In this episode of ODSC’s Ai X Podcast, join Gwendolyn D. Stripling, PhD, AI/ML Content Developer at Google Cloud and author of Low-Code AI, to discuss the importance and impact of low-code and no-code AI on the industry. In this wide-ranging interview, they’ll explore how low-code and no-code can make AI more inclusive, how businesses can effectively implement AI solutions, and real-world examples of low-code AI in practice. 

Sponsored by: https://odsc.com/ 
Find more ODSC lightning interviews, webinars, live trainings, certifications, bootcamps here – https://aiplus.training/ 

Here are the questions!


1- What is low-code and no-code AI?
2- "Low-Code AI" is a concept that seems to open up the field of artificial intelligence to a broader audience. What inspired you to write a book on this topic, and why do you believe it's important today?
3- How can low-code and no-code AI make AI more inclusive?
4- What are some examples of how low-code and no-code AI are being used to make AI more inclusive?
5- In your book, you discuss using AutoML, BigQuery ML, scikit-learn, and Keras for low-code AI solutions. Can you explain the key differences between these tools and why you chose to focus on them?
6- In your book You advocate a data-first and use-case–driven approach in "Low-Code AI." Could you elaborate on what this approach entails and how it benefits those new to machine learning and AI?
7- The book offers insights into applying machine learning models across various sectors such as retail, healthcare, financial services, energy, and telecommunications. Which sector do you think stands to gain the most from low-code AI, and why?
8- For businesses looking to implement AI solutions, the step from development to deployment can often be the most daunting. How does "Low-Code AI" assist readers in not only building but also effectively deploying their machine learning models?
9- How do you see the role of traditional data scientists and ML Engineers changing in an environment where low-code and no-code platforms are becoming more prevalent?
10- How do low-code and no-code platforms maintain a balance between simplicity for the wider audience and the complexity required for more robust, scalable AI solutions? Is there a compromise on the power or capability of the applications built?
11- Can you share some success stories or case studies where low-code or no-code AI platforms were used by non-technical individuals or teams to solve real-world problems effectively?
12- With the rise of these platforms, how do you see the role of traditional educational pathways in tech (like computer science degrees) evolving? Are we moving towards a more experience-based, portfolio-driven era for tech jobs?
13- Are there risks associated with the democratization of AI, such as the proliferation of biased algorithms, and how do low-code/no-code platforms address these concerns? How can users without deep technical knowledge ensure they're creating responsible and fair AI applications?
14- Looking ahead, how do you think the rise of low-code and no-code AI will impact the technology industry's structure? For instance, will we see a more significant presence in the freelance workforce, or will there be new types of roles within traditional tech companies?

Some useful links: 
You may find the book “Low-Code AI” here - ⁠https://a.co/d/1L85KbJ⁠

No-Code & Low-Code AI: The New Era of Inclusive Tech Development with Gwendolyn Stripling

GenAI can help data engineers become more productive, and data engineering can help GenAI drive new levels of innovation.
Published at:
https://www.eckerson.com/articles/achieving-fusion-how-genai-and-data-engineering-help-one-another

Achieving Fusion: How GenAI and Data Engineering Help One Another - Audio Blog

Data engineering is now considered a crucial job in IT as Generative AI, the hottest technology of this decade, relies on data engineers to provide accurate inputs.
Published at:
https://www.eckerson.com/articles/data-engineering-for-genai-how-to-optimize-data-pipelines-and-governance

Data Engineering for GenAI: How to Optimize Data, Pipelines, and Governance - Audio Blog

If your data team wants to implement data products, it would be wise to avoid these 12 pitfalls that can torpedo an initiative.
Published at:
https://www.eckerson.com/articles/12-pitfalls-to-avoid-when-implementing-data-products

12 Pitfalls to Avoid When Implementing Data Products - Audio Blog

This blog explores three criteria to evaluate tools that manage unstructured data pipelines for GenAI.
Published at:
https://www.eckerson.com/articles/data-engineering-for-genai-three-criteria-to-evaluate-pipeline-tools

Data Engineering for GenAI: Three Criteria to Evaluate Pipeline Tools - Audio Blog

