data science

LLM Evaluations: from Prototype to Production

cornerstone of any machine learning product. Investing in quality measurement delivers significant returns. Let’s explore the potential business advantages. As management consultant and author Peter Drucker once said, Constructing a strong evaluation system...

Exporting MLflow Experiments from Restricted HPC Systems

Computing (HPC) environments, especially in research and academic institutions, restrict communications to outbound TCP connections. Running a straightforward command-line or with the MLflow tracking URL on the HPC bash shell to...

Constructing a Personal API for Your Data Projects with FastAPI

have you ever had a messy Jupyter Notebook stuffed with copy-pasted code simply to re-use some data wrangling logic? Whether you do it for passion or for work, for those who code so...

Google’s Latest AI System Outperforms Physicians in Complex Diagnoses

going to the doctor with a baffling set of symptoms. Getting the proper diagnosis quickly is crucial, but sometimes even experienced physicians face challenges piecing together the puzzle. Sometimes it won't be something...

Plotly’s AI Tools Are Redefining Data Science Workflows 

Is there anything more frustrating than constructing a robust data model but then struggling to show it right into a tool stakeholders can use to realize their desired consequence? Data Science has never been...

Why CatBoost Works So Well: The Engineering Behind the Magic

is a cornerstone technique for modeling tabular data on account of its speed and ease. It delivers great results with none fuss. Whenever you go searching you’ll see multiple options like LightGBM, XGBoost,...

The Basis of Cognitive Complexity: Teaching CNNs to See Connections

Liberating education consists in acts of cognition, not transferrals of data. Paulo freire heated discussions around artificial intelligence is: What points of human learning is it able to capturing? Many authors suggest that artificial intelligence...

A Data Scientist’s Guide to Docker Containers

a ML to be useful it must run somewhere. This somewhere is almost certainly not your local machine. A not-so-good model that runs in a production environment is healthier than an ideal model...

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