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Building a Fraud Detection Model with H2O AI Cloud
In a previous article [1], we discussed how machine learning could be harnessed to mitigate fraud. This time, we’ll delve into a step-by-step guide on leveraging H2O AI Cloud to construct efficient fraud detection models. We’ll tackle this process in three critical stages: build, operate, and detect. First, we’ll utilize Driverless AI in ...
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Reducing False Positives in Financial Transactions with AutoML
In an increasingly digital world, combating financial fraud is a high-stakes game. However, the systems we deploy to safeguard ourselves are raising too many false alarms, with over 90% of fraud alerts being false positives. These false positives, not only frustrating for consumers but also costly for financial institutions, can eclipse t...
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H2O.ai and Snowflake Enable Developers to Train, Deploy, and Score Containerized Software Without Compromising Data Security
H2O.ai today announced its participation as a launch partner for Snowflake’s Snowpark Container Services (available in private preview), which provides our joint customers with the flexibility to train, deploy, and score models all within their Snowflake account. This further expands the ease of use for data science teams to create machin...
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AI in Insurance: Resolution Life's AI Journey with Rajesh Malla
Rajesh Malla , Head of Data Engineering – Data Platforms COE at Resolution Life insurance takes the stage at H2O World Sydney 2022 to discuss AI transformation within the insurance industry. Resolution Life is the largest life insurer in Australasia. Malla discusses the use of H2O Driverless AI to predict claim triage and other insurance ...
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AI for Good: PetFinder.my Levels Up Furry Matchmaking
Nothing tugs at the heart strings quite like a poster in your neighborhood about a missing cat or dog. For years, technology has enabled lost pets to be reunited with their families in the form of a small microchip that contains an owner’s contact information. Now some organizations are turning to emerging technology to help the millions ...
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머신러닝 자동화 솔루션 H2O Driveless AI를 이용한 뇌에서의 성차 예측
Predicting Gender Differences in the Brain Using Machine Learning Automation Solution H2O Driverless AI아동기 뇌인지 발달은 기억, 주의력, 사회성 등 고등 인지 기능에 영향을 미치고, 청소년기와 성인기의 뇌 발달로까지 이어집니다.Brain cognitive development in childhood affects higher cognitive functions such as memory, attention, and sociability, and leads to brain development in adolescence ...
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The H2O.ai Wildfire Challenge Winners Blog Series - Team PSR
Note : this is a community blog post by Team PSR – one of the H2O.ai Wildfire Challenge winners.This blog represents an experience we gained by participating in the H2O wildfire challenge. We need to mention that competing in this challenge is like a journey in a knowledge pool. For a person who is willing to get the knowledge of buildin...
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H2O.ai Tools for a Beginner
Note : this is a community blog post by Shamil Dilshan Prematunga . It was first published on Medium .Hey, this is not a deep technical blog. I’d like to share the experience I had with H2O tools when I was studying Machine Learning. As a Research Engineer, I am currently working on an area based on Telecommunication. Day by day with my e...
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H2O Integrates with Snowflake Snowpark/Java UDFs: How to better leverage the Snowflake Data Marketplace and deploy In-Database
One of the goals of machine learning is to find unknown predictive features, even hidden from subject matter experts, in datasets that might not be apparent before, and use those 3rd party features to increase the accuracy of the model.A traditional way of doing this was to try and scrape and scour distributed, stagnant data sources on th...
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Shapley summary plots: the latest addition to the H2O.ai’s Explainability arsenal
It is impossible to deploy successful AI models without taking into account or analyzing the risk element involved. Model overfitting, perpetuating historical human bias, and data drift are some of the concerns that need to be taken care of before putting the models into production. At H2O.ai, explainability is an integral part of our ML ...
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H2O AI Cloud: Democratizing AI for Every Person and Every Organization
Harnessing AI’s true potential by enabling every employee, customer, and citizen with sophisticated AI technology and easy-to-use AI applications. Democratization is an essential step in the development of AI, and AutoML technologies lie at the heart of it. AutoML tools have played a pivotal role in transforming the way we consume an...
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Successful AI: Which Comes First, the Data or the Question?
