The rise of artificial intelligence and data science across industries underscores the pressing need for effective management and governance of machine learning (ML) models. Traditional approaches to ML models management often involve disparate storage systems and lack standardized methodologies for versioning, audit, and re-use. Inspired by data lake concepts, this paper develops the concept of ML Model Lake as a centralized management framework for datasets, codes, and models within organizations environments. We provide an in-depth exploration of the Model Lake concept, delineating its architectural foundations, key components, operational benefits, and practical challenges. We discuss the transformative potential of adopting a Model Lake approach, such as enhanced model lifecycle management, discovery, audit, and reusabil-ity. Furthermore, we illustrate a real-world application of Model Lake and its transformative impact on data, code and model management practices.

When you think of Machine Learning, you think about models. These models need effective management to ensure that they are producing the outputs required to solve a specific problem or task. Machine Learning Model Management is used to help Data Scientists, Machine Learning engineers, and more to keep track and on top of all their experiments and the results produced by the model. Machine Learning Model Management sole responsibility is ensuring that the development, training, versioning and deployment of ML models is managed at an effective level. The tools used in the development cycle for Machine Learning and the managing of the models require MLOps - Machine Learning Operations. To recap and for those of you who may be unsure, MLOps is a core function to the engineering of Machine Learning.

In my previous blog post „how to manage machine learning models" I explained the difficulties within the process of developing a good machine learning model and motivated using a tool to support data scientists with this challenge. First there will be one paragraph per framework that describes the project and shows some code examples. In the end of the article you will find a framework comparison and recommendations when to use which framework. As with my previous post the sklearn dataset on Boston-Housing prices will be used as basis. You can find a notebook to play with in this github repo. This notebook also includes instructions how to install the frameworks as well as some other functions we will use within the code examples below, but that won't be discussed further, to place focus on the framework specific parts and omit boilerplate code. DVC means „data (science) version control" and aims to do for data science what git already does for software development: Making development processes traceable and reproducible.