MLA 009 Charting and Visualization Tools for Data Science
Release Date: 11/06/2018
MLG 036 Autoencoders
Machine Learning Guide
Auto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encoders, extend these concepts for applications in generative modeling, interpretability, and synthetic data generation. Links Notes and resources at - stay healthy & sharp while you learn & code Build the future of multi-agent software with . Thanks to from for recording...
info_outline
MLG 035 Large Language Models 2
Machine Learning Guide
At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as...
info_outline
MLG 034 Large Language Models 1
Machine Learning Guide
Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex...
info_outline
MLA 024 Code AI MCP Servers, ML Engineering
Machine Learning Guide
Tool use in code AI agents allows for both in-editor code completion and agent-driven file and command actions, while the Model Context Protocol (MCP) standardizes how these agents communicate with external and internal tools. MCP integration broadens the automation capabilities for developers and machine learning engineers by enabling access to a wide variety of local and cloud-based tools directly within their coding environments. Links Notes and resources at stay healthy & sharp while you learn & code Tool Use in Code AI Agents Code AI agents offer two primary modes of...
info_outline
MLA 023 Code AI Models & Modes
Machine Learning Guide
Gemini 2.5 Pro currently leads in both accuracy and cost-effectiveness among code-focused large language models, with Claude 3.7 and a DeepSeek R1/Claude 3.5 combination also performing well in specific modes. Using local open source models via tools like Ollama offers enhanced privacy but trades off model performance, and advanced workflows like custom modes and fine-tuning can further optimize development processes. Links Notes and resources at stay healthy & sharp while you learn & code Model Current Leaders According to the (as of April 12, 2025), leading...
info_outline
MLA 022 Code AI: Cursor, Cline, Roo, Aider, Copilot, Windsurf
Machine Learning Guide
Vibe coding is using large language models within IDEs or plugins to generate, edit, and review code, and has recently become a prominent and evolving technique in software and machine learning engineering. The episode outlines a comparison of current code AI tools - such as Cursor, Copilot, Windsurf, Cline, Roo Code, and Aider - explaining their architectures, capabilities, agentic features, pricing, and practical recommendations for integrating them into development workflows. Links Notes and resources at stay healthy & sharp while you learn & code Definition and...
info_outline
MLG 033 Transformers
Machine Learning Guide
Links: Notes and resources at 3Blue1Brown videos: stay healthy & sharp while you learn & code audio/video editing with AI power-tools Background & Motivation RNN Limitations: Sequential processing prevents full parallelization—even with attention tweaks—making them inefficient on modern hardware. Breakthrough: “Attention Is All You Need” replaced recurrence with self-attention, unlocking massive parallelism and scalability. Core Architecture Layer Stack: Consists of alternating self-attention and feed-forward (MLP) layers, each wrapped...
info_outline
MLA 021 Databricks: Cloud Analytics and MLOps
Machine Learning Guide
Databricks is a cloud-based platform for data analytics and machine learning operations, integrating features such as a hosted Spark cluster, Python notebook execution, Delta Lake for data management, and seamless IDE connectivity. Raybeam utilizes Databricks and other ML Ops tools according to client infrastructure, scaling needs, and project goals, favoring Databricks for its balanced feature set, ease of use, and support for both startups and enterprises. Links Notes and resources at stay healthy & sharp while you learn & code Raybeam and Databricks Raybeam is a...
info_outline
MLA 020 Kubeflow and ML Pipeline Orchestration on Kubernetes
Machine Learning Guide
Machine learning pipeline orchestration tools, such as SageMaker and Kubeflow, streamline the end-to-end process of data ingestion, model training, deployment, and monitoring, with Kubeflow providing an open-source, cross-cloud platform built atop Kubernetes. Organizations typically choose between cloud-native managed services and open-source solutions based on required flexibility, scalability, integration with existing cloud environments, and vendor lock-in considerations. Links Notes and resources at stay healthy & sharp while you learn & code - Data Scientist...
info_outline
MLA 019 Cloud, DevOps & Architecture
Machine Learning Guide
The deployment of machine learning models for real-world use involves a sequence of cloud services and architectural choices, where machine learning expertise must be complemented by DevOps and architecture skills, often requiring collaboration with professionals. Key concepts discussed include infrastructure as code, cloud container orchestration, and the distinction between DevOps and architecture, as well as practical advice for machine learning engineers wanting to deploy products securely and efficiently. Links Notes and resources at stay healthy & sharp while you learn...
info_outlinePython charting libraries - Matplotlib, Seaborn, and Bokeh - explaining, their strengths from quick EDA to interactive, HTML-exported visualizations, and clarifies where D3.js fits as a JavaScript alternative for end-user applications. It also evaluates major software solutions like Tableau, Power BI, QlikView, and Excel, detailing how modern BI tools now integrate drag-and-drop analytics with embedded machine learning, potentially allowing business users to automate entire workflows without coding.
