News Category: Media Coverage

Our founder and CEO shares his AutoML 2020 predictions with @kdnuggets http://bit.ly/38EzBjX @dotDataUS #automl #2020Predictions The AI/Analytics/DS/ML industry companies provided predictions for 2020 based on themes such as Data, Business, democratization of Data Science, AutoML, NLP, Cloud, and DataOps. Machine learning with models is becoming more common, but not yet a dominant framework exists. In 2020, PyTorch or Tensorflow will dominate the broader model training space. In 2020, we'll see more containers in the analytics stack, and more people will use Kubernetes for stateless applications such as web servers. Today's Hadoop platform teams will become the new foundation of the data organization, and the data, AI, and analytics teams will collaborate to derive value from the same data. In 2020, the data scientist will become a self-service analytics tool, and businesses will finally be able to access their data. Data integration enables self-service analytics, which will result in a larger volume of tasks…

Our CEO Ryohei Fujimaki, Ph.D., recently shared his insights with TechTarget’s SearchDataManagement: https://bit.ly/2qM2CsQ #datascience #machinelearning #artificialintelligence To improve machine learning, data scientists need structured data, and feature engineering is required to refine and clean that data to improve machine learning models. Data engineers can make feature engineering for machine learning processes easier by taking advantage of popular techniques and automating the operation to eliminate some grunt work. Feature engineering helps the machine learning processes by expanding and organizing the raw data set. A variable feature can influence the prediction models more than the raw data. When collecting raw data from multiple sources, bringing it into one place and storing it in a data lake or warehouse is the first step. The third step in machine learning is feature engineering, which involves validating, cleaning, and merging data to create a single source of truth for data analysis. Data engineers combine raw data to…

Our founder and CEO shares his top three trends for data professionals in 2020 in this article at TDWI: The rise of AutoML 2.0 platforms will be a significant trend in 2020, driven by AI. Investing in technologies to accelerate the data science process is necessary to build more effective models. Automation will need to automate the process of creating machine learning models. Data scientists and citizen data scientists will create many AI and ML models. Data scientists will use new tools to make more transparent and accurate models that will accelerate digital transformations. In 2020, big data will continue to be in high demand, resulting in continued challenges for businesses implementing AI and ML initiatives. In 2020, the adoption of full-cycle data science platforms will lead to more efficient use of data and a quicker time-to-market for AI. Read the full article: AI's Impact in 2020: 3 Trends to…

CRN’s Rick Whiting included Ryohei and dotData in a list of “The 10 Hottest Big Data Startups of 2019.” Related Articles

In the news, dotData (as a qualified partner) will be able to provide to customers the power of data science automation along with the benefits and capabilities of Microsoft's highly available, trusted, and scalable Azure cloud platform.  See the full article @Database Trends and Applications: "dotData is Selected by the Microsoft for Startups Program."

Ryohei Fujimaki, CEO of dotData, discusses some key AI predictions for 2020 with @HypergridBiz https://www.hypergridbusiness.com/2019/10/enterprise-ai-predictions-for-2020/

Datanami's article "On the Origin of Business Insight in a Data-Rich World" discusses how dotData algorithms can excel at finding the meaningful business insights hidden in data. During an architectural shift from on-prem Hadoop to cloud-based systems, questions abound about business insight. Big data was made affordable by the rise of Hadoop and other NoSQL technologies. The new technologies allowed thousands of companies to leverage the Hadoop clusters to a significant effect. Companies found that the new tech wasn't as easy as expected and hired data engineers to make their big data projects work. Hadoop was a breakthrough technology, but it was too big of a hurdle for the average company to overcome due to its technical complexity. The data lake phenomenon continues on AWS, Azure, and Google Cloud, where Hadoop-style engines can be used for processing data without the hassle of managing hardware. A man who survived the dot-com…

Alex Woodie from @Datanami has published another article, "Are We Asking Too Much from Citizen Data Scientists?" that includes mention of dotData. The article states: Organizations seeking data science capabilities have turned to a new wave of capable AutoML tools to jumpstart their initiatives. Forrester recently ranked DataRobot and H2O.ai as the two leading #AutoML providers, with other firms like dotData providing solid functionality in a fast-growing sector.

dotData and customer SMBC on the Explosion Of Automated #machinelearning.  Read more about it here: "dotData And The Explosion Of Automated Machine Learning" @Forbes. AutoML is an automated machine learning framework for analyzing data, generating well-fitting models, and using APIs to help deploy the model into production. AutoML is an automated machine learning tool used by two groups of people: data scientists who have training in machine learning and citizen data scientists. Data scientists may not yet be comfortable with AutoML because they don't want to use new technology. Because this new field is taking off rapidly, multiple vendors have created offerings for AutoML. Still, the companies focus on different types of users and different parts of the market. There are two primary users of AutoML: data scientists and citizen analysts. DataRobot, dotData, H2O.ai, and dotDataPy are all aimed at both groups, but dotData Enterprise is aimed at data scientists.…

Ryohei Fujimaki, CEO of dotData, recently sat down with @datanami’s Alex Woodie to discuss how #AutoML tools are beginning to emerge and make a real difference in the #datascience world.   #machinelearning #AI Machine learning tools are being used in data science to imbue intelligence into products and services. In the past few years, automated ML tools have gained popularity, including AutoML tools. Forrester says companies will have a stand-alone AutoML tool. Gartner says data scientists will be using tools to automate their tasks. Forrester analysts gave high marks to DataRobot, H2O.ai, and dotData, the three leading AutoML solutions providers. H2O.ai and DataRobot have the most customer deployments, DataRobot has the biggest funding, and dotData has solid capabilities but is still building market recognition. Fujimaki says dotData has a GUI that leads users through building machine learning models, and more advanced users use a Python interface to control the modeling process.…

Is the #financialservices industry ready for #AutoML?  dotData hosted a tech talk at the #Ai4Finance2019 conference last week where we polled attendees on successes, challenges and questions around #AI, #ML and AutoML.  Our CEO Ryohei Fujimaki discusses some of the surprising findings in an article on @Forbes Cognitive World. The audience for an hour-long tech talk on AutoML in the world of Financial Services provided their input on the topic of AutoML and the need for such solutions. 150+ people attended our presentation at a Financial Services Conference in the US. They included Data Scientists, Line of Business Managers, BI Analysts, Data Engineers, and IT Specialists. The challenges of Machine Learning and Data Science for finance companies are the significant upfront effort needed to make data ready for ML and data science, the need for model transparency due to regulatory oversight, and the need for domain expertise. Our second poll…

Our CEO Ryohei Fujimaki stresses on the significance of being able to test and retrain #AI models with @searchCIO.Read more about it here: https://searchcio.techtarget.com/tip/5-ways-CIOs-can-take-their-AI-project-to-the-next-level