Successful AI is a business process. Even the most sophisticated models, the latest algorithms, and highly experienced AI experts cannot make AI a practical success unless it is connected to a meaningful business goal . To make that happen, you need a good interaction between those with knowledge of the business and with the AI team. But ...
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H2O Driverless AI 1.9.1: Continuing to Push the Boundaries for Responsible AI
At H2O.ai, we have been busy. Not only do we have our most significant new software launch coming up (details here ), but we also are thrilled to announce the latest release of our flagship enterprise platform H2O Driverless AI 1.9.1. With that said, let’s jump into what is new: Faster Python scoring pipelines with embedded MOJOs for r...
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Automate your Model Documentation using H2O AutoDoc
Create model documentation for Supervised learning models in H2O-3 and Scikit-Learn — in minutes.The Federal Reserve’s 2011 guidelines state that without adequate documentation, model risk assessment and management would be ineffective. A similar requirement is put forward today by many regulatory and corporate governance bodies. Thus ...
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Combining the power of KNIME and H2O.ai in a single integrated workflow
KNIME and H2O.ai , the two data science pioneers known for their open source platforms, have partnered to further democratize AI. Our approaches are about being open, transparent, and pushing the leading edge of AI. We believe strongly that AI is not for the select few but for everyone. We are taking another step in democratizing AI by ...
Read moreThe Challenges and Benefits of AutoML
Machine Learning and Artificial Intelligence have revolutionized how organizations are utilizing their data. AutoML or Automatic Machine Learning automates and improves the end-to-end data science process. This includes everything from cleaning the data, engineering features, tuning the model, explaining the model, and deploying it into p...
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5 Key Elements to Detecting Fraud Quicker With AI
The number of transactions using electronic financial instruments has been increasing by about 23% year over year. The global COVID-19 pandemic has only accelerated that process. Electronic means have become the primary vehicle of how people purchase their goods. With this sudden increase in transactions, fraud detection systems are stres...
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3 Ways to Ensure Responsible AI Tools are Effective
Since we began our journey making tools for explainable AI (XAI) in late 2016, we’ve learned many lessons, and often the hard way. Through headlines, we’ve seen others grapple with the difficulties of deploying AI systems too. Whether it’s: a healthcare resource allocation system that likely discriminated against millions of black peop...
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Accelerating AI Transformation in Healthcare
The healthcare industry is evolving rapidly with volumes of data and increasing challenges. Early adopters of AI and machine learning in the healthcare space have embraced new data-driven initiatives and are reaping the benefits not only in terms of patient care but also in their own operations. Hospitals, physicians, and laboratories can...
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Modèles NLP avec BERT
H2O Driverless AI 1.9 vient de sortir, et je vous propose une série d’articles sur les dernières fonctionnalités innovantes de cette solution d’Automated Machine Learning, en commençant par l’implémentation de BERT pour les tâches NLPBERT , ou “Bidirectional Encoder Representations from Transformers” est considéré aujourd’hui comme l’éta...
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Exploring the Next Frontier of Automatic Machine Learning with H2O Driverless AI
At H2O.ai, it is our goal to democratize AI by bridging the gap between the State-of-the-Art (SOTA) in machine learning and a user-friendly, enterprise-ready platform. We have been working tirelessly to bring the SOTA from Kaggle competitions to our enterprise platform Driverless AI since its very first release. The growing list of Driver...
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In a World Where… AI is an Everyday Part of Business
Imagine a dramatically deep voice-over saying “In a world where…” This phrase from old movie trailers conjures up all sorts of futuristic settings, from an alien “world where the sun burns cold”, a Mad Max “world without gas” to a cyborg “world of the not too distant future”.Often the epic science fiction or futuristic stories also have a...
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Deploying Models to Maximise the Impact of Machine Learning — Part 1
Introduction to the 4 key pillars of considerations for model deployment (1st part of a blog series)So you have built a machine learning (ML) model which delivers a high level of accuracy and does not overfit. What value does it have now? Well, at the moment, nothing, zero, diddly squat. There is no economic value in a machine learning mo...
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Take Your Pega CRM on the Road to AI Transformation
How well does your company know its customers and prospects? Are your people empowered with relevant information when they interact with clients? What guides your employees at every step of the customer journey? Every successful company depends on how well it can address each of these questions. Investments in Customer Relationship Manage...
