Remove Categorization Remove ML Engineer Remove Software Engineer
article thumbnail

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

Model risk : Risk categorization of the model version. Madhubalasri is dedicated to driving innovation in ML governance and optimizing model management processes Saumitra Vikaram is a Senior Software Engineer at AWS. Keshav Chandak is a Software Engineer at AWS with a focus on the SageMaker Repository Service.

ML 89
article thumbnail

Getting Started with AI

Towards AI

What is AI Engineering AI Engineering is a new discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts [1]. In a nutshell, AI Engineering is the application of software engineering best practices to the field of AI. Bourque and R.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Must-Have Skills for a Machine Learning Engineer

Pickl AI

Fundamental Programming Skills Strong programming skills are essential for success in ML. This section will highlight the critical programming languages and concepts ML engineers should master, including Python, R , and C++, and an understanding of data structures and algorithms. million by 2030, with a remarkable CAGR of 44.8%

article thumbnail

How HSR.health is limiting risks of disease spillover from animals to humans using Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

SageMaker geospatial capabilities make it easy for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. With over 15 years of experience, he supports customers globally in leveraging AI and ML for innovative solutions that capitalize on geospatial data.

ML 101
article thumbnail

MLflow: Simplifying Machine Learning Experimentation

Viso.ai

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. Tags: To label and categorize, attach key-value pairs to models and versions. pre-deployment checks).