Links
- Notes and resources at ocdevel.com/mlg/mla-9
- Try a walking desk stay healthy & sharp while you learn & code
Core Phases in Data Science Visualization
- Exploratory Data Analysis (EDA):
- EDA occupies an early stage in the Business Intelligence (BI) pipeline, positioned just before or sometimes merged with the data cleaning (“munging”) phase.
- The outputs of EDA (e.g., correlation matrices, histograms) often serve as inputs to subsequent machine learning steps.
Python Visualization Libraries
1. Matplotlib
- The foundational plotting library in Python, supporting static, basic chart types.
- Requires substantial boilerplate code for custom visualizations.
- Serves as the core engine for many higher-level visualization tools.
- Common EDA tasks (like plotting via
.corr(),.hist(), and.scatter()methods on pandas DataFrames) depend on Matplotlib under the hood.
2. Pandas Plotting
- Pandas integrates tightly with Matplotlib and exposes simple, one-line commands for common plots (e.g.,
df.corr(),df.hist()). - Designed to make quick EDA accessible without requiring detailed knowledge of Matplotlib’s verbose syntax.
3. Seaborn
- A high-level wrapper around Matplotlib, analogous to how Keras wraps TensorFlow.
- Sets sensible defaults for chart styles, fonts, colors, and sizes, improving aesthetics with minimal effort.
- Importing Seaborn can globally enhance the appearance of all Matplotlib plots, even without direct usage of Seaborn’s plotting functions.
4. Bokeh
- A powerful library for creating interactive, web-ready plots from Python.
- Enables user interactions such as hovering, zooming, and panning within rendered plots.
- Exports visualizations as standalone HTML files or can operate as a server-linked app for live data exploration.
- Supports advanced features like cross-filtering, allowing dynamic slicing and dicing of data across multiple axes or columns.
- More suited for creating reusable, interactive dashboards rather than quick, one-off EDA visuals.
5. D3.js
- Unlike previous libraries, D3.js is a JavaScript framework for creating complex, highly customized data visualizations for web and mobile apps.
- Used predominantly on the client-side to build interactive front-end graphics for end users, not as an EDA tool for analysts.
- Common in production-grade web apps, but not typically part of a Python-based data science workflow.
Dedicated Visualization and BI Software
Tableau
- Leading commercial drag-and-drop BI tool for data visualization and dashboarding.
- Connects to diverse data sources (CSV, Excel, databases), auto-detects column types, and suggests default chart types.
- Users can interactively build visualizations, cross-filter data, and switch chart types without coding.
Power BI
- Microsoft’s BI suite, similar to Tableau, supporting end-to-end data analysis and visualization.
- Integrates data preparation, visualization, and increasingly, built-in machine learning workflows.
- Focused on empowering business users or analysts to run the BI pipeline without programming.
QlikView
- Another major BI offering is QlikView, emphasizing interactive dashboards and data exploration.
Excel
- Still widely used for basic EDA and visualizations directly on spreadsheets.
- Offers limited but accessible charting tools for histograms, scatter plots, and simple summary statistics.
- Data often originates from Excel/CSV files before being ingested for further analysis in Python/pandas.
Trends & Insights
- Workflow Integration: Modern BI tools are converging, adding both classic EDA capabilities and basic machine learning modeling, often through a code-free interface.
- Automation Risks and Opportunities: As drag-and-drop BI tools increase in capabilities (including model training and selection), some data science coding work traditionally required for BI pipelines may become accessible to non-programmers.
- Distinctions in Use:
- Python libraries (Matplotlib, Seaborn, Bokeh) excel in automating and scripting EDA, report generation, and static analysis as part of data pipelines.
- BI software (Tableau, Power BI, QlikView) shines for interactive exploration and democratized analytics, integrated from ingestion to reporting.
- D3.js stands out for tailored, production-level, end-user app visualizations, rarely leveraged by data scientists for EDA.
Key Takeaways
- For quick, code-based EDA: Use Pandas’ built-in plotters (wrapping Matplotlib).
- For pre-styled, pretty plots: Use Seaborn (with or without direct API calls).
- For interactive, shareable dashboards: Use Bokeh for Python or BI tools for no-code operation.
- For enterprise, end-user-facing dashboards: Choose BI software like Tableau or build custom apps using D3.js for total control.