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Detecting Money Laundering Networks Using H2O Driverless AI
Note: Dr. Ashrith Barthur (Principal Security Scientist, H2O.ai) and Sandip Sharma (Director of Solution Engineering, H2O.ai) will be speaking about solving money laundering and other real-world problems using machine learning at our upcoming webinar. You can grab a spot here. Artificial Intelligence has evolved from being a buzz word t...
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Insights From the New 2020 Gartner Magic Quadrant For Cloud AI Developer Services
We are excited to be named a Visionary in the new Gartner Magic Quadrant for Cloud AI Developer Services (Feb 2020), and have been recognized for both our completeness of vision and ability to execute in the emerging market for cloud-hosted artificial intelligence (AI) services for application developers. This is the second Gartner MQ tha...
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AI & ML Platforms: My Fresh Look at H2O.ai Technology
2020: A new year, a new decade, and with that, I’m taking a new and deeper look at the technology H2O.ai offers for building AI and machine learning systems. I’ve been interested in H2O.ai since its early days as a company (it was 0xdata back then) in 2014. My involvement had been only peripheral, but now I’ve begun to work with this comp...
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Interview with Patrick Hall | Machine Learning, H2O.ai & Machine Learning Interpretability
Audio Link: In this episode of Chai Time Data Science , Sanyam Bhutani interviews Patrick Hall, Sr. Director of Product at H2O.ai. Patrick has a background in Math and has completed a MS Course in Analytics.In this interview they talk all about Patrick’s journey into ML, ML Interpretability and his journey at H2O.ai, how his work has ev...
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Key Takeaways from the 2020 Gartner Magic Quadrant for Data Science and Machine Learning
We are named a Visionary in the Gartner Magic Quadrant for Data Science and Machine Learning Platforms (Feb 2020). We have been positioned furthest to the right for completeness of vision among all the vendors evaluated in the quadrant. So let’s walk you through the key strengths of our machine learning platforms. Automatic Machine Learn...
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Blink: Data to AI/ML Production Pipeline Code in Just a Few Clicks
You have the data and now want to build a really really good AI/ML model and deliver to production. There are three options available today: Write the code yourself in a Jupyter notebook/R Studio etc., for training/validation and dev-ops model handoff. You decided to do the feature engineering also. Build your own features like above,...
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Speed up your Data Analysis with Python’s Datatable package
A while ago, I did a write up on Python’s Datatable library . The article was an overview of the datatable package whose focus is on big data support and high performance. The article also compared datatable’s performance with the pandas’ library on certain parameters. This is the second article in the series with a two-fold objective: ...
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Grandmaster Series: How a Passion for Numbers Turned This Mechanical Engineer into a Kaggle Grandmaster
In conversation with Sudalai Rajkumar: A Kaggle Double Grandmaster and a Data Scientist at H2O.aiIt is rightly said that one should never seek praise. Instead, let the effort speak for itself. One of the essential traits of successful people is to never brag about their success but instead keep learning along the way. In the data science ...
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How to write a Transformer Recipe for Driverless AI
What is a transformer recipe? A transformer (or feature) recipe is a collection of programmatic steps, the same steps that a data scientist would write a code to build a column transformation. The recipe makes it possible to engineer the transformer in training and in production. The transformer recipe, and recipes, in general, provide a...
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Novel Ways To Use Driverless AI
I am biased when I write that Driverless AI is amazing, but what’s more amazing is how I see customers using it. As a Sales Engineer, my job has been to help our customers and prospects use our flagship product. In return, they give us valuable feedback and talk about how they used it. Feedback is gold to us. Driverless AI has evolved in...
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Image Tasks on H2O Driverless AI
I’d like to thank Grandmaster Yauhen Babakhin for reviewing the drafts and the very useful corrections & suggestions. Link to the video. IntroductionIn this talk Kaggle GrandMaster and Data Scientist at H2O.ai: Yauhen Babakhin shows us a few prototype demos of how DriverlessAI’s upcoming release will work with Image Data and the relat...
