Data Science Dojo

AUGUST 24, 2023

By selecting MLOps tools that address these vital aspects, you will create a continuous cycle from data scientists to deployment engineers to deploy models quickly without sacrificing quality. Examples include: Cross-validation techniques for better model evaluation.

Machine Learning 195

Machine Learning Machine Learning ML ML 195

DagsHub

APRIL 21, 2024

Although MLOps is an abbreviation for ML and operations, don’t let it confuse you as it can allow collaborations among data scientists, DevOps engineers, and IT teams. Model Training Frameworks This stage involves the process of creating and optimizing the predictive models with labeled and unlabeled data.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Mlearning.ai

MAY 23, 2023

Hey guys, in this blog we will see some of the most asked Data Science Interview Questions by interviewers in [year]. Data science has become an integral part of many industries, and as a result, the demand for skilled data scientists is soaring. What is Cross-Validation?

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

The MLOps Blog

JUNE 6, 2023

The pipeline automates the entire process of preprocessing the data and training the model, making the workflow more efficient and easier to maintain. Perform cross-validation using StratifiedKFold. The model is trained K times, using K-1 folds for training and one fold for validation.

ML 52

ML ML Cross Validation Machine Learning 52

DataRobot Blog

DECEMBER 20, 2022

Using built-in automation workflows , either through the no-code Graphical User Interface (GUI) or the code-centric DataRobot for data scientists , both data scientists and non-data scientists—such as asset managers and investment analysts—can build, evaluate, understand, explain, and deploy their own models.

AI 59

AI AI Cross Validation Machine Learning 59

AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. As an example, in the following figure, we separate Cover 3 Zone (green cluster on the left) and Cover 1 Man (blue cluster in the middle).

ML 69

ML ML Machine Learning Machine Learning 69

AWS Machine Learning Blog

JANUARY 26, 2023

Data scientists train multiple ML algorithms to examine millions of consumer data records, identify anomalies, and evaluate if a person is eligible for credit. This is a common problem that data scientists face when training their models.

ML 78

ML ML Data Scientist AWS 78