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The past decade has seen rapid development of Artificial Intelligence (AI) and Machine Learning (ML) across different industries and for a multitude of successful use cases. However, one key challenge many businesses face for larger-scale adoption of AI and ML is that their data is often not ready for AI/ML. Automated feature engineering is a technology that aims to address the fundamental challenges of data readiness for AI.

In this talk, we will review automated feature engineering technology and discuss how data scientists can benefit from this technology to transform your data and enable AI applications.

The past decade has seen rapid development of Artificial Intelligence (AI) and Machine Learning (ML) across different industries and for a multitude of successful use cases. However, one key challenge many businesses face for larger-scale adoption of AI and ML is that their data is often not ready for AI/ML. Automated feature engineering is a technology that aims to address the fundamental challenges of data readiness for AI. In this talk, we will review automated feature engineering technology and discuss how data scientists can benefit from this technology to transform your data and enable AI applications.

To access this webinar, please register here: https://hubs.li/Q01RB3Ds0

Topic: “How Programmatic Feature Discovery Changes the Data Science Workflow”

Speaker: Dr. Aaron Cheng, VP of Data Science at dotData

As a data science practitioner with 14 years of research and industrial experience, he has held various leadership positions in spearheading new product development in the fields of data science and business intelligence. At dotData, Aaron leads the data science team in working directly with clients and solving their most challenging problems. Prior to joining dotData, he was a Data Science Principle Manager with Accenture Digital, responsible for architecting data science solutions and delivering business values for the tech industry on the West Coast. He was instrumental in the strategic expansion of Accenture Digital’s footprint in the data science market in North America. Aaron received his Ph.D. degree in Applied Physics from Northwestern University.

Abstract: The past decade has seen rapid development of Artificial Intelligence (AI) and Machine Learning (ML) across different industries and for a multitude of successful use cases. However, one key challenge many businesses face for larger-scale adoption of AI and ML is that their data is often not ready for AI/ML. Automated feature engineering is a technology that aims to address the fundamental challenges of data readiness for AI.

In this talk, we will review automated feature engineering technology and discuss how data scientists can benefit from this technology to transform your data and enable AI applications.

ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01Qnvfb0 • Code of conduct: https://odsc.com/code-of-conduct/

To access this webinar, please register here: https://hubs.li/Q01RB3Ds0

Topic: “How Programmatic Feature Discovery Changes the Data Science Workflow”

Speaker: Dr. Aaron Cheng, VP of Data Science at dotData

As a data science practitioner with 14 years of research and industrial experience, he has held various leadership positions in spearheading new product development in the fields of data science and business intelligence. At dotData, Aaron leads the data science team in working directly with clients and solving their most challenging problems. Prior to joining dotData, he was a Data Science Principle Manager with Accenture Digital, responsible for architecting data science solutions and delivering business values for the tech industry on the West Coast. He was instrumental in the strategic expansion of Accenture Digital’s footprint in the data science market in North America. Aaron received his Ph.D. degree in Applied Physics from Northwestern University.

Abstract: The past decade has seen rapid development of Artificial Intelligence (AI) and Machine Learning (ML) across different industries and for a multitude of successful use cases. However, one key challenge many businesses face for larger-scale adoption of AI and ML is that their data is often not ready for AI/ML. Automated feature engineering is a technology that aims to address the fundamental challenges of data readiness for AI.

In this talk, we will review automated feature engineering technology and discuss how data scientists can benefit from this technology to transform your data and enable AI applications.

ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01Qnvfb0 • Code of conduct: https://odsc.com/code-of-conduct/

To access this webinar, please register here: https://hubs.li/Q01RB3Ds0

Topic: “How Programmatic Feature Discovery Changes the Data Science Workflow”

Speaker: Dr. Aaron Cheng, VP of Data Science at dotData

As a data science practitioner with 14 years of research and industrial experience, he has held various leadership positions in spearheading new product development in the fields of data science and business intelligence. At dotData, Aaron leads the data science team in working directly with clients and solving their most challenging problems. Prior to joining dotData, he was a Data Science Principle Manager with Accenture Digital, responsible for architecting data science solutions and delivering business values for the tech industry on the West Coast. He was instrumental in the strategic expansion of Accenture Digital’s footprint in the data science market in North America. Aaron received his Ph.D. degree in Applied Physics from Northwestern University.

Abstract: The past decade has seen rapid development of Artificial Intelligence (AI) and Machine Learning (ML) across different industries and for a multitude of successful use cases. However, one key challenge many businesses face for larger-scale adoption of AI and ML is that their data is often not ready for AI/ML. Automated feature engineering is a technology that aims to address the fundamental challenges of data readiness for AI.

In this talk, we will review automated feature engineering technology and discuss how data scientists can benefit from this technology to transform your data and enable AI applications.

ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01Qnvfb0 • Code of conduct: https://odsc.com/code-of-conduct/

To access this webinar, please register here: https://hubs.li/Q01RB3Ds0

Topic: “How Programmatic Feature Discovery Changes the Data Science Workflow”

Speaker: Dr. Aaron Cheng, VP of Data Science at dotData

As a data science practitioner with 14 years of research and industrial experience, he has held various leadership positions in spearheading new product development in the fields of data science and business intelligence. At dotData, Aaron leads the data science team in working directly with clients and solving their most challenging problems. Prior to joining dotData, he was a Data Science Principle Manager with Accenture Digital, responsible for architecting data science solutions and delivering business values for the tech industry on the West Coast. He was instrumental in the strategic expansion of Accenture Digital’s footprint in the data science market in North America. Aaron received his Ph.D. degree in Applied Physics from Northwestern University.

Abstract: The past decade has seen rapid development of Artificial Intelligence (AI) and Machine Learning (ML) across different industries and for a multitude of successful use cases. However, one key challenge many businesses face for larger-scale adoption of AI and ML is that their data is often not ready for AI/ML. Automated feature engineering is a technology that aims to address the fundamental challenges of data readiness for AI.

In this talk, we will review automated feature engineering technology and discuss how data scientists can benefit from this technology to transform your data and enable AI applications.

ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01Qnvfb0 • Code of conduct: https://odsc.com/code-of-conduct/