable AI (XAI) and Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) are very new, and that few best practices have been widely agreed upon. This combination can lead to some ugly outcomes!
This talk aims to make your interpretable machine learning project a success by describing fundamental technical challenges you will face in building an interpretable machine learning system, defining the real-world value proposition of approximate explanations for exact models, and then outlining the following viable techniques for debugging, explaining, and testing machine learning models
Mateusz is a software developer who loves all things distributed, machine learning and hates buzzwords. His favourite hobby data juggling.
He obtained his M.Sc. in Computer Science from AGH UST in Krakow, Poland, during which he did an exchange at L’ECE Paris in France and worked on distributed flight booking systems. After graduation he move to Tokyo to work as a researcher at Fujitsu Laboratories on machine learning and NLP projects, where he is still currently based.
Nanda Vijaydev, BlueData - Deploying H2O in Large Scale Distributed Environme...
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/CgoxjmdyMiU
This session will discuss how to get up and running quickly with containerized H2O environments (H2O Flow, Sparkling Water, and Driverless AI) at scale, in a multi-tenant architecture with a shared pool of resources using CPUs and/or GPUs. See how how you can spin up (and tear down) your H2O environments on-demand, with just a few mouse clicks. Find out how to enable quota management of GPU resources for greater efficiency, and easily connect your compute to your datasets for large-scale distributed machine learning. Learn how to operationalize your machine learning pipelines and deliver faster time-to-value for your AI initiative — while ensuring enterprise-grade security and high performance.
Bio: Nanda Vijaydev is senior director of solutions at BlueData (now HPE) - where she leverages technologies like Hadoop, Spark, and TensorFlow to build solutions for enterprise analytics and machine learning use cases. Nanda has 10 years of experience in data management and data science. Previously, she worked on data science and big data projects in multiple industries, including healthcare and media; was a principal solutions architect at Silicon Valley Data Science; and served as director of solutions engineering at Karmasphere. Nanda has an in-depth understanding of the data analytics and data management space, particularly in the areas of data integration, ETL, warehousing, reporting, and machine learning.
Fast Data at ING – the why, what and how of the streaming analytics platform ...
Presented at the Spark/Flink meetup during the Dutch Data Science Week 2017
How to design and implement a data ops architecture with sdc and gcp
Do you know how to use StreamSets Data Collector with Google Cloud Platform (GCP)? In this session we'll explain how YaloChat designed and implemented a streaming architecture that is sustainable, operable and scalable. Discover how we deployed Data Collector to integrate GCP components such as Pub / Sub and BigQuery to achieve DataOps in the cloud
Intro to H2O Machine Learning in Python - Galvanize Seattle
Erin LeDell presents Intro to H2O Machine Learning in Python at Galvanize Seattle, 02.02.16
- 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
Productionizing H2O Models with Apache Spark
This meetup was recorded in Mountain View, CA on January 10th, 2019.
Video recording from the meetup can be viewed here: https://youtu.be/yN26i7e_BtM
Spark pipelines represent a powerful concept to support productionizing machine learning workflows. Their API allows to combine data processing with machine learning algorithms and opens opportunities for integration with various machine learning libraries. However, to benefit from the power of pipelines, their users need to have a freedom to choose and experiment with any machine learning algorithm or library. Therefore, we developed Sparkling Water that embeds H2O machine learning library of advanced algorithms into the Spark ecosystem and exposes them via pipeline API. Furthermore, the algorithms benefit from H2O MOJOs - Model Object Optimized - a powerful concept shared across entire H2O platform to store and exchange models. The MOJOs are designed for effective model deployment with focus on scoring speed, traceability, exchangeability, and backward compatibility. In this talk we will explain the architecture of Sparkling Water with focus on integration into the Spark pipelines and MOJOs. We’ll demonstrate creation of pipelines integrating H2O machine learning models and their deployments using Scala or Python. Furthermore, we will show how to utilize pre-trained model MOJOs with Spark pipelines.
Speaker's Bio:
Michal is the VP of Engineering at H2O.ai! Michal is a geek, developer, Java, Linux, programming languages enthusiast developing software for over 15 years.
He obtained PhD from the Charles University in Prague in 2012 and post-doc at Purdue University.
During his studies he was interested in construction of not only distributed but also embedded and real-time component-based systems using model-driven methods and domain-specific languages. He participated in design and development of various systems including SOFA and Fractal component systems or jPapabench control system.