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Accelerate Machine Learning workflows with H2O.ai Driverless AI on Red Hat OpenShift, Enterprise Kubernetes Platform
Organizations globally are operationalizing containers and Kubernetes to accelerate Machine Learning lifecycles as these technologies provide data scientists and software developers with much needed agility, flexibility, portability, and scalability to train, test, and deploy ML models in production. Red Hat OpenShift is the industry’s mo...
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Natural Language Processing in H2O’s Driverless AI
Note: I’d like to thank Grandmaster SRK for a lot of suggestions and corrections with the writeup.Note: All images used here are from the talk. Link to the slides Link to the video Note 2: All of the discussion here is related to NLP. DriverlessAI also supports other domains that are covered in other talks and posts (releasing soon). Driv...
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Make your own AI — Add Your Game to Auto-ML Models
When Features and Algorithms compete, your Business Use Case(s) wins! H2O Driverless AI is an Automatic Feature Engineering /Machine Learning platform to build AI/ML models on tabular data. Driverless AI can build supervised learning models for Time Series forecasts, Regression , Classification , etc. It supports a myriad of built-i...
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Predicting Failures from Sensor Data using AI/ML — Part 2
This is Part 2 of the blog post series and continuation of the original post, Predicting Failures from Sensor Data using AI/ML — Part 1 .Missing Values & Data ImbalanceOne of the things to note is that the hard-disk data set has a lot of missing values across its columns. Check out the Missing Data Heat Map on the training data set — ...
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H2O Driverless AI: The Workbench for Data Science
This blog was written by Rohan Gupta and originally published here. 1. IntroductionIn today’s world, being a Data Scientist is not limited to those without technical knowledge. While it is recommended and sometimes important to know a little bit of code, you can get by with just intuitive knowledge. Especially if you’re on H2O’s Driverle...
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H2O Driverless AI Acceleration with Intel DAAL
This week at Strata NY 2019 we will be demoing a custom recipe that incorporates the Intel Data Analytics Acceleration Libraray (DAAL) algorithm into Driverless AI. This blog will provide an introduction to Intel DAAL and how the Make-Your-Own-Recipe capability extends H2O Driverless AI. If you are at Strata NY 2019, stop by the Intel bo...
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Custom recipes for Driverless AI: Prophet and pmdarima cases
Last updated: 09/23/19 H2O Driverless AI provides a great new feature called “custom recipes”. These recipes are essentially custom snippets of code which can incorporate any machine learning algorithm , any scorer/metric and any feature transformer. A user can create custom recipes using python utilizing any external library or his/her o...
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From Academia to Kaggle and H2O.ai: How a Physicist found love in Data Science
Learning and taking inspirations from others is always helpful. It makes even more sense in the Data Science realm, which is continuously being bombarded with new courses, MOOCs, and recommendations with every passing day. Not only such a lot of choices become overwhelming but also perplexing at times. With this thought in mind, we bring...
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Series ‘D’emocratize
Last month was very emotional for me and I suspect it was the same for many of my fellow Makers at H2O.ai. The news broke that H2O.ai raised its Series D funding of $72.5 million led by Goldman Sachs and Ping An. While some of my friends were ecstatic for me, I felt like a big weight had been lifted off me. The best word to describe what ...
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Predicting Failures from Sensor Data using AI/ML— Part 1
Last updated: 08/26/19 Whether it’s healthcare, manufacturing or anything that we depend on either personal or in business, Prevention of a problem is always known to be better than cure! Classic prevention techniques involve time-based checks to see how things are progressing, positively or negatively. Time-based chec...
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A Maker Data Scientist’s journey: from Sudoku to Kaggle
If you put enough smart people together in one space, good things happen. Erik Hersman One of the perks of being a part of H2O.ai is that you get to work with some of the brightest minds on the planet. Here you get to closely engage with people who have a great deal of experience, as well as expertise. One such set of specialists here ar...
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Detecting Sarcasm is difficult, but AI may have an answer
Recently, while shopping for a laptop bag, I stumbled upon a pretty amusing customer review: “This is the best laptop bag ever. It is so good that within two months of use, it is worthy of being used as a grocery bag.” The innate sarcasm in the review is evident as the user isn’t happy with the quality of the bag. However, as the sentence...
