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Browsing: Pipeline
metadata_dict = metadata.to_dict() diagnostic = DiagnosticReport() diagnostic.generate(real_data=real, synthetic_data=synthetic_sdv, metadata=metadata_dict, verbose=True) print(“Diagnostic score:”, diagnostic.get_score()) quality = QualityReport() quality.generate(real_data=real, synthetic_data=synthetic_sdv, metadata=metadata_dict, verbose=True) print(“Quality score:”, quality.get_score()) def show_report_details(report, title):…
Image by Author # Introduction In a retrieval-augmented generation (RAG) pipeline, embedding models are the foundation that makes retrieval work. Before a language model can answer…
How to Build an Atomic-Agents RAG Pipeline with Typed Schemas, Dynamic Context Injection, and Agent Chaining
In this tutorial, we build an advanced, end-to-end learning pipeline around Atomic-Agents by wiring together typed agent interfaces, structured prompting, and a compact retrieval layer that…
NVIDIA Researchers Introduce KVTC Transform Coding Pipeline to Compress Key-Value Caches by 20x for Efficient LLM Serving
Serving Large Language Models (LLMs) at scale is a massive engineering challenge because of Key-Value (KV) cache management. As models grow in size and reasoning capability,…
How to Build a Privacy-Preserving Federated Pipeline to Fine-Tune Large Language Models with LoRA Using Flower and PEFT
!pip -q install -U “protobuf<5” “flwr[simulation]” transformers peft accelerate datasets sentencepiece import torch if torch.cuda.is_available(): !pip -q install -U bitsandbytes import os os.environ[“RAY_DISABLE_USAGE_STATS”] = “1” os.environ[“TOKENIZERS_PARALLELISM”]…
Image by Editor # The Fragile Pipeline The gravitational pull of state of the art in modern machine learning is immense. Research teams and engineering departments…
Image by Editor # Introduction Machine learning systems are not just advanced statistics engines running on data. They are complex pipelines that touch multiple data stores,…
How to Build a Multi-Turn Crescendo Red-Teaming Pipeline to Evaluate and Stress-Test LLM Safety Using Garak
In this tutorial, we build an advanced, multi-turn crescendo-style red-teaming harness using Garak to evaluate how large language models behave under gradual conversational pressure. We implement…
A Coding Implementation to Build a Unified Apache Beam Pipeline Demonstrating Batch and Stream Processing with Event-Time Windowing Using DirectRunner
In this tutorial, we demonstrate how to build a unified Apache Beam pipeline that works seamlessly in both batch and stream-like modes using the DirectRunner. We…
How to Build a Robust Multi-Agent Pipeline Using CAMEL with Planning, Web-Augmented Reasoning, Critique, and Persistent Memory
In this tutorial, we build an advanced, end-to-end multi-agent research workflow using the CAMEL framework. We design a coordinated society of agents, Planner, Researcher, Writer, Critic,…