Spark Summit Europe 2016 Keynote - Databricks CEO
Machine learning algorithm itself is rarely the main barrier in building AI applications. Instead, the real culprit is the set of complex systems that prepares large-scale training and test data for the ML algorithms.
Apache Spark is a huge leap forward in democratizing AI. However, it does not solve all the problems. Databricks CEO Ali Ghodsi explains how Databricks democratizes AI by making it easier to build end-to-end machine learning pipelines with Apache Spark.
Machine Learning with H2O
These slides were presented by Mateusz Dymcyzk at the our Sydney AI and deep learning meetup. The authors were Sudalai Rajkumar (SRK), Data Scientist at H2O.ai and Nikhil Shekhar, ML Engineer at H2O.ai.
What's hot (20)
Near realtime AI deployment with huge data and super low latency - Levi Brack...
Near realtime AI deployment with huge data and super low latency - Levi Brack...
Driverless AI Hands-on Focused on Machine Learning Interpretability - H2O.ai
Driverless AI Hands-on Focused on Machine Learning Interpretability - H2O.ai
Lambda Architecture and open source technology stack for real time big data
Lambda Architecture and open source technology stack for real time big data
Quantifying Genuine User Experience in Virtual Desktop Ecosystems
Quantifying Genuine User Experience in Virtual Desktop Ecosystems
Forget becoming a Data Scientist, become a Machine Learning Engineer instead
Forget becoming a Data Scientist, become a Machine Learning Engineer instead
Machine Learning Interpretability - Mateusz Dymczyk - H2O AI World London 2018
Machine Learning Interpretability - Mateusz Dymczyk - H2O AI World London 2018
Nanda Vijaydev, BlueData - Deploying H2O in Large Scale Distributed Environme...
Nanda Vijaydev, BlueData - Deploying H2O in Large Scale Distributed Environme...
Fast Data at ING – the why, what and how of the streaming analytics platform ...
Fast Data at ING – the why, what and how of the streaming analytics platform ...
How to design and implement a data ops architecture with sdc and gcp
How to design and implement a data ops architecture with sdc and gcp
Intro to H2O Machine Learning in Python - Galvanize Seattle
Intro to H2O Machine Learning in Python - Galvanize Seattle
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Nvidia Deep Learning Solutions - Alex Sabatier
Alex Sabatier from Nvidia talks about the future of Deep Learning from an chipmaker perspective
- 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
Deep Water - GPU Deep Learning for H2O - Arno Candel
Deep Water brings the latest and greatest in the Deep Learning space all under the H2O hood. Use Tensorflow, Mxnet & Caffe all from standard H2O interface's including R, Python & Flow. Also deploy your models easily using the H2O platform.
- 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
Stacked Ensembles in H2O
Ensemble machine learning methods are often used when the true prediction function is not easily approximated by a single algorithm. Practitioners may prefer ensemble algorithms when model performance is valued above other factors such as model complexity and training time. The Super Learner algorithm, also called "stacking", learns the optimal combination of the base learner fits. The latest version of H2O now contains a "Stacked Ensemble" method, which allows the user to stack H2O models into a Super Learner. The Stacked Ensemble method is the the native H2O version of stacking, previously only available in the h2oEnsemble R package, and now enables stacking from all the H2O APIs: Python, R, Scala, etc.
Erin is a Statistician and Machine Learning Scientist at H2O.ai. Before joining H2O, she was the Principal Data Scientist at Wise.io (acquired by GE Digital) and Marvin Mobile Security (acquired by Veracode) and the founder of DataScientific, Inc. Erin received her Ph.D. from University of California, Berkeley. Her research focuses on ensemble machine learning, learning from imbalanced binary-outcome data, influence curve based variance estimation and statistical computing.
Deep Learning with MXNet - Dmitry Larko
Dmitry will show the audience on how get started with Mxnet and building Deep Learning models to classify images, sound and text.
- 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
Sparkling Water 2.0 - Michal Malohlava
Michal Malohlava from H2O.ai talks about the new features in Sparkling Water 2.0 and the future roadmap.
- 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,
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 Deep Water - Making Deep Learning Accessible to Everyone
Deep Water is H2O's integration with multiple open source deep learning libraries such as TensorFlow, MXNet and Caffe. On top of the performance gains from GPU backends, Deep Water naturally inherits all H2O properties in scalability. ease of use and deployment. In this talk, I will go through the motivation and benefits of Deep Water. After that, I will demonstrate how to build and deploy deep learning models with or without programming experience using H2O's R/Python/Flow (Web) interfaces.