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Mitigating Bias in AI/ML Models with Disparate Impact Analysis
Everyone understands that the biggest plus of using AI/ML models is a better automation of day-to-day business decisions, personalized customer service, enhanced user experience, waste elimination, better ROI, etc. The common question that comes up often though is — How can we be sure that the AI/ML decisions are free from bias/discrimina...
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A Driverless Approach to Make Forecasting Easy — Part 1
You are from the supply chain department or in a role in charge of creating future estimates on Product Sales, Patient admission, Retail Store Staffing, Energy use, Ticket sales, etc., based on historical data. A common problem is to forecast numbers one week, 4 weeks, 6 months or 1–5 year, etc., in future — basically short term &a...
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Custom Machine Learning Recipes: The ingredients for success
Last updated: 07/23/19Machine learning is akin to cooking in several ways. A perfect dish originates from a tried-and-tested recipe, has the right combination of ingredients, and is baked at just the right temperature. Successful AI solutions work on the same principle. One needs fresh and right quality ingredients in the form of data, ...
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AI for Smarter Manufacturing
Code 3Manufacturing is a centuries old industry and has seen significant changes dating back to the first Industrial Revolution in the late 18th century. The use of conveyor belt assembly lines to replace assembly workers, newer precision robot technologies to further reduce manufacturing time, advances in ERP, historian databases, stora...
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Leads to Leases
There is such a large amount of unstructured data being produced by companies. I personally find it so interesting that there is so much meaning and hidden value in text, audio, and visual content. Until recently, much of this data would go unused. However, since the rise of machine learning and artificial intelligence, it became possibl...
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Toward AutoML for Regulated Industry with H2O Driverless AI
Predictive models in financial services must comply with a complex regime of regulations including the Equal Credit Opportunity Act (ECOA), the Fair Credit Reporting Act (FCRA), and the Federal Reserve’s S.R. 11-7 Guidance on Model Risk Management. Among many other requirements, these and other applicable regulations stipulate predictive ...
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Underwrite.ai Transforms Credit Risk Decision-Making Using AI
Determining credit has been done by traditional techniques for decades. The challenge with traditional credit underwriting is that it doesn’t take into account all of the various aspects or features of an individual’s credit ability. Underwrite.ai, a new credit startup, saw this as an opportunity to apply machine learning and AI to impro...
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The Reproductive Science Center of SF Bay Area uses AI to Treat Infertility
Having your own baby may be a dream that many people have but some cannot realize until they seek specialized help. The Reproductive Science Center of SF Bay Area is one of the pioneer organizations conducting in-vitro fertilization. They strive to produce healthy babies for their patients. However, every patient has their own set of obst...
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An Overview of Python’s Datatable package
This blog originally appeared on Towardsdatascience.com “There were 5 Exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days”: Eric Schmidt If you are an R user, chances are that you have already been using the data.ta...
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Building an Interpretable & Deployable Propensity AI/ML Model in 7 Steps…
To start with, you may have a tabular data set with a combination of: Dates/Timestamps Categorical Values Text strings Numeric Values A business sponsor wants to build a Propensity to Buy model from historical data.How many Steps does it take? Let’s find out. We are going to use H2O’s Driverless AI instance with 1 GPU (optional...
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Forrester Research recognizes H2O.ai as a leader in the New Automatic Machine Learning Wave
Today, The Forrester New Wave™ : Automation-Focused Machine Learning Solutions, Q2 2019 was published by Forrester Research. We are thrilled that this leading analyst firm recognized us as a clear leader in their Automatic Machine Learning evaluation. We could not be prouder of our unwavering strategy and hard work that we believe is prop...
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H2O.ai Automatic Machine Learning on Red Hat OpenShift Container Platform Delivers Data Science Ease and Flexibility at Scale
Last week at Red Hat Summit in Boston, Sri Ambati, CEO and Founder, demonstrated how to use our award-winning automatic machine learning platform, H2O Driverless AI , on Red Hat OpenShift Container Platform. You can watch the replay here .What we showed not only helps data scientists achieve results, it also enables them to scale their ...
