AI

Toolkit

Model Registry

Simplify model versioning, track model lineage, and facilitate collaboration. These tools provide centralized repositories for storing and managing your models, enhancing reproducibility and enabling efficient model sharing.

Featured Apps and Tools

Other Model Registry Apps and Tools

TensorFlow is an open-source machine learning library that supports multiple programming languages including Python and Javascript. TensorFlow provides various tools for building and bringing deep learning models to production. The library became more popular in its second version(TensorFlow 2.x) because of integrating Keras as its high-level API. Keras is an open-source library that provides a simple to use Python interface for neural networks.

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Amazon SageMaker is a cloud machine-learning platform that enables developers to create, train, and deploy machine-learning models in the cloud. It also enables developers to deploy ML models on embedded systems and edge-devices.

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Neptune is an experiment tracking hub bringing organization and collaboration to data science projects. Neptune records your entire experimentation process ‚Äì exploratory notebooks, model training runs, code, hyperparameters, metrics, data versions, results, exploration visualizations, and more. Everything is stored and backed-up in an organized knowledge repository, ready to be accessed, analyzed, shared, and discussed with your team. No matter what type of problems you are working on, Neptune fits them all, from evaluating credit risk to finding the nuclei in divergent images.

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MLflow is an open-source platform for managing the machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry

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Weights and biases is a platform for tracking and visualizing machine learning experiments as well as team collaboration

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Comet is a platform for managing the machine learning lifecycle. Comet users are able to track, compare, explain and reproduce machine learning experiments.

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Determined AI is an open-source deep learning training platform for training models with built-in hyperparameter tuning and distributed training.

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ModelDB is an open source machine learning model versioning, metadata, and experiment management.

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Kubeflow is an open-source cloud-native machine learning platform for orchestrating complicated machine learning workflows on containerized environments using Kubernetes.

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ClearML (formerly Trains) is a complete, open source ML / DL experimentation and MLOps solution. ClearML eliminates the time-consuming and error-prone tasks associated with development, version tracking, and the full ML lifecycle for automation and scaling. The tool comprises the ClearML Python Package, ClearML Hosted Service (or your own self-hosted ClearML Server), and the MLOps ClearML Agent that make a unified solution

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Azure Machine Learning

Azure Machine Learning is a cloud-based platform for training, deploying, automating, managing, and tracking machine learning models. The platform supports deep learning, supervised and unsupervised learning. It also offers support for Python and R. There is also a studio that allows you to work with no code.

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More AI Toolkit categories

Building POC application

Simplify your development process while showcasing the practicality of your AI projects. With our recommended tools you can create, test, and deploy proof-of-concept applications

Code Versioning

Optimize collaboration among your team members, ensure code integrity, and simplify the deployment process. These tools provide efficient code version control, making it easier to manage complex AI and Machine Learning projects.

Data Exploration and Analysis

Visualize and analyze your datasets, identify patterns, and make informed decisions. These tools offer a range of features, from data visualization to statistical analysis, empowering you to explore and understand your data in-depth.

Data Labelling and Annotation

Accelerate the training of your machine learning models by providing high-quality labeled datasets. These tools offer intuitive interfaces, annotation options, and collaboration features, ensuring accurate and consistent data labeling for your AI projects.

Data Versioning

Keep track of changes made to your datasets, improve reproducibility, and simplify collaboration. These tools enable you to manage and version your data effectively, enhancing the reliability and accuracy of your AI and Machine Learning workflows.

Development IDE

Improve your productivity, streamline your coding process, and leverage advanced debugging and testing features. These IDEs offer a seamless development experience, providing the necessary tools for building and deploying AI applications.

Experiment Tracking

Stay organized, monitor model performance, and reproduce results easily. These tools offer experiment-tracking features, allowing you to record, compare, and analyze your experiments, improving the efficiency and effectiveness of your AI workflows.

Feature Engineering

Extract, transform, and select features to enhance the performance of your machine learning models. These tools offer a range of feature engineering techniques, empowering you to create meaningful and predictive features from your data.

Simplify feature reuse, ensure consistency, and accelerate model development. These tools provide centralized repositories for storing and accessing features, enabling easy collaboration and improving the productivity of your AI projects.

Model Debbugging and Visualization

Gain insights into model behavior, identify issues, and improve model performance. These tools offer advanced debugging and visualization capabilities, helping you understand and optimize your models for better predictions and outcomes.

Model Monitoring

Detect anomalies, ensure model reliability, and take proactive actions. These tools offer real-time monitoring features, allowing you to track model performance and make data-driven decisions to maintain optimal model performance.

Model Packaging

Simplify model deployment, ensure consistency, and enable seamless integration into your applications. These tools offer comprehensive model packaging features, making it easier to share and distribute your trained models.

Simplify model versioning, track model lineage, and facilitate collaboration. These tools provide centralized repositories for storing and managing your models, enhancing reproducibility and enabling efficient model sharing.

Deploy models as scalable APIs and enable seamless integration into your applications. These tools offer high-performance model-serving capabilities, ensuring fast and reliable predictions for your AI-driven applications.

Streamline the training and optimization of your machine learning models. These tools offer advanced training algorithms, distributed computing capabilities, and efficient resource utilization, empowering you to train models faster and more effectively.

Model Tuning and HPO

Automate the search for optimal hyperparameters, improve model accuracy, and save time and resources. These tools offer intelligent algorithms and efficient search strategies, enabling you to fine-tune your models for optimal performance.

Workflow Orchestration

Automate complex workflows, ensure reproducibility, and optimize resource utilization. These tools offer intuitive interfaces, workflow scheduling, and dependency management features, empowering you to streamline and scale your AI projects.

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