Jo-fai (or Joe) is a data scientist at H2O.ai. Before joining H2O, he was in the business intelligence team at Virgin Media in UK where he developed data products to enable quick and smart business decisions. He also worked remotely for Domino Data Lab in the US as a data science evangelist promoting products via blogging and giving talks at meetups. Joe has a background in water engineering. Before his data science journey, he was an EngD research engineer at STREAM Industrial Doctorate Centre working on machine learning techniques for drainage design optimization. Prior to that, he was an asset management consultant specialized in data mining and constrained optimization for the utilities sector in the UK and abroad. He also holds an MSc in Environmental Management and a BEng in Civil Engineering.
H2O PySparkling Water
Michal Malohlava talks about the PySparkling Water package for Spark and Python users.
- 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
Top 10 Data Science Practitioner Pitfalls
Over-fitting, misread data, NAs, collinear column elimination and other common issues play havoc in the day of practicing data scientist. In this talk, Mark Landry, one of the world’s leading Kagglers, will review the top 10 common pitfalls and steps to avoid them.
- 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
Cybersecurity with AI - Ashrith Barthur
Ashrith talks about whether it's time for the cyber security industry to start using AI to solve their challenges
- 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
Using Machine Learning For Solving Time Series Probelms
By Danilo Bassi, Senior Software Engineer at H2O.ai
Presented at Feb 16 meetup
Skutil - H2O meets Sklearn - Taylor Smith
Skutil brings the best of both worlds to H2O and sklearn, delivering an easy transition into the world of distributed computing that H2O offers, while providing the same, familiar interface that sklearn users have come to know and love.
- 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
Machine Learning with H2O, Spark, and Python at Strata 2015
Machine Learning with H2O, Spark, and Python at Strata SJ 2015-by Cliff Click and Michal Malohlava
- 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
PayPal's Fraud Detection with Deep Learning in H2O World 2014
PayPal's Fraud Detection with Deep Learning in H2O World 2014 -
Flexible Deployment, Seamlessly with Big Data, Accuracy and Responsive support.
- 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 Deep Learning at Next.ML
Scalable Data Science and Deep Learning with H2O
In this session, we introduce the H2O data science platform. We will explain its scalable in-memory architecture and design principles and focus on the implementation of distributed deep learning in H2O. Advanced features such as adaptive learning rates, various forms of regularization, automatic data transformations, checkpointing, grid-search, cross-validation and auto-tuning turn multi-layer neural networks of the past into powerful, easy-to-use predictive analytics tools accessible to everyone. 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 the hands-on-session, attendees will have learned to perform end-to-end data science workflows with H2O using both the easy-to-use web interface and the flexible R interface. We will cover data ingest, basic feature engineering, feature selection, hyperparameter optimization with N-fold cross-validation, multi-model scoring and taking models into production. We will train supervised and unsupervised methods on realistic datasets. With best-of-breed machine learning algorithms such as elastic net, random forest, gradient boosting and deep learning, you will be able to create your own smart applications.
A local installation of RStudio is recommended for this session.
- 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 Distributed Deep Learning by Arno Candel 071614
Deep Learning R Vignette Documentation: https://github.com/0xdata/h2o/tree/master/docs/deeplearning/
Deep Learning has been dominating recent machine learning competitions with better predictions. Unlike the neural networks of the past, modern Deep Learning methods have cracked the code for training stability and generalization. Deep Learning is not only the leader in image and speech recognition tasks, but is also emerging as the algorithm of choice in traditional business analytics.
This talk introduces Deep Learning and implementation concepts in the open-source H2O in-memory prediction engine. Designed for the solution of enterprise-scale problems on distributed compute clusters, it offers advanced features such as adaptive learning rate, dropout regularization and optimization for class imbalance. World record performance on the classic MNIST dataset, best-in-class accuracy for eBay text classification and others showcase the power of this game changing technology. A whole new ecosystem of Intelligent Applications is emerging with Deep Learning at its core.
About the Speaker: Arno Candel
Prior to joining 0xdata as Physicist & Hacker, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world's largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives. While at SLAC, he authored the first curvilinear finite-element simulation code for space-charge dominated relativistic free electrons and scaled it to thousands of compute nodes.
