mailitics

Category: Project Management

  • Stop Tuning Hyperparameters. Start Tuning Your Problem.

    Stop Tuning Hyperparameters. Start Tuning Your Problem. 80% of ML projects fail from bad problem framing, not bad models. A 5-step protocol to define the right problem before you write training code. The post Stop Tuning Hyperparameters. Start Tuning Your Problem. appeared first on Towards Data Science. Kaushik Rajan Go to original source

  • Advance Planning for AI Project Evaluation

    Advance Planning for AI Project Evaluation The work to do before the work begins The post Advance Planning for AI Project Evaluation appeared first on Towards Data Science. Stephanie Kirmer Go to original source

  • Iron Triangles: Powerful Tools for Analyzing Trade-Offs in AI Product Development

    Iron Triangles: Powerful Tools for Analyzing Trade-Offs in AI Product Development Conceptual overview and practical guidance The post Iron Triangles: Powerful Tools for Analyzing Trade-Offs in AI Product Development appeared first on Towards Data Science. Chinmay Kakatkar Go to original source

  • The Machine Learning Lessons I’ve Learned Last Month

    The Machine Learning Lessons I’ve Learned Last Month Delayed January: deadlines, downtimes, and flow times The post The Machine Learning Lessons I’ve Learned Last Month appeared first on Towards Data Science. Pascal Janetzky Go to original source

  • Why SaaS Product Management Is the Best Domain for Data-Driven Professionals in 2026

    Why SaaS Product Management Is the Best Domain for Data-Driven Professionals in 2026 How I use analytics, automation, and AI to build better SaaS The post Why SaaS Product Management Is the Best Domain for Data-Driven Professionals in 2026 appeared first on Towards Data Science. Yassin Zehar Go to original source

  • The 2026 Goal Tracker: How I Built a Data-Driven Vision Board Using Python, Streamlit, and Neon

    The 2026 Goal Tracker: How I Built a Data-Driven Vision Board Using Python, Streamlit, and Neon Designing a centralized system to track daily habits and long-term goals The post The 2026 Goal Tracker: How I Built a Data-Driven Vision Board Using Python, Streamlit, and Neon appeared first on Towards Data Science. Sabrine Bendimerad Go to…

  • Organizing Code, Experiments, and Research for Kaggle Competitions

    Organizing Code, Experiments, and Research for Kaggle Competitions Lessons and tips learned while earning a Kaggle Competition Medal The post Organizing Code, Experiments, and Research for Kaggle Competitions appeared first on Towards Data Science. Ibrahim Habib Go to original source

  • How I Used Machine Learning to Predict 41% of Project Delays Before They Happened

    How I Used Machine Learning to Predict 41% of Project Delays Before They Happened How data science can help project managers anticipate risks and save time The post How I Used Machine Learning to Predict 41% of Project Delays Before They Happened appeared first on Towards Data Science. Yassin Zehar Go to original source

  • The Machine Learning Lessons I’ve Learned This Month

    The Machine Learning Lessons I’ve Learned This Month September 2025: library or self-made, Ditto and Launchbar, reading widely and deeply The post The Machine Learning Lessons I’ve Learned This Month appeared first on Towards Data Science. Pascal Janetzky Go to original source

  • What Clients Really Ask for in AI Projects

    What Clients Really Ask for in AI Projects Managing AI projects is no walk in the park, but you have the power to make it easier for everyone The post What Clients Really Ask for in AI Projects appeared first on Towards Data Science. Ivo Bernardo Go to original source

  • Agentic AI and the Future of Python Project Management Tooling

    Agentic AI and the Future of Python Project Management Tooling Introducing a pyramid framework of evolution, accelerating and decelerating factors, and strategic recommendations for incumbents and new entrants The post Agentic AI and the Future of Python Project Management Tooling appeared first on Towards Data Science. Chinmay Kakatkar Go to original source

  • Tips for Setting Expectations in AI Projects

    Tips for Setting Expectations in AI Projects If you want your AI project to succeed, mastering expectation management comes first. When working with AI projets, uncertainty isn’t just a side effect, it can make or break the entire initiative. Most people impacted by AI projects don’t fully understand how AI works, or that errors are…

  • Reducing Time to Value for Data Science Projects: Part 4

    Reducing Time to Value for Data Science Projects: Part 4 Embrace your inner software developer The post Reducing Time to Value for Data Science Projects: Part 4 appeared first on Towards Data Science. Kristopher McGlinchey Go to original source

  • Apply Sphinx’s Functionality to Create Documentation for Your Next Data Science Project

    Apply Sphinx’s Functionality to Create Documentation for Your Next Data Science Project Three cases to use the Sphinx tool as a pro The post Apply Sphinx’s Functionality to Create Documentation for Your Next Data Science Project appeared first on Towards Data Science. Radmila Mandzhieva Go to original source

  • Why AI Projects Fail

    Why AI Projects Fail No one agrees on the exact number, but estimates say anywhere from 50% to 80% of AI projects end in failure. The post Why AI Projects Fail appeared first on Towards Data Science. Ivo Bernardo Go to original source

  • Building a Personal API for Your Data Projects with FastAPI

    Building a Personal API for Your Data Projects with FastAPI How many times have you had a messy Jupyter Notebook filled with copy-pasted code just to re-use some data wrangling logic? Whether you do it for passion or for work, if you code a lot, then you’ve probably answered something like “way too many”. You’re…

  • Ivory Tower Notes: The Problem

    Ivory Tower Notes: The Problem Did you ever spend months on a Machine Learning project, only to discover you never defined the “correct” problem at the start? If so, or even if not, and you are only starting with the data science or AI field, welcome to my first Ivory Tower Note, where I will address…

  • Manage Environment Variables with Pydantic

    Manage Environment Variables with Pydantic Introduction Developers work on applications that are supposed to be deployed on some server in order to allow anyone to use those. Typically in the machine where these apps live, developers set up environment variables that allow the app to run. These variables can be API keys of external services,…