Foundation models, generative AI, and LLMs are all indicating that businesses are turning to data science and machine learning to create a bigger impact and more customer value.
Adapting to fast market shifts brings operational challenges, which organizations need to solve in order to stay relevant.
Whether you’re building the next ChatGPT, or an ML/AI product that will shake the world, you have to think about:
- Limiting reliance on external AI APIs and managing your own infrastructure.
- Fine-tuning models with proprietary data for your specific use cases.
- Improving models based on user feedback and model outputs.
- Monitoring model performance and costs in production.
Join Qwak’s Product Manager Guy Eshet to learn more about how to apply existing best in class MLOps techniques to build data pipelines, manage experiments and deploy new model versions.