He also led a collaboration with CERN to model the electromagnetic performance of CLIC, a ginormous e+e- collider and potential successor of LHC. Arno has authored dozens of scientific papers and was a sought-after academic conference speaker. He holds a PhD and Masters summa cum laude in Physics from ETH Zurich.
- 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
Transform your Business with AI, Deep Learning and Machine Learning
Video: https://www.youtube.com/watch?v=R3IXd1iwqjc
Meetup: http://www.meetup.com/SF-Bay-ACM/events/231709894/
In this talk, Arno Candel presents a brief history of AI and how Deep Learning and Machine Learning techniques are transforming our everyday lives. Arno will introduce H2O, a scalable open-source machine learning platform, and show live demos on how to train sophisticated machine learning models on large distributed datasets. He will show how data scientists and application developers can use the Flow GUI, R, Python, Java, Scala, JavaScript and JSON to build smarter applications, and how to take them to production. He will present customer use cases from verticals including insurance, fraud, churn, fintech, and marketing.
- 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
Applying Machine Learning using H2O
These slides will show how to approach a multi-class (classification) problem using H2O. The data that is being used is an aggregated log of multiple systems that are constantly providing information about their status, connections and traffic. In large organizations, these log datasets can be very huge and unidentifiable due to the number of sources, legacy systems etc. In our example, we use a created response for each source. The use H2O to classify the source of data.
Ashrith Barthur is a Security Scientist at H2O currently working on algorithms that detect anomalous behaviour in user activities, network traffic, attacks, financial fraud and global money movement. He has a PhD from Purdue University in the field of information security, specialized in Anomalous behaviour in DNS protocol.
Don’t forget to download H2O!
http://www.h2o.ai/download/
Strata San Jose 2016: Scalable Ensemble Learning with H2O
Erin LeDell's presentation on Scalable Ensemble Learning with H2O at Strata + Hadoop World San Jose, 03.29.16
- 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
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MongoDB .local Chicago 2019: MongoDB – Powering the new age data demands
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LIMS Implementation
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AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...
I will share the vision and the production journey of how we build enterprise shared AI As A Service platforms with distributed deep learning technologies. Including those topics:
1) The vision of Enterprise Shared AI As A Service and typical AI services use cases at FinTech industry
2) The high level architecture design principles for AI As A Service
3) The technical evaluation journey to choose an enterprise deep learning framework with comparisons, such as why we choose Deep learning framework based on Spark ecosystem
4) Share some production AI use cases, such as how we implemented new Users-Items Propensity Models with deep learning algorithms with Spark,improve the quality , performance and accuracy of offer and campaigns design, targeting offer matching and linking etc.
5) Share some experiences and tips of using deep learning technologies on top of Spark , such as how we conduct Intel BigDL into a real production.
Building a Real-Time Security Application Using Log Data and Machine Learning...
Building a Real-Time Security Application Using Log Data and Machine Learning- Karthik Aaravabhoomi
- 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
DevOps Days Rockies MLOps
This is my talk on DevOps & MLOps - the same but different from 2022 DevOps Days Rockies
MongoDB World 2019: High Performance Auditing of Changes Based on MongoDB Cha...
Take advantage of the elasticity of the cloud by creating resources that can heal themselves. Learn to create Compute Engine resources in GCP using Terraform that will install and configure a MongoDB replica set for you.
Comcast Labs Connect - PHLAI Conference Philadelphia 2018
Open Data Group's Product Manager Rehgan Avon Speaks about Achieving AI and ML Operational Excellence at the Comcast Labs Connect - Artificial Intelligence And Machine Learning Conference in Philadelphia 2018.
Machine Learning - Eine Challenge für Architekten
Aufgrund vielfältiger potenzieller Geschäftschancen, die Machine Learning bietet, starten viele Unternehmen Initiativen für datengetriebene Innovationen. Dabei gründen sie Analytics-Teams, schreiben neue Stellen für Data Scientists aus, bauen intern Know-how auf und fordern von der IT-Organisation eine Infrastruktur für "heavy" Data Engineering & Processing samt Bereitstellung einer Analytics-Toolbox ein. Für IT-Architekten warten hier spannende Herausforderungen, u.a. bei der Zusammenarbeit mit interdisziplinären Teams, deren Mitglieder unterschiedlich ausgeprägte Kenntnisse im Bereich Machine Learning (ML) und Bedarfe bei der Tool-Unterstützung haben.
