- 247 Views
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How realistic is truly end-to-end LLMOps on Databricks?
Databricks is positioning the platform as a full stack for LLM development — from data ingestion → feature/embedding pipelines → fine-tuning (Mosaic AI) → evaluation → deployment (Model Serving) → monitoring (Lakehouse Monitoring).I’m curious about r...
- 247 Views
- 2 replies
- 4 kudos
- 4 kudos
Thank You @Gecofer for taking the time to share such a clear, experience-backed breakdown of where Databricks shines and where real-world LLM Ops architectures still need supporting components. Your explanation was incredibly practical and resonates ...
- 4 kudos
- 309 Views
- 1 replies
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How to Leverage Databricks for End-to-End AI Model Development
Hi everyone,I’m exploring how to use Databricks as a platform for end-to-end AI and machine learning model development, and I’d love to get insights from professionals and practitioners who have hands-on experience.Specifically, I’m curious about:Set...
- 309 Views
- 1 replies
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You can leverage Databricks for end-to-end AI model development by using its Lakehouse Platform, which unifies data engineering, analytics, and machine learning in one workspace. Start by ingesting and transforming data using Apache Spark and Delta L...
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- 296 Views
- 1 replies
- 1 kudos
Why keep both Azure OpenAI and Databricks?
Hi everyone,I’m curious to hear your thoughts on the benefits of having both Azure OpenAI and Azure Databricks within the same ecosystem.From what I can see, Databricks provides a strong foundation for data engineering, governance, and model lifecycl...
- 296 Views
- 1 replies
- 1 kudos
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Two use case I can think of is RAG:Use Databricks for vector indexing (e.g., via Delta Lake or FAISS) and Azure OpenAI for inference.Example: A chatbot that queries Databricks-hosted documents and uses GPT-4 for response generation.Agentic Workflows:...
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- 1336 Views
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Resolved! How Important is High-Quality Data Annotation in Training ML Models?
I've been exploring the foundations of building robust AI/ML systems, and I keep circling back to one crucial element — data annotation. No matter how advanced our models are, the quality of labeled data directly impacts the accuracy and performance ...
- 1336 Views
- 2 replies
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High-quality data annotation is crucial for training reliable ML models. It improves accuracy, reduces bias, saves costs, and is especially critical in sensitive fields like healthcare and autonomous driving. Clear guidelines, verification, and exper...
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