The MLOps Blog

MARCH 21, 2023

Your data scientists develop models on this component, which stores all parameters, feature definitions, artifacts, and other experiment-related information they care about for every experiment they run. Building a Machine Learning platform (Lemonade). Design Patterns in Machine Learning for MLOps (by Pier Paolo Ippolito).

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Machine Learning Machine Learning Data Scientist ML 69

Towards AI

FEBRUARY 20, 2024

I am starting a series with this blog, which will guide a beginner to get the hang of the ‘Machine learning world’. Photo by Andrea De Santis on Unsplash So, What is Machine Learning? Definition says, machine learning is the ability of computers to learn without explicit programming.

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Explosion

MAY 17, 2023

That’s definitely new. Once you’re past prototyping and want to deliver the best system you can, supervised learning will often give you better efficiency, accuracy and reliability than in-context learning for non-generative tasks — tasks where there is a specific right answer that you want the model to find.

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Pickl AI

JANUARY 2, 2025

Summary: Entropy in Machine Learning quantifies uncertainty, driving better decision-making in algorithms. It optimises decision trees, probabilistic models, clustering, and reinforcement learning. Entropy enhances clustering, federated learning, finance, and bioinformatics.

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Data Science 101

OCTOBER 31, 2019

A definition from the book ‘Data Mining: Practical Machine Learning Tools and Techniques’, written by, Ian Witten and Eibe Frank describes Data mining as follows: “ Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Clustering. Classification. Regression.

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How to Learn Machine Learning

MAY 2, 2025

Compute Resources : Azure ML provides scalable compute options like training clusters, inference clusters, and compute instances that can be automatically scaled based on workload demands. Leverage Data Labeling : For supervised learning projects, utilize Azure ML’s data labeling capabilities to efficiently annotate datasets.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

They use self-supervised learning algorithms to perform a variety of natural language processing (NLP) tasks in ways that are similar to how humans use language (see Figure 1). This edge cluster was also connected to an instance of Red Hat Advanced Cluster Management for Kubernetes (RHACM) hub running in the cloud.

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Snorkel AI

APRIL 18, 2023

Typically, you let the experts read some articles, label them, and then use them as training data and train the supervised learning model. To address all these problems, we looked into weak supervised learning. Once we label a fraction of documents, we use that as training data to train the supervised learning model.

Supervised Learning 52

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Snorkel AI

APRIL 18, 2023

Typically, you let the experts read some articles, label them, and then use them as training data and train the supervised learning model. To address all these problems, we looked into weak supervised learning. Once we label a fraction of documents, we use that as training data to train the supervised learning model.

Supervised Learning 52

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Pickl AI

JULY 12, 2024

This section delves into its foundational definitions, types, and critical concepts crucial for comprehending its vast landscape. Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data.

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Pickl AI

NOVEMBER 18, 2024

Key Takeaways Machine Learning Models are vital for modern technology applications. Types include supervised, unsupervised, and reinforcement learning. Key steps involve problem definition, data preparation, and algorithm selection. Ethical considerations are crucial in developing fair Machine Learning solutions.

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Pickl AI

SEPTEMBER 17, 2024

Key Terms and Definitions To fully grasp the concepts of dimensionality reduction, it’s essential to understand the key terms and definitions associated with this field. This simplification makes exploring patterns, relationships, and clusters within the data easier.

Machine Learning 52

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Explosion

AUGUST 3, 2017

You’ll collect more user actions, giving you lots of smaller pieces to learn from, and a much tighter feedback loop between the human and the model. By definition, you can’t directly control what the process returns. However, the unsupervised algorithm won’t usually return clusters that map neatly to the labels you care about.

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Pickl AI

OCTOBER 24, 2024

Summary: This article compares Artificial Intelligence (AI) vs Machine Learning (ML), clarifying their definitions, applications, and key differences. While AI aims to replicate human intelligence across various domains, ML focuses on learning from data to improve performance.

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Pickl AI

MAY 31, 2023

Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines.

Data Science 52

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The MLOps Blog

JANUARY 5, 2023

A lot of the time, search engines are being shown like just pass some images through a pre-trained network, and then the features coming out of it will cluster this data sample, and that’s true, but if it clusters the way you think it should be, that is another story, right? How self-supervised learning works.

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Explosion

SEPTEMBER 17, 2013

You should use two tags of history, and features derived from the Brown word clusters distributed here. Averaged Perceptron POS tagging is a “supervised learning problem”. And it definitely doesn’t matter enough to adopt a slow and complicated algorithm like Conditional Random Fields. Here’s the problem.

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Dataconomy

MARCH 18, 2025

Definition and concept of anomaly detection Anomalies in data can manifest as unexpected spikes, drops, or shifts in trends. How anomaly detection works Understanding how anomaly detection works involves exploring different machine learning approaches. Points situated in low-density regions are tagged as anomalies.

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ML @ CMU

DECEMBER 2, 2024

Second, they extend the classification of positive definite kernels from Euclidean distances to Manhattan distances, offering a broader foundation for kernel methods.

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