AI

Toolkit

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.

Featured Apps and Tools

Other Workflow Orchestration Apps and Tools

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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Polyaxon is a platform for automating and reproducing deep learning and machine learning applications. It has a full set of tools for MLOps. Polyaxon offers powerful features built directly into the core software which can be customized and configured based on the needs of each individual deployment.

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Dagster is a data orchestrator for machine learning, analytics, and ETL

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Valohai is a managed machine learning platform that enables data scientists to build, deploy, and track machine learning models.

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Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code. It borrows concepts from software engineering and applies them to machine-learning code; applied concepts include modularity, separation of concerns and versioning

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Luigi is a Python (2.7, 3.6, 3.7 tested) package that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, handling failures, command line integration, and much more.

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Apache Airflow is an open-source workflow management platform.

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The workflow automation platform for complex, mission-critical data and ML processes at scale. It makes it easy to create concurrent, scalable, and maintainable workflows for machine learning and data processing.

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Backend.AI is a cloud resource management platform focused on the orchestration of computing resources between Deep Learning framework and cloud infrast

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ZenML is the open-source MLOps framework for reproducible ML pipelines and production-ready Machine Learning.

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Metaflow is a library for managing data science projects. The library is available in Python and R. It supports data scientists from prototyping projects to production.

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Prefect is the new standard in dataflow automation , trusted to build, run, and monitor millions of data workflows and pipelines.

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Cadence¬Æ Tensilica¬Æ Vision digital signal processor (DSP) family is designed for demanding imaging, computer vision, and neural network (NN) applications

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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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