5 Recent Data Science and AI Webinars You Need to See
Each month, ODSC has a few insightful webinars that touch on a range of issues that are important in the data science world, from use cases of machine learning models, to new techniques/frameworks, and more. This is why we want to begin highlighting them for you. So here’s a summary of a few recent webinars that you’ll want to watch.
Real-time Analytics & Built-in Machine Learning Models with a Single Database
Akmal Chaudhri, Senior Technical Evangelist at SingleStore, explores the importance of delivering real-time experiences in today’s big data industry and how data models and algorithms rely on powerful and versatile data infrastructure.
The session covers the concept of HTAP or universal storage, the impact of choosing the right database for analytics and AI/ML models, and a live demonstration of tools necessary for model building.
Low-code AI at your Fingertips
This webinar focuses on the low-code no-code revolution, and how it is making AI accessible to everyone, not just developers. Microsoft’s Ashish Bhatia, Principal Product Manager, explores how with the rise of low-code platforms, citizen developers can easily build and deploy AI models without any prior programming experience. The webinar will showcase the power of low-code AI and inspire attendees to become part of this movement.
You’ll also get to learn how to leverage AI Builder and Power Platform to drive the next generation of intelligent solutions.
Real-time Machine Learning with Hopsworks — an integrated Feature Store and Model Serving platform
In this webinar, you’ll learn about the challenges and opportunities of real-time machine learning systems, and the value they can create, as demonstrated by companies such as TikTok which is seen in best-personalized search and recommendation systems. These systems are based on real-time machine learning with a feature store, a vector database, and a model serving platform.
The webinar will also analyze the synergies of an integrated feature store and model serving platform for the operationalization of real-time ML-enabled services. The session will also cover the key MLOps principles required to ensure integrated version management for upgrading and downgrading models and the features that feed them.
Open-source Data Lake Management, Curation, and Governance for New and Growing Companies
Arjuna Chala, Associate Vice President at HPCC Systems and Special Projects, discusses the challenges associated with managing data lake technology for start-ups and rapidly-growing companies. This is due to how data lakes can become too large and complex. This makes it difficult for teams to manage them, which can negatively impact the usefulness of an organization’s data and slow down the implementation of new analytics and applications.
Arjuna showcases how the HPCC Systems Data Lake platform, which is completely free and open source, has developed powerful storage and compute capabilities that can manage massive quantities of data.
Predicting Employee Burnout at Scale
Ceridian’s Brittany Schmaling, Sr. Data Analyst, Shanmugaraja Krishnasamy Venugopal, Sr. Data Scientist, & Seng Kin Chu, Data Scientist will each discuss how they used deidentification and feature selection on employee data to create models that accurately predict employee burnout. They will cover topics such as how to define and validate a people measure like burnout, how to keep data secure when working with people-centered data, and how to leverage feature selection to identify additional features to measure burnout.
Finally, you’ll also learn about the ethical considerations of using employee data, as well as best practices for working with data to help organizations accomplish their goals.
Conclusion
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Originally posted on OpenDataScience.com
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