AutoML 2.0: Making AI in manufacturing simple
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  • Carl Bowen
  • Media
  • May 26, 2020

AutoML 2.0: Making AI in manufacturing simple

Published @SME_MFG: https://bit.ly/2TEOkFr — With #AutoML 2.0, firms can leverage the wealth of data at a manufacturer’s disposal, to create ML/AI algorithms in a matter of days,” our CEO Ryohei Fujimaki shared his insights with SME for this article.  #manufacturing #MachineLearning #DataScience #ArtificialIntelligence #ML and AI

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  • Carl Bowen
  • Media
  • May 4, 2020

AI-FastStart Launch – covered by RT Insights

#ICYMI: Last week, we unveiled our new all-inclusive bundle of #technology and services, dotData AI-FastStart, which is designed to empower #BusinessIntelligence teams. Learn more via @RTInsights: https://bit.ly/3c3twyC #AI #DataScience #MachineLearning

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  • Carl Bowen
  • Media
  • April 30, 2020

The Coolest Data Science And Machine Learning Tool Companies Of The 2020 Big Data 100

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.

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  • Carl Bowen
  • Media
  • April 24, 2020

Why Your Company Needs White-Box Models in Enterprise Data Science

Our CEO Ryohei Fujimaki, Ph.D., explains the benefits of white-box models in Enterprise Data Science @AIWorldExpo #AI Trends: https://bit.ly/2Kx4VGt

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  • Carl Bowen
  • Media
  • March 27, 2020

How to Optimize Hyperparameter Tuning for Machine Learning Models

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

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  • Sachin Andhare
  • Blog
  • March 26, 2020

Shattering 5 Misconceptions about Automated Machine Learning

Ask data engineers about the most frustrating part of their job and the answer will most likely include “data preparation.”  Talk to a data scientist about the AI/ML workflow and what bogs them down, the answer invariably will be feature engineering. 

Analytics and data science leaders are well aware of the limitations of current AI/ML development platforms. They often lament about their team’s ability to only manage a few projects per year. BI leaders, on the other hand, have been trying to embed predictive analytics in their dashboards but face the daunting task of learning how to build AI/ML models. Automated machine learning (AutoML) was built specifically to address some of the challenges of data science – the underlying practice at the heart of both problems. 

Like every new technology, there is a lot of confusion surrounding AutoML. Here are the top 5 misconceptions about AutoML:

    1. AutoML means selecting the algorithms and building ML models automatically: In the early days of AutoML, the focus was on building and validating models.  But the next generation AutoML 2.0 platforms include end-to-end automation and are able to do much more –  from data preparation, feature engineering to building and deploying models in production. These new platforms are helping development teams reduce the time required to build and deploy ML models from months to days.  AutoML 2.0 platforms address hundreds of use cases and dramatically accelerate enterprise AI initiatives by making AI/ML development accessible to BI developers and data engineers, while also accelerating the work of data scientists.
    2. Feature Engineering (FE) implies selecting features once they are manually built:  FE involves exploring features, generating and selecting the best features using relational, transactional, temporal, geo-locational or text data across multiple tables. Many traditional AutoML platforms require data science teams to generate features manually, a very time-consuming process that requires a lot of domain knowledge. AutoML 2.0 platforms provide AI-powered FE that enables any user to automatically build the right features, test hypotheses and iterate rapidly. FE automation solves the biggest pain point in data science.
    3. Traditional AutoML platforms can ingest raw data from enterprise data sources to build ML pipelines:  A typical enterprise data architecture includes master data preparation tools designed for data cleansing, formatting and standardization before the data is stored in data lakes and data marts for further analysis. This processed data requires further manipulation that is specific to AI/ML pipelines including additional table joining and further data prep and cleansing. Traditional AutoML platforms require data engineers to write SQL code and perform manual joins to complete these remaining tasks. AutoML 2.0 platforms, on the other hand, perform automatic data pre-processing to help with profiling, cleansing, missing value imputation and outlier filtering, and help discover complex relationships between tables creating a single flat-file format ready for ML consumption.
    4. Model Accuracy is more important than feature transparency and explanation:  This depends on the use-case and there needs to be a balance between accuracy and interpretability. Many ML platforms and data scientists create complex features that are based on non-linear mathematical transformations. These features, however, cannot be logically explained. Incorporating these types of features leads to a lack of trust and resistance from business stakeholders and, ultimately, project failure. In the case of heavily regulated industries such as financial services, insurance and healthcare, feature explainability is critical.
    5. AutoML is not for BI teams and requires a data science background: First generation AutoML platforms were cumbersome, lacked user experiences for BI developers and provided challenging workflows. Even today many AutoML platforms are geared towards data scientists and require a strong ML background. AutoML 2.0 has unleashed a revolution by empowering citizen data scientists – BI analysts, data engineers and business users to embark on data science projects without requiring data scientists. AutoML 2.0 is the secret weapon the BI community can leverage to build powerful predictive analytics solutions in days – instead of the months typically associated with Augmented Analytics.

Learn more about dotData:

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Why dotData

Why AutoML 2.0

 

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  • Carl Bowen
  • Media
  • December 23, 2019

What were the biggest big data topics of 2019?

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

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  • Carl Bowen
  • Media
  • December 12, 2019

How can feature engineering be streamlined for machine learning?

Our CEO Ryohei Fujimaki, Ph.D., recently shared his insights with TechTarget’s SearchDataManagement: https://bit.ly/2qM2CsQ #datascience #machinelearning #artificialintelligence

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  • Carl Bowen
  • Media
  • September 3, 2019

dotData and customer SMBC @Forbes on the Explosion Of Automated #machinelearning

dotData and customer SMBC on the Explosion Of Automated #machinelearning.  Read more about it here: “dotData And The Explosion Of Automated Machine Learning” @Forbes.

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  • Carl Bowen
  • Media
  • August 29, 2019

Repost: AutoML Tools Emerge as Data Science Difference Makers

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
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  • dotData
  • Media
  • March 12, 2019

Automating Data Science and Machine Learning for Business Insights

Data is the oil that greases the cogs of the modern machine.  But, there’s a problem.  Organizations are struggling to gain business insights from this new power.
Data: If it’s the next oil, is it renewable or toxic?
The Economist magazine famously described data as the new oil.  It certainly has the potential to grease the wheels of the digital economy.  With that potential are both opportunities and threats.  Some go further in saying that data is the new asbestos.
Read the full article on “Automating Data Science and Machine Learning for Business Insights” on Information Age, featuring  Ryohei Fujimaki, dotData CEO and founder.

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