In this episode of ODSC’s Ai X Podcast, we are joined by Francis Govers, a contributor to the design of over 30 manned and unmanned land, sea, air, and space vehicles, and an expert in robotics and autonomy. 
Francis is an Air Force veteran, spent 10 years at NASA, was a lead engineer for the International Space Station, was Deputy Chief Engineer for the US Army Future Combat Systems, participated in the DARPA Grand Challenge, and managed a Zeppelin airship. He worked on the "Yellow Line" for football and designed telemetry systems for NASCAR and IndyCar. He designed RAMSEE, the robot security guard, as CTO of Gamma 2 Robotics. As a commercial pilot, writer, artist, musician, engineer, race car nut, and designer, Francis has a serious addiction to building things that frequently get him into trouble. 
He has published 48 magazine articles and contributed to five books. He received five outstanding achievement awards from NASA, the Explorer Award by the National Space Society, recognition from Scientific American for “World Changing Ideas”, and was recognized by the Vertical Flight Society as a "Titan of Autonomy". 
SHOW TOPICS:
- The second edition of “Artificial Intelligence for Robotics” which came out in February
- Tell us about your background and key career moments
- Tell us about your book - what’s the motivation and audience?
- The growing industry of robotics startups and their appeal to investors
- What is the fundamental difference between an AI-enabled robot and a traditional robot?
- How does autonomy differ from pre-programmed robotic behaviors?
- Why is supervised learning important in AI robotics, and how is it used in this book?
- You show the reader how to build a robot in your book. What are the primary technical components (hardware and software) needed to build an AI robot?
- Tell us about Robotic Operating Systems aka ROS
- How can deep learning be used for robotics and is transfer learning applicable?
- How can we teach a robot to listen and what are the main challenges in developing a robot capable of understanding human speech?
- What is Spectral Analysis?
- Beyond just listening, how do we give a robot a personality, known as artificial personality (AP)?
- What is the purpose of Monte Carlo modeling in creating human-like interactions for the robot?
- How does the robot’s personality system enable it to hold simple conversations with children?
- Why is it important for the robot to understand the mood of its human user during interactions?
- How do robots traditionally navigate unstructured envoirnments and what new methods does AI introduce?
SHOW  NOTES:
Francis Govers: ⁠https://www.linkedin.com/in/francisxgoversiii/⁠ 
Artificial Intelligence for Robotics: ⁠https://www.packtpub.com/en-us/product/artificial-intelligence-for-robotics-9781788835442⁠ 
NASA Space Station: ⁠https://www.nasa.gov/international-space-station/⁠ 
Q-Learning: https://en.wikipedia.org/wiki/Q-learning 
 
This episode was sponsored by:  
Ai+ Training⁠ ⁠https://aiplus.training/⁠⁠ 
And created in partnership with ODSC⁠ ⁠https://odsc.com/ ⁠⁠ 
Join us at our upcoming and highly anticipated conference ODSC West in South San Francisco October 29-31.

AI for Robotics and Autonomy with Francis X. Govers III

Thrilled to share Conor Moag’s inspiring journey in data analytics! On the latest episode of Fika Bites, Conor, a data engineer at Dorset County Hospital, discusses how the National Competency Framework (NCF) provided him with clarity, direction, and the tools to excel in his career. 

Through routine self-assessment and proactive planning, Conor has successfully navigated from Band 5 to Band 6 roles and is now exploring data science and machine learning pathways!

This conversation is a fantastic reminder of the value in setting structured development goals and having supportive mentorship. Conor’s experience highlights the power of continuous learning and the NCF’s role in fostering growth and adaptability for data professionals.

Fika Bites Episode - Conor Moag

A fresh perspective on data architecture, advocating for an 'anti-monolith' approach inspired by engineering best practices.
Published at: https://www.eckerson.com/articles/good-data-architecture-anti-monolith

Good Data Architecture - Anti-Monolith - Audio Blog

In this episode of Tech Talk Today, we break down the key differences between Data Engineers and Data Scientists. While both roles play a crucial part in managing and analyzing data, their responsibilities, skill sets, and contributions vary significantly. Visit us at valuecoders.com for more insights!

Data Engineers and Data Scientists_ Defining Their Roles

This blog, the second in a series, explores the mix of infrastructure types that support modern AI.
Published at:
https://www.eckerson.com/articles/cloud-on-prem-hybrid-oh-my-where-ai-adopters-host-their-projects-and-why

Cloud, On Prem, Hybrid, Oh My! Where AI Adopters Host their Projects and Why - Audio Blog

Since our establishment in 2004, ValueCoders has been providing IT outsourcing services globally.

Listen to data and analytics leaders share the secrets of their success. Wayne Eckerson, long-time global thought leader interviews guests who run data and analytics programs at Fortune 2000 organizations around the world. Tune in to stay abreast of the latest technologies, techniques, and trends in our fast-paced industry.

With Ai X Podcast, Open Data Science Conference (ODSC) brings its vast experience in building community and its knowledge of the data science and AI fields to the podcast platform. The interests and challenges of the data science community are wide ranging. To reflect this Ai X Podcast will offer a similarly wide range of content, from one-on-one interviews with leading experts, to career talks, to educational interviews, to profiles of AI Startup Founders. Join us every two weeks to discover what’s going on in the data science community.

Find more ODSC lightning interviews, webinars, live trainings, certifications, bootcamps here - https://aiplus.training/  
Don't miss out on this exciting opportunity to expand your knowledge and stay ahead of the curve.

Stream DNA podcast seeks to highlight activities within the health data and analytics profession, aiming to engage with individuals carrying out interesting and innovative projects within the health and care ecosystem.

New tracks tagged #DataEngineering