Global AI and Data Science

At our March virtual meetup (check the blog to watch the replay), we welcomed Kush R. Varshney to talk about trustworthy machine learning. When we asked him where our attendees might find opportunities to play with some of these concepts, he suggested the IBM Research Data Quality for AI API, which comes with a free trial:


If you're interested in exploring this API, we invite you to do so over the next couple of weeks and join us here on the community to share your impressions and feedback and ask and answer questions.

We got in touch with our colleagues at IBM Research in India, and they have kindly committed to making themselves available during this time and check for new replies to this thread and respond where they can.

In addition, we've set up a check-in call on Thursday, April 28 at 8am Pacific, in case you'd like to meet them and go in more depth.

In order to participate in this "community trial" and to join our April 2022 cohort, simply follow these steps:

• Go to the Data Quality for AI API website
• Find the "Start a trial or the API" button and complete the registration
• Post a comment below to introduce yourself or dive right in and start asking questions
• RSVP for the April 28 check-in call

See you soon!

------------------------------
Tim Bonnemann
------------------------------
#GlobalAIandDataScience
#GlobalDataScience

 Thanks Tim for sharing the API details. In CIO AI Guild, we are looking at enhancing our CI/CD pipelines for Data & AI projects. Had a few qns regarding the API.
1.Noticed that this API overlaps with OpenScale functionality in certain areas like Bias checks. Can you clarify this overlap? Do they both have the same underlying implementation and the API provides an abstraction for same functionality as in OpenScale for Bias check, but which can be used outside of OpenScale?
2. What happens after the trial license expires in 6 months ? For eg: if we want to use it in a CI/CD pipeline how do we proceed after the trial period?
Thanks,
Sneha

------------------------------
SNEHA VARGHESE
------------------------------

Original Message:
Sent: Wed April 13, 2022 04:03 PM
From: Tim Bonnemann
Subject: Interested in exploring the Data Quality for AI API? Join our community trial!

At our March virtual meetup (check the blog to watch the replay), we welcomed Kush R. Varshney to talk about trustworthy machine learning. When we asked him where our attendees might find opportunities to play with some of these concepts, he suggested the IBM Research Data Quality for AI API, which comes with a free trial:

Data practitioners spend a considerable amount of time in iterative pre-processing of data before it is considered to be of adequate quality for downstream machine learning tasks. Although time consuming, pre-processing is an essential step because the quality of training data directly impacts the complexity as well as accuracy of AI models. Getting insights into the quality of data before it enters a machine learning pipeline can significantly reduce model building time, streamline data preparation efforts and improve the overall reliability of the AI pipeline.

The Data Quality for AI is an integrated toolkit that provides various data profiling and quality estimation metrics to assess the quality of ingested data in a systematic and objective manner. These metrics quantify data issues as a score between 0 and 1, where 1 indicates no issues were detected. These metrics are for tabular datasets and accept the input in the form of a comma separated value file.

The step by step developer guides are available on IBM Developer's Learning Path page.

If you're interested in exploring this API, we invite you to do so over the next couple of weeks and join us here on the community to share your impressions and feedback and ask and answer questions.

We got in touch with our colleagues at IBM Research in India, and they have kindly committed to making themselves available during this time and check for new replies to this thread and respond where they can.

In addition, we've set up a check-in call on Thursday, April 28 at 8am Pacific, in case you'd like to meet them and go in more depth.

In order to participate in this "community trial" and to join our April 2022 cohort, simply follow these steps:

• Go to the Data Quality for AI API website
• Find the "Start a trial or the API" button and complete the registration
• Post a comment below to introduce yourself or dive right in and start asking questions
• RSVP for the April 28 check-in call

See you soon!

------------------------------
Tim Bonnemann
------------------------------
#GlobalAIandDataScience
#GlobalDataScience

Thanks, we'll get those answered for you shortly!

------------------------------
Tim Bonnemann
------------------------------

Original Message:
Sent: Wed April 20, 2022 01:21 PM
From: SNEHA VARGHESE
Subject: Interested in exploring the Data Quality for AI API? Join our community trial!

 Thanks Tim for sharing the API details. In CIO AI Guild, we are looking at enhancing our CI/CD pipelines for Data & AI projects. Had a few qns regarding the API.
1.Noticed that this API overlaps with OpenScale functionality in certain areas like Bias checks. Can you clarify this overlap? Do they both have the same underlying implementation and the API provides an abstraction for same functionality as in OpenScale for Bias check, but which can be used outside of OpenScale?
2. What happens after the trial license expires in 6 months ? For eg: if we want to use it in a CI/CD pipeline how do we proceed after the trial period?
Thanks,
Sneha

------------------------------
SNEHA VARGHESE

Original Message:
Sent: Wed April 13, 2022 04:03 PM
From: Tim Bonnemann
Subject: Interested in exploring the Data Quality for AI API? Join our community trial!

