AI & Data
Databricks ML Associate: MLOps Workflows
MLOps is the set of engineering practices that makes machine learning development repeatable from experiment through production. In Databricks, that…
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Databricks ML Associate: Feature Engineering
Feature engineering turns raw operational data into variables that machine learning models can learn from. The technical transformations may be…
AI & Data
Databricks ML Associate: MLflow Experiment Tracking & Model Registry
Machine learning development is iterative. Engineers change training data, features, parameters, algorithms, code, and environments, and each choice can affect…
AI & Data
Databricks ML Associate: Batch vs Real-Time Model Inference
Batch and real-time inference solve different operational problems. Batch scoring processes many records on a schedule or as a dataset…
AI & Data
Databricks GenAI Engineer Associate: Vector Search for Enterprise AI
Enterprise AI applications often need to find information by meaning rather than exact keywords. Vector retrieval represents text, images, or…
AI & Data
Databricks GenAI Engineer Associate: Serving & Monitoring GenAI Models
Putting a generative AI model behind an endpoint is only the start of production operations. A reliable serving design must…
AI & Data
Databricks GenAI Engineer Associate: MLflow GenAI Evaluation
Generative AI evaluation is difficult because many important qualities are not captured by one accuracy number. A response can be…
AI & Data
Databricks GenAI Engineer Associate: Unity Catalog for GenAI
Generative AI governance must cover more than model access. Production applications depend on prompts, evaluation datasets, vector indexes, tools, functions,…
AI & Data
Databricks GenAI Engineer Associate: Building RAG Systems
Retrieval-augmented generation, or RAG, improves a generative AI application by retrieving relevant enterprise information and placing that information into the…