ML

Facial Recognition whether you agree with it or not it is here to stay. Understand what the algebra is behind facial recognition, what is involved, and what kind of software there is out there.

Spark has a machine learning aspect to it and it’s called Spark MLLib. We discuss an intro into machine learning, some models, then apply some of those common machine learning models.

Join us for a session on MLOps, where we delve into the transformative practices and tools that bridge the gap between machine learning development and production deployment. Discover how MLOps enhances collaboration, reproducibility, and scalability in machine learning projects, ensuring seamless transitions from data engineering to model monitoring. Learn about the latest technologies, including Docker, Kubernetes, and MLflow, and explore real-world case studies highlighting best practices and common challenges. Whether you’re a data scientist, engineer, or manager, this session will equip you with the knowledge to streamline your ML workflows and drive impactful business outcomes.

This presentation will assume that the attendees have little to no knowledge of creating and operationalizing ML Models.

Jupyter Notebooks has been a platform for Data Analysts and Data Scientists for the last few years but it may be expanding to a more general population including students, financial analysts, and other Scientific rigors. Running a Jupyter Notebook today is just as important as running a web browser. It is an essential platform for learning, conveying information, and telling a story.

This session will focus on data governance and making data available within your enterprise. Who owns the data, how do we obtain the data, and what does governance look like?

How do we move information realtime and connect machine learning models to make decisions on our business data? This presentation goes through machine learning and Kafka tools that would help achieve that goal.

Jupyter Lab has been a platform for Data Analysts and Data Scientists for the last few years. Still, it may expand to a more general population, including students, financial analysts, and other Scientific rigors. Running a Jupyter Lab today is just as important as running a web browser. It is an essential platform for learning, conveying information, and telling a story.