Bigowl aitech
Knowledge extraction and incorporation is currently considered to be beneficial for efficient Big Data analytics. Knowledge can take part in workflow design, constraint definition, parameter selection and configuration, human interactive and decision-making strategies. Here we present BIGOWL, an ontology to support knowledge management in Big Data analytics. BIGOWL is designed to cover a wide vocabulary of terms concerning Big Data analytics workflows, including their components and how they are connected, from data sources to the analytics visualization. It also takes into consideration aspects such as parameters, restrictions and formats. This ontology defines not only the taxonomic relationships between the different concepts, but also instances representing specific individuals to guide the users in the design of Big Data analytics workflows. For testing purposes, two case studies are developed, which consists in: first, real-world streaming processing with Spark of traffic Open Data, for route optimization in urban environment of New York city; and second, data mining classification of an academic dataset on local/cloud platforms. The analytics workflows resulting from the BIGOWL semantic model are validated and successfully evaluated.
Algolytics company Overview 2015
Story of Algolytic. Assisting companies with lots of data to get insights and predictions about customer behaviour, predicting risks based on statistical data analysis.
Story of how we can help, how we changed from consulting company to product copmpany.
Overview of data analytics and predictive analytics solutions - such as Advanced Miner platform for complex data mining, Social Network analysis, Data quality tools.
Contact us for more info http://algolytics.com/blog/
check our news at the Blog: http://algolytics.com/blog/
Algolytics company Overview 2015
Story of our company - what we offer, how we changed from consulting company to product copmpany.
Overview of data analytics and predictive analytics solutions - such as Advanced Miner platform for complex data mining, Social Network analysis, Data quality tools etc.
Contact us for more info http://algolytics.com/blog/
check our news at the Blog: http://algolytics.com/blog/
Big Data LDN 2018: HOW AUTOMATION CAN ACCELERATE THE DELIVERY OF MACHINE LEAR...
Date: 14th November 2018
Location: AI Lab Theatre
Time: 14:30 - 15:00
Speaker: Andre Balleyguier
Organisation: DataRobot
An Introduction to Graph: Database, Analytics, and Cloud Services
Graph analysis employs powerful algorithms to explore and discover relationships in social network, IoT, big data, and complex transaction data. Learn how graph technologies are used in applications such as fraud detection for banking, customer 360, public safety, and manufacturing. This session will provide an overview and demos of graph technologies for Oracle Cloud Services, Oracle Database, NoSQL, Spark and Hadoop, including PGX analytics and PGQL property graph query language.
Presented at Analytics and Data Summit, March 20, 2018
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...
Watch full webinar here: https://bit.ly/35FUn32
Presented at CDAO New Zealand
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python, and Scala put advanced techniques at the fingertips of the data scientists.
However, most architecture laid out to enable data scientists miss two key challenges:
- Data scientists spend most of their time looking for the right data and massaging it into a usable format
- Results and algorithms created by data scientists often stay out of the reach of regular data analysts and business users
Watch this session on-demand to understand how data virtualization offers an alternative to address these issues and can accelerate data acquisition and massaging. And a customer story on the use of Machine Learning with data virtualization.
What is the future of data strategy?
Watch here: https://bit.ly/3i2iJbu
You will often hear that "data is the new gold". In this context, data management is one of the areas that has received more attention by the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
Join us for an exciting session that will cover:
- The most interesting trends in data management.
- Our predictions on how those trends will change the data management world.
- How these trends are shaping the future of data virtualization and our own software.
SharePoint Site Redesign : Information Architecture and User-centered Design ...
The Rockwell Automation Technical Communications Team presented their SharePoint redesign project at the Cleveland User Experience Professionals Association (Cleveland-UXPA) meeting on September 26th, 2013.
Similar to H2O AutoML roadmap - Ray Peck (20)
MongoDB .local Chicago 2019: MongoDB – Powering the new age data demands
MongoDB .local Chicago 2019: MongoDB – Powering the new age data demands
AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...
AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...
Building a Real-Time Security Application Using Log Data and Machine Learning...
Building a Real-Time Security Application Using Log Data and Machine Learning...
MongoDB World 2019: High Performance Auditing of Changes Based on MongoDB Cha...
MongoDB World 2019: High Performance Auditing of Changes Based on MongoDB Cha...
