News Category: Media Coverage

In the news, Part 5 of CRN’s Big Data 100 looks at the vendors solution providers need to know in the data science and machine learning software space.  Read the full article here.

Our CEO Ryohei Fujimaki, Ph.D., explains the benefits of white-box models in Enterprise Data Science @AIWorldExpo #AI Trends: AI and Machine Learning are accelerating digital transformation initiatives across multiple industries. When fully operationalized, AI and ML enable organizations to make data-driven decisions at unprecedented speed, transparency, and accountability levels. White Box Models (WBMs) render prediction results and influencing variables easily interpretable, such as linear models and decision/regression tree models. Data scientists create complex features using complex mathematical transformations and deep learning algorithms, but such models are difficult to explain from customer behaviors. Modeling for business problems has implications for three key personas: model developers, model consumers, and business units or organizations. Transparency levels depend on the nature of the business. The lowest level is the black-box model, which cannot provide insight into the model's workings. Linear and decision tree models are White Box models. Black-Box models are harder to understand…

Ryohei Fujimaki, Ph.D., founder and CEO of dotData explains Feature Engineering and why it needs to be automated https://bit.ly/2UMwZKs  #datascience #AutoML Machine learning can help enterprises prevent fraud, find anomalies and predict customer churn. The most critical step in AI/ML is to select the right features to train AI/ML models. Features are a crucial part of the data science workflow. Feature engineering is the process of using domain knowledge and statistics to transform raw data into a format that machine learning models can use. When making predictions about customer churn, we analyze historical behavior and create hypotheses, test them, and then make predictions about customer churn. ML algorithms extract the business hypothesis from historical data, such as logistic regression, decision tree, and support vector machine. We may write many SQL-like queries to perform temporal aggregation on two tables to extract temporal user behavior patterns. Historical patterns can be the basis of a…

Ryohei Fujimaki, Ph.D., founder and CEO of dotData, discusses why enterprises must readjust hyperparameters as part of any ongoing maintenance.  Full article on TechTarget | Search Enterprise AI -  https://bit.ly/2JgevNc Optimizing the hyperparameters of an AI model comes at a cost, and experts recommend that tuning these parameters during the design process instead of retrofitting them is a way to ensure better performance and accuracy. For example, hyperparameters may need to be tuned while training a new model for a customer in California. Enterprises should make hyperparameter tuning part of their design process. If the process relies on manual effort, the AI model quality is likely to decrease over time. Zillow uses hyperparameter optimization in its development process to tune machine learning algorithms for particular use cases, improving performance. Machine learning algorithms require hyperparameter tuning for noisy data but not for clean data. It's essential to tune hyperparameters as part…

ICYMI: dotData Receives APN Machine Learning Competency Partner of the Year Award. The award recognizes dotData's rapid growth and success in the enterprise AI/ML market and its contribution to the AWS business in 2019.  Read the full article for details.

Reposted from TechTarget - Ryohei Fujimaki, Ph.D., founder and CEO of dotData, comments on how ML (Machine Learning) has significant potential for solving business problems.  Read the full article here - The Future State of Machine Learning Needs Improved Frameworks  #datascience #AutoML Machine learning is becoming a crucial part of business intelligence, but it's still difficult to access. Programmers and analysts must create machine learning models without assistance, and this is not yet possible. In the current state of machine learning, automated machine learning refers to frameworks that automate pieces of the machine learning process. Machine learning systems need to know how to work with data and find features in the data to provide accurate responses. Data analysts have to create initial data sets for machine learning applications using artificial intelligence to assist analysts in finding features in the data. Machine learning is more complex than RDBMS, especially in vision…

“When MS&AD Insurance Group Holdings needed to create an AI-driven recommendation system for their MSI subsidiary, they turned to AutoML 2.0 to help drive results and achieve their goals…”

From TechTarget: Our CEO, Ryohei Fujimaki, Ph.D., comments on how automation will maximize their current data science team.  Read the full article at 5 Ways Enterprises Adapt to the Data Scientist Shortage.    Because of the shortage of data scientists, organizations are taking different approaches to hire, train, and retain data professionals. Data scientists typically do operational tasks to improve data quality, but this is changing. Automation can significantly simplify model development and operationalization and help companies maximize their AI and ML investments. Just-in-time data engineering is a promising development in data science automation that data science teams can automate. The data scientists shortage is being dealt with by adding data management, modeling automation, and democratizing data resources via self-service analytics. To get more out of their limited data experts, companies are exploring new team strategies. Many companies respond to the scarcity of data scientists by hiring external consultants or…

We’re excited to share that dotData was just named to CRN’s list of the "10 Hot Big Data Companies To Watch In 2020.” To learn more about our work in data science automation and our inclusion in the list, read it here: https://bit.ly/35Z9lir

The discussion about interpretability vs. explainability should start with why interpretability and explainability are important for various individuals,” our CEO Ryohei Fujimaki told @sEnterpriseAI's @glawton: https://bit.ly/2ZAh5VT #ArtificialIntelligence #MachineLearning AI explainability and AI interpretability are distinctions often used interchangeably but with very different applications. Explainable AI allows users to understand the models and can pass down the explanation to users. The distinction between interpretability and explainability is essential for various individuals. Interpretability applies to rules-based algorithms, and explainability applies to black-box deep learning algorithms. For users, developers, and C-suite members, providing explanations for AI/machine learning models boosts confidence in the models. Transparent models of machine learning can help make sense of the data, while explainable models can make sense of black-box models. Black box models might look like a word cloud and weigh positive words more highly than negative words. Discussing how AI-UX fits into explainable vs. interpretable AI shifts focus…

What were the biggest big data topics of 2019? CRN’s Rick Whiting shares a list of the top 10 topics, including the explosion of automated machine learning software companies such as dotData. Read it here: https://bit.ly/2PPwM84 #bigdata #autoML #MachineLearning #AI

  From Microsoft for Startups blog ..."Their solution automates the complete data science process from raw business data, through feature engineering, to machine learning." Because they're few and far between, data scientists are called rock stars of AI and machine learning. Data scientists bring the skills required for successful data science projects, including working with complex mathematical models. DotData's solution for automating data science enables businesses large and small to accelerate their data science projects. Dr. Fujimaki is a data science research fellow who founded dotData, a data science platform for enterprise clients. There are other solutions out there for machine learning, but dotData provides a full-cycle data science solution. AI-based feature engineering allows for faster time to value and a shorter project turnaround. As a startup, dotData is engaged with Microsoft for Startups to get their products in front of customers and provide them with resources to support marketing and…