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Algorithm Optimization for Machine Learning

Machine learning optimization is an important part of all machine learning models. Whether used to classify an image in facial recognition software or cluster users into like-minded customer groups, all types of machine learning model will have undergone a process of optimization. In fact, machine learning itself can be described as solving an optimization problem, […]

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A Guide to Deploying Machine Learning Models on Kubernetes

How to Deploy Models on Kubernetes Kubernetes is a container orchestration platform used to manage containerized applications. It used to automate important parts of the container management process such as container replication, scaling, monitoring and scheduling. It’s an open-source platform written in Google’s Go programming language. Kubernetes is one of the most popular container management

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What is Covariate Shift?

Covariate shift is a specific type of dataset shift often encountered in machine learning. It is when the distribution of input data shifts between the training environment and live environment. Although the input distribution may change, the output distribution or labels remain the same. Covariate shift is also known as covariate drift, and is a very

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Outlier Detection and Analysis Methods

Outlier detection is a key consideration within the development and deployment of machine learning algorithms. Models are often developed and leveraged to perform outlier detection for different organizations that rely on large datasets to function. Economic modeling, financial forecasting, scientific research, and e-commerce campaigns are some of the varied areas that machine learning-driven outlier detection

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