Iterative.ai

Iterative.ai is ana machine learning operations (MLOps (machine learning operations) company focused on streamlining the workflow of data scientists. The company builds developer tools for machine learning that are designed to reduce the complexity of managing datasets, ML infrastructure, and ML models lifecycle management. Iterative.ai's products have been developed by over 200 open sourceopen-source contributors, engaged with by more than 4000 community members, used by over 400 companies, and awarded more than 7000 Github stars.

DVC can find uses in the storage and processing of data files and in the production of other data or machine learning models. DVC also enables the user to perform the following:

• Track and save data and machine learning models in the same way that code is captured.

• Create and switch between versions of data and ML models.

• Gain insight into the original structure of datasets and ML artifacts.

• Compare model metrics among experiments.

• Adopt engineering tools and best practices in data science projects.

Some of the advantagesAdvantages of the DVC tool include those below:

• Data compliance: Data modification attempts can be reviewed as Git receives requests. The user can audit the project's history to learn when and why datasets or models were approved.

Continuous Machine Learning (CML) is an open-source library for implementing CI/CD (continuous integration/delivery) in Machinemachine Learninglearning Projectsprojects. It can be used to automate parts of the user's development workflow, including model training and evaluation, comparing ML experiments across the user's project history, and monitoring variable datasets.

CML was developed to enable the use of GitLab or GitHub to manage ML experiments, track whoever trains ML models or modifies data and at what time, and to automatically generate reports for ML experiments, with metrics and plots in every Git Pull Request. CML allows the users to build their own ML platform using GitHub or GitLab and cloud services, such as AWS, Azure, or GCP. Like DVC, CML works independently of extraneous databases and services.

Iterative.ai's Studio is a collaboration tool for machine learning, offering data and model management, experiment tracking, vualizationvisualization, and automation. Studio is offered for teams and for individual users, and works with other of Iterative.ai's software products.

MLEM is an open-source tool offered by Iterative.ai and is intended to help users simplify machine learning model deployments. MLEM allows users to save an ML model with a Python call, can capture the ML models metadata automatically in a human-readable YAML format, allows users to deploy models where they want, and allowingenables them to switch platforms for deployments, and. MLEM is developed to help users make a Git model registery -registry and was developed for Git-native ML models.

Iterative.ai's Studio is a collaboration tool for machine learning, offering data and model management, experiment tracking, vualization, and automation. Studio is offered for teams and for individual users, and works with other of Iterative.ai's software products.

MLEM is an open-source tool offered by Iterative.ai and intended to help users simplify machine learning model deployments. MLEM allows users to save an ML model with a Python call, can capture the ML models metadata automatically in a human-readable YAML format, allows users to deploy models where they want and allowing them to switch platforms for deployments, and MLEM is developed to help users make a Git model registery - and was developed for Git-native ML models.