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AI/ML Projects — Don’t get stymied in the last mile
Data Scientists build AI/ML models from data, and then deploy it to production – in addition to a plethora of tasks around data insights, data cleansing etc., Part of the Data Scientist job description/requirement is making models available for transparency, auditability as well as explainability for both regulators as well as internal bu...
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Hortifrut uses AI to Determine the Freshness of Blueberries
Who doesn’t love sweet, delicious blueberries?Providing a steady supply of beautiful, tasty berries to the market is no small effort and Hortifrut, based in Chile, has been growing and distributing berries for the last 30 years. Today, they are using AI to provide fresh berries to the world everyday.Hortifrut, the largest global producer ...
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H2O Driverless AI Updates
We are excited to announce the new release of H2O Driverless AI with lots of improved features.Below are some of the exciting new features we have added:Version 1.6.1 LTS (April 18, 2019) – Available here Several improvements for MLI (partial dependence plots, Shapley values) Improved documentation for model deployment, time-series ...
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AI/ML Model Scoring - What Good Looks Like in Production
One of the main reasons why we build AI/Machine Learning models is for it to be used in production to support expert decision making. Whether your business is deciding what creatives your customers should be getting on emails or determining a product recommendation for a web page, AI/Models provide relevance/context to customers to drive ...
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Machine Learning with H2O – the Benefits of VMware
This blog was originally posted by Justin Murray of VMware and can be accessed here. This brief article introduces a short 4.5 minute video that explains the reasons why VMware vSphere is a great platform for data scientists/engineers to use as their base operating platform. The video then demonstrates an example of this, showing a data...
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How to explain a model with H2O Driverless AI
The ability to explain and trust the outcome of an AI-driven business decision is now a crucial aspect of the data science journey. There are many tools in the marketplace that claim to provide transparency and interpretability around machine learning models but how does one actually explain a model? H2O Driverless AI provides robust inte...
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What is Your AI Thinking? Part 3
In the past two posts we’ve learned a little about interpretable machine learning in general. In this post, we will focus on how to accomplish interpretable machine learning using H2O Driverless AI . To review, the past two posts discussed: Exploratory data analysis (EDA) Accurate and interpretable models Global explanations Local...
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What is Your AI Thinking? Part 2
Explaining AI to the Business PersonWelcome to part 2 of our blog series: What is Your AI Thinking? We will explore some of the most promising testing methods for enhancing trust in AI and machine learning models and systems. We will also cover the best practice of model documentation from a business and regulatory standpoint.More Techniq...
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What is Your AI Thinking? Part 1
Explaining AI to the Business PersonExplainable AI is in the news, and for good reason. Financial services companies have cited the ability to explain AI-based decisions as one of the critical roadblocks to further adoption of AI for their industry . Moreover, interpretability, fairness, and transparency of data-driven decision support sy...
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Finding Clarity in the Automated Modeling Space
There is an arms race happening in Data Science and Machine Learning space. It’s the race toward automation. Granted, the questions we as Data Scientists are asked to solve for will never be automated, but many of the routine tasks will be. What are these routine tasks? They range from data ingestion to feature generation. Then we have l...
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For Today’s BI Analyst - Accelerating your AI/ML efforts with Driverless AI
Whether you are starting out as a novice data scientist or a veteran in AI and Machine Learning, modern tools can guide you in creating some of the best models from your data. Not to mention, ease of moving models to production.Also don’t forget the experienced BI Analysts in your organization, who wants to play with data science , only t...
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The Making of H2O Driverless AI - Automatic Machine Learning
It is my pleasure to share with you some never before exposed nuggets and insights from the making of H2O Driverless AI, our latest automatic machine learning product on our mission to democratize AI. This has been truly a team effort, and I couldn’t be more proud of our brilliant makers who continue to relentlessly create and innovate. T...
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Launching the Academic Program … OR ... What Made My First Four Weeks at H2O.ai so Special!
We just launched the H2O.ai Academic Program at our sold-out H2O World London. With nearly 1000 people in attendance, we received the first online sign-up forms submitted by professors and students alike. This program will massively democratize AI in academia, increasing the number of AI-skilled graduates – with both technical and busine...
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Welcome H2O.ai's Driverless AI Community!
