Leaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 24, 2020.
The Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms has the largest number of leaders ever. We examine the leaders and changes and trends vs previous years.
Alteryx, Data Science Platform, Databricks, Dataiku, DataRobot, Domino, Gartner, Google, H2O, IBM, Knime, Machine Learning, Magic Quadrant, MathWorks, Microsoft Azure, RapidMiner, SAS, TIBCO
- H2O Framework for Machine Learning - Jan 6, 2020.
This article is an overview of H2O, a scalable and fast open-source platform for machine learning. We will apply it to perform classification tasks.
Automated Machine Learning, AutoML, H2O, Machine Learning, Python
Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 11, 2019.
We compare Gartner 2019 MQ for Data Science, Machine Learning Platforms to its previous versions and identify notable changes for leaders and challengers, including RapidMiner, KNIME, TIBCO, Alteryx, Dataiku, SAS, and MathWorks.
Alteryx, Data Science Platform, Dataiku, DataRobot, Gartner, Google, H2O, IBM, Knime, Machine Learning, Magic Quadrant, MathWorks, Microsoft, RapidMiner, SAS, TIBCO
- Automated Machine Learning in Python - Jan 18, 2019.
An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.
Automated Machine Learning, AutoML, H2O, Keras, Machine Learning, Python, scikit-learn
- Data Science For Business: 3 Reasons You Need To Learn The Expected Value Framework - Jul 26, 2018.
This article highlights the importance of learning the expected value framework in data science, covering classification, maximization and testing.
Business, Business Value, Data Science, H2O
Gainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 27, 2018.
We compare Gartner 2018 Magic Quadrant for Data Science, Machine Learning Platforms vs its 2017 version and identify notable changes for leaders and challengers, including IBM, SAS, RapidMiner, KNIME, Alteryx, H2O.ai, and Domino.
Alteryx, Anaconda, Angoss, Data Science Platform, Domino, Gartner, H2O, IBM, Knime, Machine Learning, Magic Quadrant, RapidMiner, SAS
- Deep Learning in H2O using R - Jan 22, 2018.
This article is about implementing Deep Learning (DL) using the H2O package in R. We start with a background on DL, followed by some features of H2O's DL framework, followed by an implementation using R.
Backpropagation, Deep Learning, Gradient Descent, H2O, Machine Learning, R
Using Machine Learning to Predict and Explain Employee Attrition - Oct 4, 2017.
Employee attrition (churn) is a major cost to an organization. We recently used two new techniques to predict and explain employee turnover: automated ML with H2O and variable importance analysis with LIME.
Churn, H2O, HR, Machine Learning, Predictive Analytics, Workforce Analytics
- Why Every Company Needs a Digital Brain - Jul 11, 2017.
As emerging technologies like AI/machine learning are adopted across different parts of the business, enterprises require a “digital brain” to coordinate those efforts and generate systemic intelligence.
AI, Enterprise, H2O, Machine Learning
- Deep Learning for Internet of Things Using H2O - Apr 6, 2016.
H2O is feature-rich open source machine learning platform known for its R and Spark integration and it’s ease of use. This is an overview of using H2O deep learning for data science with the Internet of Things.
Deep Learning, H2O, Internet of Things, IoT, R
- Spark + Deep Learning: Distributed Deep Neural Network Training with SparkNet - Dec 4, 2015.
Training deep neural nets can take precious time and resources. By leveraging an existing distributed batch processing framework, SparkNet can train neural nets quickly and efficiently.
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Apache Spark, Caffe, Deep Learning, Distributed Systems, H2O, Matthew Mayo, Neural Networks
- R leads RapidMiner, Python catches up, Big Data tools grow, Spark ignites - May 25, 2015.
R is the most popular overall tool among data miners, although Python usage is growing faster. RapidMiner continues to be most popular suite for data mining/data science. Hadoop/Big Data tools usage grew to 29%, propelled by 3x growth in Spark. Other tools with strong growth include H2O (0xdata), Actian, MLlib, and Alteryx.
Actian, Apache Spark, Data Mining Software, H2O, Knime, Poll, Python, R, RapidMiner, SQL
- Interview: Arno Candel, H2O.ai on the Basics of Deep Learning to Get You Started - Jan 20, 2015.
We discuss how Deep Learning is different from the other methods of Machine Learning, unique characteristics and benefits of Deep Learning, and the key components of H2O architecture.
Apache Spark, Arno Candel, Deep Learning, H2O, Machine Learning
