Mlops
14 articles tagged with "Mlops"
LIME for ML Models: 5-Step Guide
Use LIME to explain single-model predictions: build local samples, fit a weighted surrogate, and verify stability, fidelity, and scope.
How to Build AI Streams with Kafka and Flink
Low-latency AI streams: use Kafka to ingest, Flink to build features and score, with replay, lateness handling, and exactly-once delivery.
Serverless Allocation Models For ETL And AI Workloads
Match serverless settings to each workload's tightest limit: latency for inference, throughput for ETL, dbt warehouse, joins for features.
Interpreting AutoML Results with SHAP
Explain AutoML decisions with SHAP: choose the right explainer, read global/local plots, and avoid misreading feature attributions.
Git Workflows for Data Teams
Use one Git branch model, short-lived branches with reviews and CI, map Dev/Stage/Prod, and keep notebooks and large files out of Git.
Caching with Redis: Best Practices for Engineers
Practical Redis caching guide: design keys, set TTLs with jitter, choose eviction policies, monitor, scale, and secure production caches.
How to Optimize Data Flow in Distributed ML Pipelines
Profile pipelines, optimize storage and formats, parallelize loading and shuffling, and cache to boost GPU utilization and cut costs.
Real-Time Ad Campaign Optimization with AI
AI and streaming data enable instant bid, budget, and audience adjustments to cut CPA, boost ROAS, and maintain governance.
Unified Storage with Apache Iceberg: Future Trends
Iceberg unifies streaming and historical data with metadata-driven ACID tables, time travel, and AI-ready file formats.
Structured Streaming for Live Video on Databricks
Build low-latency live video pipelines with a unified lakehouse streaming approach, efficient state stores, and medallion data layers.
Databricks Projects for Data Engineer Portfolios
Build end-to-end Databricks portfolio projects that integrate Snowflake and Airflow to showcase ML, ELT, and orchestration skills.
Databricks for Anomaly Detection in Data Pipelines
Build real-time anomaly detection pipelines in Databricks using Delta Live Tables, Unity Catalog, Isolation Forest models, and SQL alerts.