The Modern Data Science Development Toolkit with Greg Michaelson ODSC's Ai X Podcast
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- Technology
In this engaging discussion on the modern data science development toolkit, Greg Michaelson takes you through the tools, skills, and techniques that will help you improve your end-to-end development process.
Sponsored by: https://odsc.com/
Find more ODSC lightning interviews, webinars, live trainings, certifications, bootcamps here – https://aiplus.training/
Topics:
- Tell us about your background and role at Zerve?
- What are the key emerging languages and frameworks shaping the modern data science toolkit?
- Will programming languages continue to be important in the Modern Data Science Development Toolkit or do you see them slowly being replaced by no-code, low-code, and AI code-assist tools?
- Has Automated Machine Learning (AutoML) changed the way data scientists approach model building and deployment?
- How is the data science workflow evolving? Which tools and techniques are streamlining each stage, and how are they impacting team collaboration?
- How are cloud platforms such as Google Colab transforming the data science landscape? What are the benefits and challenges of cloud-based development for modern data scientists?
- Early last year we saw Open AI roll out GPT models in quick succession with good performance. That was followed later in the year by open-source models such as LlaMa 2. - How can data scientists leverage the power of open-source tools while benefiting from the features and support offered by commercial platforms and strike that balance?
- What are the challenges and best practices in deploying and monitoring machine learning models in production environments?
- How are Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) being integrated into data science workflows, and what unique advantages do they offer
- Generative AI (GenAI) has seen significant advancements recently. How is it being used creatively in data science, and what potential does it hold for future applications?
- Looking ahead: What are the emerging trends and technologies that will redefine the data science toolkit in the next few years? How can data scientists prepare for this evolving toolkit?
Some useful links:
Learn more about Zerve here - https://www.zerve.ai/
Discover all of ODSC’s Ai X Podcast episodes here - https://odsc.com/podcast/
In this engaging discussion on the modern data science development toolkit, Greg Michaelson takes you through the tools, skills, and techniques that will help you improve your end-to-end development process.
Sponsored by: https://odsc.com/
Find more ODSC lightning interviews, webinars, live trainings, certifications, bootcamps here – https://aiplus.training/
Topics:
- Tell us about your background and role at Zerve?
- What are the key emerging languages and frameworks shaping the modern data science toolkit?
- Will programming languages continue to be important in the Modern Data Science Development Toolkit or do you see them slowly being replaced by no-code, low-code, and AI code-assist tools?
- Has Automated Machine Learning (AutoML) changed the way data scientists approach model building and deployment?
- How is the data science workflow evolving? Which tools and techniques are streamlining each stage, and how are they impacting team collaboration?
- How are cloud platforms such as Google Colab transforming the data science landscape? What are the benefits and challenges of cloud-based development for modern data scientists?
- Early last year we saw Open AI roll out GPT models in quick succession with good performance. That was followed later in the year by open-source models such as LlaMa 2. - How can data scientists leverage the power of open-source tools while benefiting from the features and support offered by commercial platforms and strike that balance?
- What are the challenges and best practices in deploying and monitoring machine learning models in production environments?
- How are Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) being integrated into data science workflows, and what unique advantages do they offer
- Generative AI (GenAI) has seen significant advancements recently. How is it being used creatively in data science, and what potential does it hold for future applications?
- Looking ahead: What are the emerging trends and technologies that will redefine the data science toolkit in the next few years? How can data scientists prepare for this evolving toolkit?
Some useful links:
Learn more about Zerve here - https://www.zerve.ai/
Discover all of ODSC’s Ai X Podcast episodes here - https://odsc.com/podcast/