Data Science Dojo

AUGUST 24, 2023

MLOps practices include cross-validation, training pipeline management, and continuous integration to automatically test and validate model updates. Examples include: Cross-validation techniques for better model evaluation. Managing training pipelines and workflows for a more efficient and streamlined process.

Machine Learning 195

Machine Learning Machine Learning ML ML 195

Heartbeat

OCTOBER 23, 2023

Algorithm Development and Validation: Data scientists and machine learning engineers are responsible for developing and validating algorithms that power health informatics applications. Issues such as informed consent, data ownership, and responsible data sharing must be carefully addressed to maintain public trust.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Big Data Analytics 52

Pickl AI

AUGUST 9, 2024

Model Evaluation Techniques for evaluating machine learning models, including cross-validation, confusion matrix, and performance metrics. Understanding how to assess model performance is crucial for data scientists. Students should learn about data validation techniques and the importance of data governance.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

APRIL 26, 2024

Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on unseen data. Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. In my previous role, we had a project with a tight deadline.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Heartbeat

MAY 29, 2023

Use a representative and diverse validation dataset to ensure that the model is not overfitting to the training data. Some of the steps that can be taken include: Data Governance: Implementing rigorous data governance policies that ensure fairness, transparency, and accountability in the data used to train LLMs.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78