I am very excited to announce the formation of the inaugural community for H2O Driverless AI users. The Driverless AI Community is open for anyone looking to engage with other users as well as experts from H2O.ai’s Driverless AI, Driverless AI is an award-winning automatic machine learning platform that does “AI to do AI” to solve re...
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How This AI Tool Breathes New Life Into Data Science
Ask any data scientist in your workplace. Any Data Science Supervised Learning ML/AI project will go through many steps and iterations before it can be put in production. Starting with the question of “Are we solving for a regression or classification problem?” Data Collection & Curation Are there Outliers? What is the Distribu...
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What does NVIDIA’s Rapids platform mean for the Data Science community?
Today NVIDIA announced the launch of the RAPIDS suite of software libraries to enables GPU acceleration for data science workflows and we’re excited to partner with NVIDIA to bring GPU accelerated open source technology for the machine learning and AI community. “Machine learning is transforming businesses and NVIDIA GPUs are speeding...
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Automatic Feature Engineering for Text Analytics - The Latest Addition to Our Kaggle Grandmasters' Recipes
According to Kaggle’s ‘The State of Machine Learning and Data Science ’ survey , text data is the second most used data type at work for data scientists. There are a lot of interesting text analytics applications like sentiment prediction, product categorization, document classification and so on. In the latest version (1.3) of our Driver...
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Interpretability: The missing link between machine learning, healthcare, and the FDA?
Recent advances enable practitioners to break open machine learning’s “black box”.From machine learning algorithms guiding analytical tests in drug manufacture, to predictive models recommending courses of treatment, to sophisticated software that can read images better than doctors, machine learning has promised a new world of healthcar...
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The different flavors of AutoML
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 (e.g. H2O , scikit-learn , keras ). Although these tools have made it easy to train and evaluate ma...
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How to Frame Your Business Problem for Automatic Machine Learning
Over the last several years, machine learning has become an integral part of many organizations’ decision-making at various levels. With not enough data scientists to fill the increasing demand for data-driven business processes, H2O.ai has developed a product called Driverless AI that automates several time consuming aspects of a typica...
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Time is Money! Automate Your Time-Series Forecasts with Driverless AI
Time-series forecasting is one of the most common and important tasks in business analytics. There are many real-world applications like sales, weather, stock market, energy demand, just to name a few. We strongly believe that automation can help our users deliver business value in a timely manner. Therefore, once again we translated our ...
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From Kaggle Grand Masters’ Recipes to Production Ready in a Few Clicks
Introducing Accelerated Automatic Pipelines in H2O Driverless AIAt H2O, we work really hard to make machine learning fast, accurate, and accessible to everyone. With H2O Driverless AI, users can leverage years of world-class, Kaggle Grand Masters experience and our GPU-accelerated algorithms (H2O4GPU ) to produce top quality predictive ...
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Come meet the Makers!
NVIDIA’s GPU Technology Conference (GTC) Silicon Valley, March 26-29th is the premier AI and deep learning event, providing you with training, insights, and direct access to the industry’s best and brightest. It’s where you will see the latest breakthroughs in self-driving cars, smart cities, healthcare, high-performance computing, virtu...
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How Driverless AI Prevents Overfitting and Leakage
By Marios Michailidis , Competitive Data Scientist, H2O.ai In this post, I’ll provide an overview of overfitting, k-fold cross-validation, and leakage. I’ll also explain how Driverless AI avoids overfitting and leakage.An Introduction to OverfittingA common pitfall that causes machine learning models to fail when tested in a real-world e...
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Driverless AI - Introduction, Hands-On Lab and Updates
#H2OWorld was an incredible experience. Thank you to everyone who joined us! There were so many fascinating conversations and interesting presentations. I’d love to invite you to enjoy the presentations by visiting our YouTube channel . Over the next few weeks, we’ll be highlighting many of the talks. Today I’m excited to share two prese...
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Driverless AI Blog
In today’s market, there aren’t enough data scientists to satisfy the growing demand for people in the field. With many companies moving towards automating processes across their businesses (everything from HR to Marketing), companies are forced to compete for the best data science talent to meet their needs. A report by McKinsey says th...
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