Introduction to a new space: MLOps

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 realworld 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.

In this presentation, we will perform a rigorous list of what is required to be successful in the MLOps space.

Model Development
Model Packaging
Model Deployment
Model Cataloging
Model Monitoring
Model Maintainance

Then, we will discuss the technologies that we can use to piece these technologies together:

Some of the technologies we will discover include:

Airflow, Kubeflow, MLFlow
Prometheus & Grafana
TensorFlow, XGBoost, Dask, and More
Serving Models
Kafka
Hyperparameter Tuning

About Daniel Hinojosa

Daniel is a programmer, consultant, instructor, speaker, and recent author. With over 20 years of experience, he does work for private, educational, and government institutions. He is also currently a speaker for No Fluff Just Stuff tour. Daniel loves JVM languages like Java, Groovy, and Scala; but also dabbles with non JVM languages like Haskell, Ruby, Python, LISP, C, C++. He is an avid Pomodoro Technique Practitioner and makes every attempt to learn a new programming language every year. For downtime, he enjoys reading, swimming, Legos, football, and barbecuing.

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