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Top Highlights from 10 Powerful Machine Learning Conferences in 2020

Analytics Vidhya

Overview Have a look at the top AI and ML conferences of the year Go through the resources attached with them for a better. The post Top Highlights from 10 Powerful Machine Learning Conferences in 2020 appeared first on Analytics Vidhya.

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Cloud ML In Perspective: Surprises of 2021, Projections for 2022

KDnuggets

Let’s take a closer look on Cloud ML market in 2021 in retrospective (with occasional drills into realities of 2020, too). Read this in-depth analysis.

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Made With ML: Discover, build, and showcase machine learning projects

KDnuggets

This is a short introduction to Made With ML, a useful resource for machine learning engineers looking to get ideas for projects to build, and for those looking to share innovative portfolio projects once built.

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10 Must-Know Python Libraries for Machine Learning in 2024

Machine Learning Mastery

As we progress through 2024, machine learning (ML) continues to evolve at a rapid pace. Python, with its rich ecosystem of libraries, remains at the forefront of ML development.

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Are Model Explanations Useful in Practice? Rethinking How to Support Human-ML Interactions.

ML @ CMU

Our work further motivates novel directions for developing and evaluating tools to support human-ML interactions. Model explanations have been touted as crucial information to facilitate human-ML interactions in many real-world applications where end users make decisions informed by ML predictions.

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Building a Mature Machine Learning Team

KDnuggets

After spending a lot of time thinking about the paths that software companies take toward ML maturity, this framework was created to follow as you adopt ML and then mature as an organization.

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Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

AWS Machine Learning Blog

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

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