[[https://medium.com/oreillymedia/ai-orchestration-enables-decision-intelligence-2a88d8306ac9|AI Orchestration Enables Decision Intelligence]]

[[https://www.ibm.com/za-en/cloud/automation/watson-orchestrate|
Introducing IBM Watson Orchestrate
]]

[[https://www.run.ai/|Accelerate AI by Optimizing Compute Resources]]

[[https://www.advsyscon.com/blog/hyperautomation-what-is-hyper-automation/|What is Hyperautomation?]]

[[https://www.apexofinnovation.com/%E2%80%8B%E2%80%8Bare-you-prepared-for-these-emerging-ai-technologies/|​​Are You Prepared for These Emerging AI Technologies?]]


[[https://neptune.ai/blog/best-workflow-and-pipeline-orchestration-tools|Best Workflow and Pipeline Orchestration Tools: Machine Learning Guide]]



Kale – Aims at simplifying the Data Science experience of deploying Kubeflow Pipelines workflows.

Flyte – Easy to create concurrent, scalable, and maintainable workflows for machine learning.

MLRun – Generic mechanism for data scientists to build, run, and monitor ML tasks and pipelines.

Prefect – A workflow management system, designed for modern infrastructure.

ZenML – An extensible open-source MLOps framework to create reproducible pipelines.

Argo – Open source container-native workflow engine for orchestrating parallel jobs on Kubernetes.

Kedro – Library that implements software engineering best-practice for data and ML pipelines.

Luigi – Python module that helps you build complex pipelines of batch jobs.

Metaflow – Human-friendly lib that helps scientists and engineers build and manage data science projects.

Couler – Unified interface for constructing and managing workflows on different workflow engines.

Valohai – Simple and powerful tool to train, evaluate and deploy models.

Dagster.io – Data orchestrator for machine learning, analytics, and ETL.

Netflix Genie – Genie developed by Netflix is an open-source distributed workflow/task orchestration framework.

[[https://www.mezzanine.ai/|ORCHESTRATED MACHINE LEARNING, CURATED FOR BUSINESS]]