Introduction: Fundamentals of LLMOps
Greetings! This self-guided course will teach you core concepts and emerging best practices for large language model operations (LLMOps). From the fundamentals to the cutting edge, this course is designed to be a useful guide and will be updated frequently given the rapidly-evolving nature of the field (sign up to receive twice-monthly updates).
Ready to get certified and prove you have the knowledge? Arize University offers parallel certifications in ML observability. Enroll here.
Who Are We?
About the authors of the course:
Jason Lopatecki is Co-Founder and CEO of Arize AI. He is a garage-to-IPO executive with an extensive background in building marketing-leading products and businesses that heavily leverage analytics. Prior to Arize, Lopatecki was co-founder and chief innovation officer at TubeMogul, where he scaled the business into a public company and eventual acquisition by Adobe. Jason has hands-on knowledge of big data architectures, programmatic advertising systems, distributed systems, and machine learning and data processing architectures. In his free time, Lopatecki tinkers with personal machine learning projects as a hobby, with a special interest in unsupervised learning and deep neural networks. He holds an electrical engineering and computer science degree from UC Berkeley.
Aparna Dhinakaran is the Co-Founder and Chief Product Officer at Arize AI. A frequent speaker at top conferences and thought leader in the space, Dhinakaran was recently named to the Forbes 30 Under 30. Before Arize, Dhinakaran was an ML engineer and leader at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michelangelo. She has a bachelor’s from UC Berkeley’s Electrical Engineering and Computer Science program, where she published research with Berkeley’s AI Research group. She is on a leave of absence from the Computer Vision PhD program at Cornell University.
Francisco Castillo Carrasco (“Kiko”) is a data scientist and engineer at Arize AI. He has an MA in applied mathematics from Arizona State University and previously did research at the von Karman Institute for Fluid Dynamics.
Sally-Ann Delucia is an ML Solutions Engineer at Arize AI. Previously, she was an engineer and solutions architect at SuperAnnotate and New York Life Insurance Company. Delucia combines deep technical expertise with a creative outlook and is always striving to develop solutions that are not only technically sound but also socially responsible. She has a Master of Science in Applied Data Science from Fairfield University.
Trevor LaViale is an ML Solutions Engineer at Arize AI. He draws from a range of experience guiding companies of all sizes through various types of integrations, and employing a variety of modeling approaches while in the real estate/financial industry. Trevor has a double bachelor’s degree in Mathematics and Finance from the University of Washington and is currently pursuing a MS in Computer Science with a focus in Artificial Intelligence at Johns Hopkins University.
Amber Roberts is a community-oriented machine learning engineer at Arize AI. Amber’s role at Arize is to help teams across all industries build ML observability into their productionalized AI environments. Previously, Amber was a product manager of AI at Splunk and the Head of Artificial Intelligence at Insight Data Science. A Carnegie Fellow, Amber has an MS in Astrophysics from the Universidad de Chile. When Amber isn’t expertly teaching ML observability best practices, you can find her playing with her two puppies, Rusty and Sully, on Florida’s warm beaches.
Hakan Tekgul is a customer-focused Machine Learning Engineer and Data Scientist who is focused on enabling AI for different machine learning teams around the world. He is currently an ML Solutions Engineer at Arize, helping Arize customers generating ML Observability insights around the globe. Prior to Arize, Hakan led the ML Solutions team at Tazi AI, a no-code ML platform provider for data scientists. Hakan studied Computer Engineering at University of Illinois and received his graduate degree on Machine Learning from Georgia Tech.