Pipeline

In this tutorial, we build a complete pipeline for single-cell RNA sequencing analysis scanpy. We start by installing the required libraries and loading the PBMC 3k dataset, then perform quality …

In this tutorial, we will explore how we use fearlessly As a high-performance, Python-native data engine for building end-to-end analytical pipelines. We start by loading the real-world MNIST dataset, then …

In this tutorial, we demonstrate how to efficiently fine-tune using a large language model tasteless And QLoRA. We focus on building a stable, end-to-end supervised fine-tuning pipeline that handles common …

In this tutorial, we design an end-to-end, production-style analytics and modeling pipeline using wax Operating efficiently on millions of rows without materializing the data in memory. We generate a realistic, …

INSTANCE_I = int(np.clip(INSTANCE_I, 0, len(X_test)-1)) x = X_test.iloc(INSTANCE_I).values y_true = float(y_test.iloc(INSTANCE_I)) pred = float(model.predict((x))(0)) iv = explainer.explain(x, budget=int(BUDGET_LOCAL), random_state=0) baseline = float(getattr(iv, “baseline_value”, 0.0)) main_effects = extract_main_effects(iv, feature_names) pair_df = …

import subprocess, sys, os, json, hashlib def pip(cmd): subprocess.check_call((sys.executable, “-m”, “pip”) + cmd) pip((“uninstall”, “-y”, “pillow”, “PIL”, “torchaudio”, “colpali-engine”)) pip((“install”, “-q”, “–upgrade”, “pip”)) pip((“install”, “-q”, “pillow<12”, “torchaudio==2.8.0”)) pip((“install”, “-q”, “colpali-engine”, …

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): print(f”n===== {title} details =====”) …

We spend too much time debating about Snowflake vs. Databricks and not enough time talking about the underlying architecture. The truth is, a shiny new tool won’t save you if …

Image by author # Introduction In the retrieval-augmented generation (RAG) pipeline, the embedding model is the basis that performs the retrieval task. Before a language model can answer a question, …

What’s inside the playbook? This is not just theory. This is a strategic guide for7 frameworksWhich are defining the 2026 data scenario: Kappa Shift:Learn why treating everything as a stream …