Comcast Labs Connect - PHLAI Conference Philadelphia 2018
Comcast Labs Connect - PHLAI Conference Philadelphia 2018
Techniques for scaling application with security and visibility in cloud
Techniques for scaling application with security and visibility in cloud
Big Data LDN 2018: HOW AUTOMATION CAN ACCELERATE THE DELIVERY OF MACHINE LEAR...
Big Data LDN 2018: HOW AUTOMATION CAN ACCELERATE THE DELIVERY OF MACHINE LEAR...
An Introduction to Graph: Database, Analytics, and Cloud Services
An Introduction to Graph: Database, Analytics, and Cloud Services
Making advertising personal, 4th NL Recommenders Meetup
Making advertising personal, 4th NL Recommenders Meetup
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...
SharePoint Site Redesign : Information Architecture and User-centered Design ...
SharePoint Site Redesign : Information Architecture and User-centered Design ...
More from Sri Ambati
GenAISummit 2024 May 28 Sri Ambati Keynote: AGI Belongs to The Community in O...
“AGI should be open source and in the public domain at the service of humanity and the planet.”
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
Sri Ambati's keynote presentation at Wells Fargo Day in San Francisco, April 2024
Generative AI Masterclass - Model Risk Management.pptx
The complete slide deck from H2O.ai's GenAI Master Class held at their GenAI Day event in Atlanta GA
AI and the Future of Software Development: A Sneak Peek
Pritika Mehta, Co-Founder, Butternut.ai
H2O Open Source GenAI World SF 2023
LLMOps: Match report from the top of the 5th
Stefan Krawczyk, Chief Executive Officer, DAGWorks Inc.
H2O Open Source GenAI World SF 2023
Building, Evaluating, and Optimizing your RAG App for Production
Jerry Liu, CEO, LlamaIndex
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Building LLM Solutions using Open Source and Closed Source Solutions in Coher...
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.
Risk Management for LLMs
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!
Open-Source AI: Community is the Way
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.
Building Custom GenAI Apps at H2O
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
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.
Cutting Edge Tricks from LLM Papers
Sanyam Bhutani, Sr Data Scientist, H2O.ai
H2O Open Source GenAI World SF 2023
Practitioner's Guide to LLMs: Exploring Use Cases and a Glimpse Beyond Curren...
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.
Open Source h2oGPT with Retrieval Augmented Generation (RAG), Web Search, and...
- Jon McKinney, Director of Research, H2O.ai
- Arno Candel, Chief Technology Officer, H2O.ai
H2O Open Source GenAI World SF 2023
KGM Mastering Classification and Regression with LLMs: Insights from Kaggle C...
Philipp Singer, Senior Principal Data Scientist, H2O.ai
H2O Open Source GenAI World SF 2023
LLM Interpretability
Kim Montgomery, Principal Data Scientist, H2O.ai
H2O Open Source GenAI World SF 2023
Never Reply to an Email Again
Luiz Pizzato, Executive Manager Artificial Intelligence, Commonwealth Bank
H2O Open Source GenAI World SF 2023
Introducción al Aprendizaje Automatico con H2O-3 (1)
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...
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...
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)
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GenAISummit 2024 May 28 Sri Ambati Keynote: AGI Belongs to The Community in O...
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Generative AI Masterclass - Model Risk Management.pptx
Generative AI Masterclass - Model Risk Management.pptx
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Open Source h2oGPT with Retrieval Augmented Generation (RAG), Web Search, and...
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Introducción al Aprendizaje Automatico con H2O-3 (1)
Introducción al Aprendizaje Automatico con H2O-3 (1)
From Rapid Prototypes to an end-to-end Model Deployment: an AI Hedge Fund Use...
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AI Foundations Course Module 1 - Shifting to the Next Step in Your AI Transfo...
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Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
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The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
DevOps and Testing slides at DASA Connect
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf
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LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdf
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Essentials of Automations: The Art of Triggers and Actions in FME
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
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Transcript: Selling digital books in 2024: Insights from industry leaders - T...
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Enhancing Performance with Globus and the Science DMZ
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar
PHP Frameworks: I want to break free (IPC Berlin 2024)
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
The Art of the Pitch: WordPress Relationships and Sales
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
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Quantum Computing: Current Landscape and the Future Role of APIs
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UiPath Community Day Dubai: AI at Work..
