Ai4 Finance: AutoML & Beyond - Part 2

Automated Machine Learning (AutoML) was great, but only AutoML 2.0 can deliver results exponentially by using AI. Learn how US Electrical Services applied dotData AI-FastStart to maximize resources for results.

Adding AI, Machine Learning, or Predictive Capabilities to your BI stack has typically meant finding a data scientist or, even worse, learning about data science yourself. Join dotData's CEO, Dr. Ryohei Fujimaki, as he discusses the latest advancements in Automated Machine Learning: AutoML 2.0, and how it can help any business intelligence professional add AI and ML capabilities to their dashboards in record time.

Key Takeaways:
- What is Augmented Analytics - and how can it help?
- See how adding AI/ML to your BI stack is done today, and why it fails;
- Learn how AutoML 2.0 can accelerate your predictive BI work to days,
not months.

In part 2 of our conversation about AutoML, Dr. Fujimaki discusses the role of data scientists in an AutoML workflow, as well as the four core pillars of AutoML and how your organization can build a sophisticated data science workflow that leverages AutoML.

Join our CEO, Ryohei Fujimaki PhD as he discusses the brave new world of AutoML and it’s limitations. You will learn how AutoML works, and why automating the Machine Learning selection & optimization is just part of the solution.

Aaron Cheng, PhD, dotData’s VP of Data Science discusses how you can scale your data science process using automation. Join Dr. Cheng as he discusses the challenges of data science and how the four pillars of data science automation can help you scale your data science practice without adding resources.

Join our CEO, Ryohei Fujimaki PhD as he discusses the four pillars of data science automation and how they can help you create a full-cycle AI/ML development process.

Join dotData VP of Data Science Aaron Cheng, as he explores the world of data science, AI and Machine Learning development to dive deep into the problem of ongoing failures. Aaron will explore the world of data science, the key reasons projects fail, and what you can do to mitigate those failures.