10 Machine Learning Innovation Tracking and Management Tools of 2022

Machine Learning Innovation Tracking and Management tools are algorithms applications of AI

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine Learning Innovation Tracking and Management tools are algorithms applications of AI that give systems the ability to learn and improve without ample human input. While working on a machine learning project, getting good results from a single model-training run is one thing. But keeping all of your machine learning experiments well organized and having a process that lets you draw valid conclusions from them is quite another. ML teams implement experiment tracking in different ways, may it be by using spreadsheets, GitHub, or self-built platforms. Machine Learning innovation tracking is crucial and for that you need the best machine learning management tools. This article features the top 10 Machine Learning Innovation Tracking and Management tools of 2022.

Neptune

Neptune is a metadata store for any MLOps workflow. It was built for both research and production teams that run a lot of experiments. It lets you monitor, visualize, and compare thousands of ML models in one place. Neptune supports experiment tracking, model registry, and model monitoring and it's designed in a way that enables easy collaboration. It is one of the best Machine Learning Innovation Tracking and Management tools of 2022.

Weights & Biases

Weight & Biases is a machine learning platform built for experiment tracking, dataset versioning, and model management. For the experiment tracking part, its main focus is to help Data Scientists track every part of the model training process, visualize models, and compare experiments. One of the best machine learning management tools of 2022.

Comet

Comet is a Machine Learning platform that helps data scientists track, compare, explain and optimize experiments and models across the model's entire lifecycle, i.e. from training to production. In terms of experiment tracking, data scientists can register datasets, code changes, experimentation history, and models. It is one of the top ML tools for 2022.

Sacred + Omniboard

Sacred is open-source software that allows machine learning researchers to configure, organize, log, and reproduce experiments. Sacred doesn't come with its proper UI but there are a few dashboarding tools that you can connect to it, such as Omniboard. It is one of the best Machine Learning Innovation Tracking and Management tools of 2022.

MLflow

MLflow is an open-source platform that helps manage the whole machine learning lifecycle. This includes experimentation, but also model storage, reproducibility, and deployment. Each of these four elements is represented by one MLflow component: Tracking, Model Registry, Projects, and Models. It is one of the best Machine Learning Management tools of 2022.

TensorBoard

TensorBoard is the visualization toolkit for TensorFlow, so it's often the first choice of TensorFlow users. TensorBoard offers a suite of features for the visualization and debugging of machine learning models. Users can track experiment metrics like loss and accuracy, visualize the model graph, project embeddings to a lower-dimensional space, and much more. One of the top ML tools for 2022.

Guild AI

Guild AI is an experiment tracking system for machine learning, available under the Apache 2.0 open-source license. It's equipped with features that allow you to run analysis, visualization, and diffing, automate pipelines, tune hyperparameters with AutoML, do scheduling, parallel processing, and remote training. It is one of the best Machine Learning Innovation Tracking and Management tools of 2022.

Polyaxon

Polyaxon is one of the best ML tools for reproducible and scalable machine learning and deep learning applications. It includes a wide range of features from tracking and optimization of experiments to model management, run orchestration, and regulatory compliance. The main goal of its developers is to maximize the results and productivity while saving costs.

ClearML

ClearML is an open-source platform, a suite of tools to streamline your ML workflow, supported by the team behind Allegro AI. The suite includes model training logging and tracking, ML pipelines management and data processing, data management, orchestration, and deployment. It is one of the best Machine Learning Innovation Tracking and Management tools of 2022.

Valohai

Valohai is an MLOps platform that automates everything from data extraction to model deployment. The team behind this tool says that Valohai "offers Kubeflow-like machine orchestration and MLflow-like experiment tracking without any setup". Although experiment tracking is not the main focus of this platform, it provides some functionality such as experiments comparison, version control, model lineage, and traceability. One of the best data management tools of 2022.