AWS Machine Learning Blog
DECEMBER 19, 2023
Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. However, putting an ML model into production at scale is challenging and requires a set of best practices.
ODSC - Open Data Science
MARCH 27, 2023
ML Governance: A Lean Approach Ryan Dawson | Principal Data Engineer | Thoughtworks Meissane Chami | Senior ML Engineer | Thoughtworks During this session, you’ll discuss the day-to-day realities of ML Governance. Scaling AI/ML Workloads with Ray Kai Fricke | Senior Software Engineer | Anyscale Inc.
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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.
ODSC - Open Data Science
FEBRUARY 22, 2024
Key points of this talk are: In this talk, we will focus on: The dangers of using post-hoc explainability methods as tools for decision making, and how traditional ML isn’t suited in situations where want to perform interventions on the system. Conclusion Can’t wait to start learning from these incredible speakers and experts?
Pickl AI
JULY 14, 2023
Explainable AI and Ethical Considerations (2010s-present): As AI systems became more complex and influential, concerns about transparency, fairness, and accountability arose. Researchers began addressing the need for Explainable AI (XAI) to make AI systems more understandable and interpretable.
Heartbeat
FEBRUARY 1, 2023
Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. In this article, we’ll learn everything there is to know about these operations and how ML engineers go about performing them. What is MLOps? They might also help with data preparation and cleaning.
The MLOps Blog
JANUARY 23, 2023
Most of our customers are doing ML/MLOps at a reasonable scale, NOT at the hyperscale of big-tech FAANG companies. Not a fork: – The MLOps team should consist of a DevOps engineer, a backend software engineer, a data scientist, + regular software folks. Ok, let me explain. How about the ML engineer?
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