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Off-the-shelf vs Custom Machine Learning Models?

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Companies can engage in different approaches to model development. From fully managed ML services, all the way to custom models. Depending on business requirements, available expertise, and planning constraints, they must make a choice: should they develop custom solutions from scratch? Or should they choose an off-the-shelf service? For all stages of ML workloads, a decision must be met concerning how the different puzzle pieces will fit together.


Using SHAP to Explain Machine Learning Models

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Do you understand how your machine learning model works? Despite the ever-increasing usage of machine learning (ML) and deep learning (DL) techniques, the majority of companies say they can't explain the decisions of their ML algorithms [1]. This is, at least in part, due to the increasing complexity of both the data and models used. It's not easy to find a nice, stable aggregation over 100 decision trees in a random forest to say which features were most important or how the model came to the conclusion it did. This problem grows even more complex in application domains such as computer vision (CV) or natural language processing (NLP), where we no longer have the same high-level, understandable features to help us understand the model's failures.


How to Create Unbiased Machine Learning Models - KDnuggets

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AI systems are becoming increasingly popular and central in many industries. They decide who might get a loan from the bank, whether an individual should be convicted, and we may even entrust them with our lives when using systems such as autonomous vehicles in the near future. Thus, there is a growing need for mechanisms to harness and control these systems so that we may ensure that they behave as desired. One important issue that has been gaining popularity in the last few years is fairness. While usually ML models are evaluated based on metrics such as accuracy, the idea of fairness is that we must ensure that our models are unbiased with regard to attributes such as gender, race and other selected attributes.


Mastering Machine Learning Explainability in Python

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For data scientists, a key part of interpreting machine learning models is understanding which factors impact predictions. In order to effectively use machine learning in their decision-making processes, companies need to know which factors are most important. For example, if a company wants to predict the likelihood of customer churn, it might also want to know what exactly drives a customer to leave a company. In this example, the model might indicate that customers who purchase products that rarely go on sale are much more likely to stop purchasing. Armed with this knowledge, a company can make smarter pricing decisions in the future.


When to Retrain an Machine Learning Model? Run these 5 checks to decide on the schedule - KDnuggets

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The world and data are not static. But most machine learning models are. Once they are in production, they become less relevant with time. The data distributions evolve, the behavioral patterns change, and models need updates to keep up with new reality. The usual process is to retrain the models at defined intervals.


Why 90% of machine learning models never hit the market

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Corporations are going through rough times. The times are uncertain, and having to make customer experiences more and more seamless and immersive isn't taking off any of the pressure on companies. In that light, it's understandable that they're pouring billions of dollars into the development of machine learning models to improve their products. Companies can't just throw money at data scientists and machine learning engineers, and hope that magic happens. Here's how AI can improve your company's customer journey The data speaks for itself.


InformationWeek, serving the information needs of the Business Technology Community

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Most managers feel euphoria when implementing a technology meant to enhance the workflow of a team or an organization. But they often overlook the details that help implement the technology successfully. The same sentiment can occur for managers who oversee data scientists, data engineers, and analysts examining machine learning initiatives. Every organization seems to be in love with machine learning. Because love is blind, so to speak, IT teams become the first line of defense in protecting that euphoric feeling.


Image-Based Deep Learning Models Can Predict Abdominal Surgery Outcomes: Study

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Deep learning is a type of machine learning in which a model learns to perform classification tasks directly from images, text or sound. Image-based deep learning models (DLMs) have been used in other disciplines, but this method has yet to be used to predict surgical outcomes. With this background, researchers carried out a study to examine whether deep learning models (DLMs) using routine preoperative imaging can predict surgical complexity and outcomes in abdominal wall reconstruction. They applied image-based deep learning to predict complexity, defined as need for component separation, and pulmonary and wound complications after abdominal wall reconstruction (AWR). This quality improvement study was performed at an 874-bed hospital and tertiary hernia referral center from September 2019 to January 2020.


Model Monitoring Enables Robust Machine Learning Applications - Gradient Flow

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According to the 2020 Gartner Hype Cycle for Artificial Intelligence, machine learning (ML) is entering the Trough of Disillusionment phase. This is the phase where the real work begins--best practices, infrastructures, and tools are being developed to facilitate the technology's integration into real-world production environments. Today, ML technologies have secured a central role in many companies. ML technologies also are beginning to gain footholds across industries as they become more widely adopted in enterprises. For example, advances in speech and natural language models are fueling growth in voice applications.


Deep–learning model improves radiologist interpretation of X-rays

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Consequently, the use of artificial intelligence, deep-learning algorithms to assist with the interpretation of X-rays has the potential to improve diagnostic …