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Maxime Armstrong and Yuhan Luo, Software Engineers at Dagster, dive into the intricacies of running AI pipelines in this engaging discussion. They explore the growing accessibility of large language models and their integration into business practices like support bots. The duo emphasizes the high costs associated with maintaining AI teams and shares strategies for cost-effective AI implementation. They also discuss the importance of observability, error management, and future challenges in balancing open-source and managed solutions, all while optimizing data handling and model training.
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Quick takeaways
- Integrating LLMs into business practices enhances efficiency but raises significant costs related to AI infrastructure and ongoing operations.
- Effective cost management in AI pipelines relies on optimization strategies and robust observability tools to minimize inefficiencies and track performance.
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Integrating AI into Business Practices
AI technologies, particularly large language models (LLMs), are increasingly being incorporated into various business operations, such as customer support and knowledge management. This integration brings notable benefits, but it also highlights rising costs associated with maintaining AI infrastructure and teams. Teams deploying these technologies must manage expenses not only during the model training phase but also throughout the ongoing operation of AI pipelines. Understanding the comprehensive cost implications, including observability and runtime expenses, is crucial for organizations adopting AI solutions.
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