Vertex AI Experiments with Ivan Nardini and Karthik Ramachandran
Hosts Anu Srivastava and Nikita Namjoshi are joined by guests Ivan Nardini and Karthik Ramachandran in a conversation about Vertex AI Experiments this week on the podcast. Vertex AI Experiments allows for easy, thorough ML experimentation and analysis of ML strategies.
Our guests start the show with a brief introduction to Vertex AI and go on to help us understand where Experiments fits in. Because building ML models takes trial and error as we figure out what architecture and data management will work best, Experiments is a handy tool that helps developers try different variations. With extensive tracking capabilities and analysis tools, developers can see what is working, what’s not, and get ideas for other things to try. Ivan tells us about the two concepts to keep in mind before using Experiments: runs, which are training configurations, and experiments, adjustments you make as you look for the best solution.
Vertex ML Metadata, a managed ML metadata tool, helps analyze Experiment runs in a graph, Ivan tells us. He takes us through an example ML model build and training using Vertex AI Experiments and other tools. He and Karthik also elaborate on the relationship between Vertex AI Experiments and Pipelines. We talk about the future of AI, including the foundational model, and some cool examples of what’s happening in the real world with Vertex AI Experiments.
Ivan Nardini
Ivan Nardini is a customer engineer specialized in ML and passionate about Developer Advocacy and MLE. He is currently collaborating and enabling Data Science developers and practitioners to define and implement MLOps on Vertex AI. He is an active contributor in Google Cloud.
Karthik Ramachandran
Karthik Ramachandran is a Product Managed on the VertexAI team. He’s been focused on developing MLOps tools like Vertex Pipelines and Experiments.
Cool things of the week
- Expanding the Google Cloud Ready - Sustainability initiative with 12 new partners blog
- Large Language Models and how they are used with Natural Language Understanding. pdf
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