Open-source alternatives to Clearml

Polyaxon is a Kubernetes-native machine learning orchestration platform and MLOps pipeline orchestrator. It serves as a control plane for managing distributed deep learning workloads, automated machine learning pipelines, and experiment tracking. The platform distinguishes itself through specialized services for distributed training management, including MPI-based coordination for PyTorch and TensorFlow. It provides an automated hyperparameter optimization service utilizing Bayesian, random, and grid search algorithms, alongside managed interactive AI workspaces for launching Jupyter notebook

PyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It functions as a low-code environment that leverages a scikit-learn native engine to execute preprocessing, training, and evaluation for tabular data. The platform distinguishes itself as an LLM-powered ML copilot, using large language model agents to analyze datasets, design experiment configurations, and explain model results. It also serves as a Kubernetes ML orchestrator and model registry, enabling the versioning of trained pipelines and their promotion to production API endp

ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself

FedML is a distributed machine learning training library, federated learning framework, and GPU workload orchestrator. It provides the core system components necessary to execute large-scale model training and fine-tuning across multi-cloud, on-premise, and decentralized GPU clusters, while offering a dedicated engine for scalable model serving and an MLOps pipeline manager for end-to-end lifecycle management. The platform distinguishes itself by enabling privacy-preserving federated learning across decentralized edge devices and organizational silos, keeping raw data on local hardware. It al

Cube Studio is a cloud-native MLOps platform and Kubernetes-based AI orchestrator designed for the entire machine learning lifecycle. It provides a distributed training framework for large-scale model fine-tuning, a GPU resource manager for hardware virtualization, and an ML pipeline orchestrator that uses visual directed acyclic graphs to manage end-to-end workflows. The platform distinguishes itself through its specialized LLM inference server, which supports retrieval-augmented generation and the construction of private knowledge bases. It features a dedicated system for supervised fine-tu

MLOps-Basics is a collection of implementation guides and blueprints for automating the machine learning lifecycle. It provides practical workflows for managing the transition of models from training to production deployment, focusing on the integration of operational tools into the machine learning pipeline. The project features specific architectural patterns for deploying containerized models using serverless infrastructure and cloud registries. It includes frameworks for tracking large datasets and model artifacts via remote storage, as well as guides for converting models into standardiz

Azure Machine Learning Notebooks is a cloud-based environment for developing and executing interactive Jupyter notebooks within a managed machine learning workspace. It provides managed machine learning compute through cloud-based workstations and containerized environments pre-configured with GPU drivers and kernels for high-performance model training. The project functions as a distributed GPU training platform and an ML experiment tracking system to monitor training metrics and version data assets. It also serves as an MLOps pipeline orchestrator for automating modular workflows and a mode

This project is a distributed computing platform designed to orchestrate containerized workloads across heterogeneous hardware clusters. It functions as a centralized control plane that manages resource allocation, scheduling, and execution environments, enabling organizations to share high-performance computing infrastructure securely among multiple users and projects. The platform distinguishes itself through advanced hardware virtualization and multi-tenant management capabilities. It supports the partitioning of physical graphics processing units into fractional slices, allowing multiple

SwanLab is an open-source machine learning experiment tracking platform and observability tool. It provides a centralized dashboard for logging training metrics, hyperparameters, and hardware performance to monitor and analyze AI model training runs. The platform is distinguished by its focus on self-hosted infrastructure, allowing users to deploy private instances via Docker or Kubernetes for secure on-premises data control. It also includes specialized utilities for migrating historical experiment logs and synchronizing real-time metrics from external tools like MLflow. The system covers a

This project is a structured educational program and comprehensive training curriculum designed to teach the end-to-end lifecycle of machine learning models. It serves as a resource for engineers to master the transition of data science projects from development into reliable, production-ready systems. The curriculum focuses on the practical application of engineering best practices, emphasizing the orchestration of complex data processing and training sequences. It provides instruction on building repeatable workflows, managing experiment metadata, and implementing infrastructure automation

KServe is a Kubernetes-native platform for deploying and serving machine learning models as scalable inference services. It supports both generative AI models, including large language models, and traditional predictive models from frameworks such as TensorFlow, PyTorch, Scikit-Learn, XGBoost, and ONNX. The platform manages the full lifecycle of model deployments, including revision tracking, canary rollouts, A/B testing, and automatic rollbacks, and provides serverless scale-to-zero capabilities for cost-efficient resource management. KServe distinguishes itself through a standardized infere

