AWS Machine Learning Blog

OCTOBER 3, 2024

This approach led to data scientists spending more than 50% of their time on operational tasks, leaving little room for innovation, and posed challenges in monitoring model performance in production. To meet this demand amidst rising claim volumes, Aviva recognizes the need for increased automation through AI technology.

DevOps 82

DevOps Data Scientist Data Science ML 82

DataRobot Blog

MARCH 16, 2023

This means they need the tools that can help with testing and documenting the model, automation across the entire pipeline and they need to be able to seamlessly integrate the model into business critical applications or workflows. They need the right expertise at the right stage as they work up the AI maturity curve.

AI 98

AI AI DevOps Neural Network 98

Marktechpost

JULY 17, 2023

Data gathering, pre-processing, modeling, and deployment are all steps in the iterative process of predictive analytics that results in output. We can automate the procedure to deliver forecasts based on new data continuously fed throughout time. This tool’s user-friendly UI consistently receives acclaim from users.

Data Mining 98

Data Mining Machine Learning Data Scientist Artificial Intelligence 98

AWS Machine Learning Blog

OCTOBER 20, 2023

The functional architecture with different capabilities is implemented using a number of AWS services, including AWS Organizations , SageMaker, AWS DevOps services, and a data lake. The reference architecture for the ML platform with various AWS services is shown in the following diagram.

ML 116

ML Data Scientist ML Engineer Data Science 116

ODSC - Open Data Science

JULY 19, 2023

A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team. For the customer, this helps them reduce the time it takes to bootstrap a new data science project and get it to production.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

Marktechpost

JULY 17, 2023

The advantages of using synthetic data include easing restrictions when using private or controlled data, adjusting the data requirements to specific circumstances that cannot be met with accurate data, and producing datasets for DevOps teams to use for software testing and quality assurance.

Machine Learning 52

Machine Learning Data Scientist Computer Vision Deep Learning 52

The MLOps Blog

OCTOBER 3, 2023

I switched from analytics to data science, then to machine learning, then to data engineering, then to MLOps. For me, it was a little bit of a longer journey because I kind of had data engineering and cloud engineering and DevOps engineering in between. You shifted straight from data science, if I understand correctly.

ML 52

ML Data Scientist Machine Learning Data Science 52

The MLOps Blog

MARCH 21, 2023

It should be able to version the project assets of your data scientists, such as the data, the model parameters, and the metadata that comes out of your workflow. Automation You want the ML models to keep running in a healthy state without the data scientists incurring much overhead in moving them across the different lifecycle phases.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69