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

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.

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