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
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Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™
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Pushing the limits of ePRTC: 100ns holdover for 100 days
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Essentials of Automations: The Art of Triggers and Actions in FME
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PHP Frameworks: I want to break free (IPC Berlin 2024)
PHP Frameworks: I want to break free (IPC Berlin 2024)
The Art of the Pitch: WordPress Relationships and Sales
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H2O AutoML roadmap - Ray Peck
- 1. H2O AutoML Roadmap 2016.10 Raymond Peck Director of Product Engineering, H2O.ai rpeck@h2o.ai © H2O.ai, 2016 1
- 2. What Will We Cover? • What is AutoML? • What is the roadmap for H2O AutoML? © H2O.ai, 2016 2
- 3. What is AutoML? H2O AutoML automates parts of data preparation and model training in order to help both Machine Learning / Data Science experts and complete novices. Other AutoML projects concentrate on novices. © H2O.ai, 2016 3
- 4. Outside AutoML Projects • auto-sklearn • AutoCompete • TPOT • DataRobot • Automatic Statistician • BigML • et al... © H2O.ai, 2016 4
- 5. Who is the Target Audience? • "Big green button" for novice users such as software developers and business analysts; • Iterative, interactive use and controls for expert users: • Machine Learning experts • Descriptive Data Scientists © H2O.ai, 2016 5
- 6. What Are the Pieces? • data cleaning • feature engineering / feature generation • feature selection • for both the original and generated features • model hyperparameter tuning • automatic smart ensemble generation © H2O.ai, 2016 6
- 7. Prior Work @ H2O • ensembles (stacking), from Erin LeDell • random hyperparameter search with automatic stopping, from Raymond Peck • some dataset characterization and feature engineering, from Spencer Aiello • hyperopt Bayesian hyperparameter optimization, from Abhishek Malali © H2O.ai, 2016 7
- 8. Current Work • random hyperparameter search with parameter values based on open datasets • moving ensembles into the back end • working on basic metalearning for hyperparameter vectors, starting with 140 OpenML datasets © H2O.ai, 2016 8
- 9. Future Work • feature selection • feature engineering for IID data • Bayesian hyperparameter search with warm start • feature engineering for non-IID data, e.g. time series • iterate w/ larger datasets that are typical for our customers • distribution guesser for regression © H2O.ai, 2016 9
- 10. How Do We Evaluate Our Work? • public datasets from • OpenML • ChaLearn AutoML challenge • Kaggle • our own Data Scientists' work with customer datasets • customer feedback (soon) © H2O.ai, 2016 10
- 11. Data Cleaning • outlier analysis (with user feedback) • sentinel value detection • as a side-effect of outlier analysis • type-based heuristics (e.g., 999999, 1970.01.01) • identifier detection (e.g., customer ID) • smart imputation © H2O.ai, 2016 11
- 12. Feature Generation We will be using several techniques including: • type-based heuristics • date/time expansion • log and other transforms of numerics • interactions (product, ratio, etc) • feature generation with Deep Learning deepfeatures() • clustering © H2O.ai, 2016 12
- 13. Feature Selection We will be evaluating several techniques including: • Mutual Information (non-linear correlation) • variable importance from GBM and Deep Learning • PCA • GLM with Elastic Net / LASSO Perhaps different selectors for initial data and transforms / interactions to trade off speed and the detection of non-linear relationships. © H2O.ai, 2016 13
- 14. Hyperparameter Tuning • currently do random hyperparameter search with metric-based smart stopping • hyperparameter values taken from hand-tuning 140 OpenML datasets • soon adding simple "nearest neighbors" warm start (basic metalearning) • then adding Bayesian hyperparameter optimization • possibly integrating hyperopt into the back end © H2O.ai, 2016 14
- 15. Automatic Smart Ensemble Generation • currently adding Erin LeDell's stacking / SuperLearner into the back end • initially, ensemble top N models from hyperparameter searches • optional "use original features" • smarter ensemble generation for faster scoring, less overfitting: • greedy ensemble creation • ensemble models with uncorrelated residuals © H2O.ai, 2016 15
- 16. Possible Futures • try to predict accuracy from dataset metadata • training time prediction • scoring time prediction • multiple concurrent H2O clusters for speed • freeze/thaw model training • outlier analysis with user feedback • residuals analysis with user feedback • composite models using pre-clustering step © H2O.ai, 2016 16