Building and Managing an Isolation Forest Anomaly Detection Pipeline with Kedro
Anomaly (fraud) detection pipeline on credit card transaction data using Isolation Forest machine learning model and Kedro framework
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Machine Learning
Machine learning is a field of artificial intelligence (AI) that is concerned with learning from data. Machine learning has three components:
- Supervised learning: Fitting predictive models using data for which outcomes are available.
- Unsupervised learning: Transforming and partitioning data where outcomes are not available.
- Reinforcement learning: on-line learning in environments where not all events are observable. Reinforcement learning is frequently applied in robotics.
Posts on machine learning
In the following posts, machine learning is applied to solve problems using R.
Extract Insights from Text Data inside Databases using OpenAI GPT-3 and MindsDB integration
Imagine you have a lot of text data inside your database. And you want to extract insights to analyze it or perform various AI tasks on text data. In this article, you will learn how to use MindsDB to integrate your database with OpenAI GPT-3 and get insights from all your text data at once with a few SQL commands instead of making multiple individual API calls, ETL-ing and moving massive amounts of data.
ML System Design Doc - Reliable ML
ML System Design Doc
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LovelyPlots
LovelyPlots is a repository containing matplotlib style sheets to nicely format figures for scientific papers, thesis and presentations while keeping them fully editable in Adobe Illustrator. Additonaly, .svg exports options allows figures to automatically adapt their font to your document’s font. For example, .svg figures imported in a .tex file will automatically be generated with the text font used in your .tex file.
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Transformers: Architecture, Training & Usage
Where Transformer and self-supervised learning first triumphed
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ML System Design Doc - Reliable ML
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Automated Data Drift Detection For Machine Learning Pipelines
Mastering Tabular Data Validation with TFDV: A Practical Guide to Ensure Accurate and Reliable Machine Learning Models.
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On-device machine learning for everyone
Delight your customers with innovative machine learning features. MediaPipe contains everything that you need to customize and deploy to mobile (Android, iOS), web, desktop, edge devices, and IoT, effortlessly.
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Monitoring NLP models in production
All production ML models need monitoring. NLP models are no exception. But, monitoring models that use text data can be quite different from, say, a model built on tabular data.
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Automate ML model retraining and deployment with MLflow in Databricks
Getting a working machine learning model deployed for user consumption is a great achievement. We see statistics showing that machine learning models often fail to make it into production, whether this is due to insufficient data, lack of direction or other reasons.
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Building a Machine Learning Platform [Definitive Guide]
Moving across the typical machine learning lifecycle can be a nightmare. From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot.
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Dust Platform Documentation
Use the Dust Platform to design and deploy Large Language Model apps
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Daft is a fast and scalable Python dataframe for Complex Data and Machine Learning workloads
Daft is currently in its Alpha release phase - please expect bugs and rapid improvements to the project. We welcome user feedback/feature requests in our Discussions forums.
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HOPSWORKS - Python-centric Feature Store
Easily build and manage feature, training and inference pipelines. Support for Python, PySpark, Spark, Flink, and SQL
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Open-source vector similarity search for Postgres
Open-source vector similarity search for Postgres
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