LLMs

MLOps: Machine Learning Life Cycle

MLOps Lifecycle strings model and software development together in an unified machine learning life cycle for CI/CD/CT of ML products.

50 Public Datasets for Machine Learning Projects

Best sources to find free real-world public datasets for your machine learning and data science projects.

MLOps — the dust has not settled yet

Universe of MLOps tools and how The Big 3 (Amazon, Google, and Microsoft) think about ML lifecycle and MLOps maturity level.

Data Visualization Chart Cheatsheets

Cheatsheets and decision trees to find the best visualization for your data and purpose

Setting Up Data Collection

Examines the effort-cost spectrum for data collection approaches from do-it-yourself to fully-outsource-it w.r.t. team’s ability/budget.

Best Path for Developers to Get into Machine Learning

5 steps for devs to learn machine learning: Kaggle micro-courses & competitions, ML Bootcamp, Andrew Ng’s ML course, Neural Networks.

To be agile, or not to be, that is the question

Arguments against and for embracing Agile in data science and machine learning projects.

Model Evaluation vs. Model Testing vs. Model Explainability

Is the model’s performance score enough? Testing an ML model is not the same as software testing.

Top 10 Programming Languages Portfolio

Most popular programming languages for 2023: Top 10 programming languages and why you should have them in your portfolio.

Actionable Insights from 4 Types of Data Analytics

How to collect data and extract actionable insights from descriptive, diagnostic, predictive, and prescriptive data analytics using the drivetrain approach