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AI Agents in Analytics Workflows: Too Early or Already Behind?

A look at how AI agents are reshaping the data analytics workflow and whether you’re ahead or behind the curve.

Advanced Feature Engineering Using Scikit-Learn Pipelines with Pandas’ ColumnTransformer and NumPy Arrays

Pandas , NumPy , and Scikit-learn .

Navigating Imbalanced Datasets with Pandas and Scikit-learn

Imbalanced datasets, where a majority of the data samples belong to one class and the remaining minority belong to others, are not that rare.

Building a Custom PDF Parser with PyPDF and LangChain

PDFs look simple — until you try to parse one. Here’s how to build your own parser.

Bridging the Gap: New Datasets Push Recommender Research Toward Real-World Scale

Publicly available datasets in recommender research currently shaping the field.

Step-by-Step Guide to Deploying Machine Learning Models with FastAPI and Docker

You've trained your machine learning model, and it's performing great on test data.

Top 7 MCP Clients for AI Tooling

The most popular clients that seamlessly and reliably work with MCP servers ranging from IDEs to chatbots and plugins.

Why You Need RAG to Stay Relevant as a Data Scientist

How retrieval-augmented generation (RAG) reduces LLM costs, minimises hallucinations, and keeps you employable in the age of AI.

Stop Writing Messy Python: A Clean Code Crash Course

Writing Python that works is easy. But writing Python that's clean, readable, and maintainable? That's what this crash course is for.

How to Optimize Language Model Size for Deployment

The rise of language models, and more specifically large language models (LLMs), has been of such a magnitude that it has permeated every aspect of modern AI applications — from chatbots and search engines to enterprise automation and coding assistants.

Loss Functions Explained: Understand the Maths in Just 2 Minutes Each

I must say, with the ongoing hype around machine learning, a lot of people jump straight to the application side without really understanding how things work behind the scenes.

10 MLOps Tools for Machine Learning Practitioners to Know

Machine learning is not just about building models.

NumPy Ninjutsu: Mastering Array Operations for High-Performance Machine Learning

Machine learning workflows typically involve plenty of numerical computations in the form of mathematical and algebraic operations upon data stored as large vectors, matrices, or even tensors — matrix counterparts with three or more dimensions.

10 Python One-Liners That Will Simplify Feature Engineering

Feature engineering is a key process in most data analysis workflows, especially when constructing machine learning models.

Best Practices for Building the AI Development Platform in Government 

By John P. Desmond, AI Trends Editor  The AI stack defined by Carnegie Mellon University is fundamental to the approach being taken by the US Army for its AI development platform efforts, according to Isaac Faber, Chief Data Scientist at the US Army AI Integration Center, speaking at the AI World Government event held in-person and virtually […]

Advance Trustworthy AI and ML, and Identify Best Practices for Scaling AI 

By John P. Desmond, AI Trends Editor   Advancing trustworthy AI and machine learning to mitigate agency risk is a priority for the US Department of Energy (DOE), and identifying best practices for implementing AI at scale is a priority for the US General Services Administration (GSA).   That’s what attendees learned in two sessions at the AI […]

Promise and Perils of Using AI for Hiring: Guard Against Data Bias 

By AI Trends Staff   While AI in hiring is now widely used for writing job descriptions, screening candidates, and automating interviews, it poses a risk of wide discrimination if not implemented carefully.  That was the message from Keith Sonderling, Commissioner with the US Equal Opportunity Commision, speaking at the AI World Government event held live and virtually in […]

Predictive Maintenance Proving Out as Successful AI Use Case 

By John P. Desmond, AI Trends Editor   More companies are successfully exploiting predictive maintenance systems that combine AI and IoT sensors to collect data that anticipates breakdowns and recommends preventive action before break or machines fail, in a demonstration of an AI use case with proven value.   This growth is reflected in optimistic market forecasts. […]