SF Big Analytics event at Baidu : Part 1 Deep Learning by Prof Andrew Ng

GraphHouse and Training GNNs at Internet Scale

SF Big Analytics 2024-06-27: Confidential Containers for GPU Compute: Lift-and-Shift Strategy for AI

Evaluation of LLMs and LLM Systems: Ensuring Relevance and Effectiveness in Real-World Applications

Federated Learning: from simulation to production, cross-device and cross-silo

Change Stream Processing with Debezium and Apache Flink

Unlocking LLM Potential: From Federated Learning to Introspection

Declarative Reasoning with Timelines: The Next Step in Event Processing

NVIDIA: Accelerate Spark With RAPIDS For Cost Savings

NVIDIA RAPIDS Accelerator for Apache Spark ML

NVIDIA: Federated XGBoost - Formulation to Implementation

Data Lake to Dashboard

Fraud Detection with Graph Features and Graph Neural Networks

Time Series Data and Stream Processing Performance Tuning

Large-scale Data Process and ML Pipelines

SFBigAnalytics_20220920: DoorDash: Building Scalable Real Time Event Processing with Kafka and Flink

Fast Analytics with Presto and Alluxio

SFBigAnalytics: A Data Engineer Odyssey - The Pains Leading to Data Observability Best Practices

Modern Data Processing Pipelines

SFBigAnalytics_20220707: Getting The Most From Your Data With Open Metadata

Run Spark Applications As A Service In Large Scale On Kubernetes

SFBigAnalytics_20220607: Zookeeper vs Raft: Stateful Distributed Coordination with High Availability

SF Big Analytics_20220505: How can StarRocks outperform ClickHouse, Apache Druid and Trino

The Modern Data Team

SFBigAnalytics_20220315: Persia: Scaling DL Based Recommenders Up To 100 Trillion Parameters