TorchServe

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1. 
   Stan

   Stan is software for Bayesian data analysis that combines statistical modeling capabilities with user-friendly interfaces and open-source development. Its probabilistic programming language supports applications from simple linear regression to multi-level models and time-series analysis. Interfaces exist for Python, Julia, R, and the Unix shell, usable on laptops, clusters, or in the cloud. Official documentation covers the language, inference algorithms, and built-in functions.

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2. 
   MLOps Guide

   A guide explaining MLOps concepts and how to build an MLOps environment. It covers theory and a tutorial using tools such as DVC, CML, IBM Watson Machine Learning, IBM Watson OpenScale, Terraform and Cookiecutter. The guide is described as modular, allowing tools to be swapped according to project preferences and use cases.

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3. 
   Information Theory, Inference, and Learning Algorithms by David J.C. MacKay

   A 2003 book by David J.C. MacKay published by Cambridge University Press, covering information theory, inference, and learning algorithms. The site offers corrections, software, and downloadable figures. It lists a price of £35.00 or $60.00.

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4. 
   Tracking AI

   TrackingAI compares AI model results on two IQ tests, Mensa Norway and an Offline Test whose questions are not published. It presents charts of AI IQ scores, model rankings, answer speed, cost, question-by-question accuracy and changes over time.

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5. 
   Valohai

   Valohai is an MLOps platform for AI products that combine LLMs and specialized models. It provides experiment tracking, data versioning, hyperparameter optimization, pipeline automation and compute orchestration, and runs on existing AWS, Azure, GCP or on-prem infrastructure. Pricing is per user license across Pro and Enterprise tiers.

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6. 
   Edward

   Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for experimentation and research with probabilistic models, from hierarchical models on small data sets to deep probabilistic models on large data sets. It fuses Bayesian statistics and machine learning, deep learning, and probabilistic programming, and is built on TensorFlow.

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