The emergence of foundation models and generative AI has introduced a new era for building AI systems. Selecting the right model from a range of architectures and sizes, curating data, engineering optimal prompts, tuning models for specific tasks, grounding model outputs in real-world data, optimizing hardware – these are just a few of the novel challenges that large models introduce. Delve into the fundamental tenets of MLOps, the necessary adaptations required for generative AI, and capabilities within Vertex AI to support this new workflow.

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MySQL is the most widely used open-source relational database. However, it was designed to scale up using larger machines. When you need to scale out, you’re often faced with manual sharding. Sharding introduces operational challenges, hinders scalability, and often leads to increased infrastructure and maintenance costs. This session presents a real-world case study of a financial services company who successfully migrated its large, sharded MySQL databases to Spanner, future proofing its business for availability and cost.

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At Credit Karma, we enable financial progress for more than 100 million of our members by recommending them personalized financial products when they interact with our application. In this talk we are introducing our machine learning platform to build interactive and production model-building workflows to serve relevant financial products to Credit Karma users. Vega, Credit Karma’s Machine Learning Platform, has 3 major components: 1) QueryProcessor for feature and training data generation, backed by Google BigQuery, 2) PipelineProcessor for feature transformations, offline scoring and model-analysis, backed by Apache Beam 3) ModelProcessor for running Tensorflow and Scikit models, backed by Google AI Platform, which provides data scientists the flexibility to explore different kinds of machine learning or deep learning models, ranging from gradient boosted trees to neural network with complex structures Vega exposed a unified Python API for Feature Generation, Modeling ETL, Model Training and Model Analysis. Vega supports writing interactive notebooks and python scripts to run these components in local mode with sampled data and in cloud mode for large scale distributed computing. Vega provides the ability to chain the processors provided by data scientists through Python code to define the entire workflow. Then it automatically generates the execution plan for deploying the workflow on Apache Airflow for running offline model experiments and refreshes. Overall, with the unified python API and automated Airflow DAG generation, Vega has improved the efficiency of ML Engineering. Using Airflow we deploy more than 20K features and 100 models daily