IBM Journey to AI blog
AUGUST 11, 2023
MLOps fosters greater collaboration between data scientists, software engineers and IT staff. Origins of the MLOps process MLOps was born out of the realization that ML lifecycle management was slow and difficult to scale for business application. How to use ML to automate the refining process into a cyclical ML process.
Pickl AI
JULY 14, 2023
Robotics also witnessed advancements, with AI-powered robots becoming more capable in navigation, manipulation, and interaction with the physical world. Explainable AI and Ethical Considerations (2010s-present): As AI systems became more complex and influential, concerns about transparency, fairness, and accountability arose.
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ODSC - Open Data Science
FEBRUARY 22, 2024
By the end of this session, you’ll have a practical blueprint to efficiently harness feature stores within ML workflows. Using Graphs for Large Feature Engineering Pipelines Wes Madrigal | ML Engineer | Mad Consulting Feature engineering from raw entity-level data is complex, but there are ways to reduce that complexity.
ODSC - Open Data Science
FEBRUARY 24, 2023
Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst. Its goal is to help with a quick analysis of target characteristics, training vs testing data, and other such data characterization tasks.
The MLOps Blog
JUNE 27, 2023
Collaborative workflows : Dataset storage and versioning tools should support collaborative workflows, allowing multiple users to access and contribute to datasets simultaneously, ensuring efficient collaboration among ML engineers, data scientists, and other stakeholders. Check out the documentation to get started.
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