At our March virtual meetup (check the blog to watch the replay), we welcomed Kush R. Varshney to talk about trustworthy machine learning. When we asked him where our attendees might find opportunities to play with some of these concepts, he suggested the IBM Research Data Quality for AI API, which comes with a free trial:

Data practitioners spend a considerable amount of time in iterative pre-processing of data before it is considered to be of adequate quality for downstream machine learning tasks. Although time consuming, pre-processing is an essential step because the quality of training data directly impacts the complexity as well as accuracy of AI models. Getting insights into the quality of data before it enters a machine learning pipeline can significantly reduce model building time, streamline data preparation efforts and improve the overall reliability of the AI pipeline.

The Data Quality for AI is an integrated toolkit that provides various data profiling and quality estimation metrics to assess the quality of ingested data in a systematic and objective manner. These metrics quantify data issues as a score between 0 and 1, where 1 indicates no issues were detected. These metrics are for tabular datasets and accept the input in the form of a comma separated value file.

The step by step developer guides are available on IBM Developer's Learning Path page.

If you're interested in exploring this API, we invite you to do so over the next couple of weeks and join us here on the community to share your impressions and feedback and ask and answer questions.

We got in touch with our colleagues at IBM Research in India, and they have kindly committed to making themselves available during this time and check for new replies to this thread and respond where they can.

In addition, we've set up a check-in call on Thursday, April 28 at 8am Pacific, in case you'd like to meet them and go in more depth.

In order to participate in this "community trial" and to join our April 2022 cohort, simply follow these steps:

• Go to the Data Quality for AI API website
• Find the "Start a trial or the API" button and complete the registration
• Post a comment below to introduce yourself or dive right in and start asking questions
• RSVP for the April 28 check-in call

See you soon!

------------------------------
Tim Bonnemann
------------------------------
#GlobalAIandDataScience
#GlobalDataScience

Thanks for your questions, Sneha. 

The APIs are meant to quickly try out and understand the functionality, and contains only a subset of the functionalities. The full toolkit is available as Python library. If you are interested, we can have a discussion to explain the scope of the full toolkit and questions around each functionality. You can slack me at @hima.​​​

------------------------------
Hima Patel
------------------------------

Original Message:
Sent: Wed April 20, 2022 01:21 PM
From: SNEHA VARGHESE
Subject: Interested in exploring the Data Quality for AI API? Join our community trial!

 Thanks Tim for sharing the API details. In CIO AI Guild, we are looking at enhancing our CI/CD pipelines for Data & AI projects. Had a few qns regarding the API.
1.Noticed that this API overlaps with OpenScale functionality in certain areas like Bias checks. Can you clarify this overlap? Do they both have the same underlying implementation and the API provides an abstraction for same functionality as in OpenScale for Bias check, but which can be used outside of OpenScale?
2. What happens after the trial license expires in 6 months ? For eg: if we want to use it in a CI/CD pipeline how do we proceed after the trial period?
Thanks,
Sneha

------------------------------
SNEHA VARGHESE

Original Message:
Sent: Wed April 13, 2022 04:03 PM
From: Tim Bonnemann
Subject: Interested in exploring the Data Quality for AI API? Join our community trial!

At our March virtual meetup (check the blog to watch the replay), we welcomed Kush R. Varshney to talk about trustworthy machine learning. When we asked him where our attendees might find opportunities to play with some of these concepts, he suggested the IBM Research Data Quality for AI API, which comes with a free trial:

Data practitioners spend a considerable amount of time in iterative pre-processing of data before it is considered to be of adequate quality for downstream machine learning tasks. Although time consuming, pre-processing is an essential step because the quality of training data directly impacts the complexity as well as accuracy of AI models. Getting insights into the quality of data before it enters a machine learning pipeline can significantly reduce model building time, streamline data preparation efforts and improve the overall reliability of the AI pipeline.

The Data Quality for AI is an integrated toolkit that provides various data profiling and quality estimation metrics to assess the quality of ingested data in a systematic and objective manner. These metrics quantify data issues as a score between 0 and 1, where 1 indicates no issues were detected. These metrics are for tabular datasets and accept the input in the form of a comma separated value file.

The step by step developer guides are available on IBM Developer's Learning Path page.

If you're interested in exploring this API, we invite you to do so over the next couple of weeks and join us here on the community to share your impressions and feedback and ask and answer questions.

We got in touch with our colleagues at IBM Research in India, and they have kindly committed to making themselves available during this time and check for new replies to this thread and respond where they can.

In addition, we've set up a check-in call on Thursday, April 28 at 8am Pacific, in case you'd like to meet them and go in more depth.

In order to participate in this "community trial" and to join our April 2022 cohort, simply follow these steps:

• Go to the Data Quality for AI API website
• Find the "Start a trial or the API" button and complete the registration
• Post a comment below to introduce yourself or dive right in and start asking questions
• RSVP for the April 28 check-in call

See you soon!

------------------------------
Tim Bonnemann
------------------------------
#GlobalAIandDataScience
#GlobalDataScience

Quick reminder that you'll have an opportunity the check in with the team behind the Data Quality for AI API tomorrow morning, Thursday, April 28, from 8am Pacific Time.

Whether you already had a chance to play with the API or are still planning to do so, please feel free to join our call. The team will be available for up to one full hour, so we should be able to get any and all of your questions answered.

Cheers!

------------------------------
Tim Bonnemann
------------------------------

Original Message:
Sent: Wed April 13, 2022 04:03 PM
From: Tim Bonnemann
Subject: Interested in exploring the Data Quality for AI API? Join our community trial!

At our March virtual meetup (check the blog to watch the replay), we welcomed Kush R. Varshney to talk about trustworthy machine learning. When we asked him where our attendees might find opportunities to play with some of these concepts, he suggested the IBM Research Data Quality for AI API, which comes with a free trial:

Data practitioners spend a considerable amount of time in iterative pre-processing of data before it is considered to be of adequate quality for downstream machine learning tasks. Although time consuming, pre-processing is an essential step because the quality of training data directly impacts the complexity as well as accuracy of AI models. Getting insights into the quality of data before it enters a machine learning pipeline can significantly reduce model building time, streamline data preparation efforts and improve the overall reliability of the AI pipeline.

The Data Quality for AI is an integrated toolkit that provides various data profiling and quality estimation metrics to assess the quality of ingested data in a systematic and objective manner. These metrics quantify data issues as a score between 0 and 1, where 1 indicates no issues were detected. These metrics are for tabular datasets and accept the input in the form of a comma separated value file.

The step by step developer guides are available on IBM Developer's Learning Path page.

If you're interested in exploring this API, we invite you to do so over the next couple of weeks and join us here on the community to share your impressions and feedback and ask and answer questions.

We got in touch with our colleagues at IBM Research in India, and they have kindly committed to making themselves available during this time and check for new replies to this thread and respond where they can.

In addition, we've set up a check-in call on Thursday, April 28 at 8am Pacific, in case you'd like to meet them and go in more depth.

In order to participate in this "community trial" and to join our April 2022 cohort, simply follow these steps:

• Go to the Data Quality for AI API website
• Find the "Start a trial or the API" button and complete the registration
• Post a comment below to introduce yourself or dive right in and start asking questions
• RSVP for the April 28 check-in call

See you soon!

------------------------------
Tim Bonnemann
------------------------------
#GlobalAIandDataScience
#GlobalDataScience

Starting in ~30 minutes!

------------------------------
Tim Bonnemann
------------------------------

Original Message:
Sent: Thu April 28, 2022 12:58 AM
From: Tim Bonnemann
Subject: Interested in exploring the Data Quality for AI API? Join our community trial!

Quick reminder that you'll have an opportunity the check in with the team behind the Data Quality for AI API tomorrow morning, Thursday, April 28, from 8am Pacific Time.

Whether you already had a chance to play with the API or are still planning to do so, please feel free to join our call. The team will be available for up to one full hour, so we should be able to get any and all of your questions answered.

Cheers!

------------------------------
Tim Bonnemann

Original Message:
Sent: Wed April 13, 2022 04:03 PM
From: Tim Bonnemann
Subject: Interested in exploring the Data Quality for AI API? Join our community trial!

At our March virtual meetup (check the blog to watch the replay), we welcomed Kush R. Varshney to talk about trustworthy machine learning. When we asked him where our attendees might find opportunities to play with some of these concepts, he suggested the IBM Research Data Quality for AI API, which comes with a free trial:

Data practitioners spend a considerable amount of time in iterative pre-processing of data before it is considered to be of adequate quality for downstream machine learning tasks. Although time consuming, pre-processing is an essential step because the quality of training data directly impacts the complexity as well as accuracy of AI models. Getting insights into the quality of data before it enters a machine learning pipeline can significantly reduce model building time, streamline data preparation efforts and improve the overall reliability of the AI pipeline.

The Data Quality for AI is an integrated toolkit that provides various data profiling and quality estimation metrics to assess the quality of ingested data in a systematic and objective manner. These metrics quantify data issues as a score between 0 and 1, where 1 indicates no issues were detected. These metrics are for tabular datasets and accept the input in the form of a comma separated value file.

The step by step developer guides are available on IBM Developer's Learning Path page.

If you're interested in exploring this API, we invite you to do so over the next couple of weeks and join us here on the community to share your impressions and feedback and ask and answer questions.

We got in touch with our colleagues at IBM Research in India, and they have kindly committed to making themselves available during this time and check for new replies to this thread and respond where they can.

In addition, we've set up a check-in call on Thursday, April 28 at 8am Pacific, in case you'd like to meet them and go in more depth.

In order to participate in this "community trial" and to join our April 2022 cohort, simply follow these steps:

• Go to the Data Quality for AI API website
• Find the "Start a trial or the API" button and complete the registration
• Post a comment below to introduce yourself or dive right in and start asking questions
• RSVP for the April 28 check-in call

See you soon!

------------------------------
Tim Bonnemann
------------------------------
#GlobalAIandDataScience
#GlobalDataScience