DVC is a data versioning tool and pipeline orchestrator designed to track large datasets and machine learning models. It functions as a system for managing large data artifacts by storing lightweight metadata in version control while keeping the actual binaries in a separate cache. The project serves as an experiment tracker and remote storage synchronizer, enabling the execution and comparison of machine learning iterations based on hyperparameters and performance metrics. It provides a bridge for pushing and pulling these large data artifacts between local environments and cloud or on-premi

Maestro is a distributed job scheduler and containerized data pipeline tool designed to coordinate complex sequences of tasks. It functions as a Kubernetes workflow orchestrator and MLOps automation platform, utilizing directed acyclic graphs to manage task dependencies and execution order across computing clusters. The system distinguishes itself through the use of isolated container environments for each workflow step, ensuring consistent runtime dependencies. It incorporates an asynchronous event bus to coordinate state transitions and provides lifecycle hook integration that dispatches sy

Meshery is a service mesh management plane and cloud native infrastructure orchestrator. It provides a visual design-as-code environment for modeling microservices and infrastructure components through declarative blueprints, functioning as a centralized platform for designing, deploying, and managing service mesh infrastructure. The platform is distinguished by its ability to translate visual designs into active deployments and its use of gRPC-based adapters to integrate with diverse infrastructure providers. It features a multi-tenant architecture that manages shared workspaces and role-bas

OpenDevOps is a DevOps automation platform and hybrid cloud management platform designed to control infrastructure across multiple geographic regions and hybrid environments through a unified interface. It serves as a Kubernetes cluster orchestrator for deploying and administering containerized applications to ensure high availability and scaling. The system provides a framework for enterprise DevOps automation, executing repetitive maintenance and deployment workflows to reduce manual operational effort. It incorporates a role-based access control system to verify user identities and assign

Metaflow is a Python machine learning framework and MLOps workflow orchestrator designed to manage the lifecycle of data pipelines from local prototyping to production. It serves as a distributed compute manager and an experiment tracking system, enabling the creation of reproducible pipelines that transition between development and high-availability production environments. The framework distinguishes itself through an integrated checkpointing system that automatically persists intermediate data artifacts to remote storage, allowing failed runs to be resumed from the last successful step. It

Storm is a distributed stream processing framework designed to execute unbounded computations across a cluster to process real-time data streams. It functions as a data pipeline orchestrator that allows users to define and deploy declarative data flow graphs connecting streaming sources to processing components. The system operates as a multi-tenant distributed compute engine that isolates workloads and limits resource usage across shared clusters using dedicated pools and access control. It is also a secure distributed processing engine that employs encrypted node communication and SSL-secur

Tke is a container management platform that centralizes the administration, monitoring, and security of multiple Kubernetes clusters through a graphical console and command-line interfaces. It provides multi-tenant infrastructure capabilities that enforce security boundaries and resource isolation between separate teams and workloads across diverse environments. The platform includes cross-cluster project management by grouping multiple namespaces under unified project containers, alongside visual application deployment tools that run and edit workloads via graphical editors and configuration

The mlops-python-package serves as a standardized Python project template, data access abstraction layer, and workflow orchestrator for machine learning operations. It structures artificial intelligence workflows by connecting data processing and model training steps using directed acyclic graphs to manage execution order and inter-step dependencies. The framework manages external configuration files and global service contexts to control program execution parameters and share dependencies across the application lifecycle. It includes strict type schema validation for tabular data frames and

Mage AI is a Python-based data pipeline orchestrator and self-hosted data integrated development environment. It is designed for building, scheduling, and monitoring data workflows using a block-based pipeline design and interactive notebook interface. The platform distinguishes itself by integrating generative AI capabilities, allowing users to connect large language model providers via API to incorporate artificial intelligence into automated data streams. It also functions as an Apache Spark data processor, managing the kernels and infrastructure required for high-volume analytics and larg

Bisheng is an enterprise AI framework and LLM DevOps platform designed to manage the full lifecycle of large language models. It provides a unified system for dataset curation, supervised fine-tuning, model versioning, and performance evaluation. The platform features a visual workflow orchestrator for building retrieval-augmented generation pipelines and complex task sequences using flowcharts with conditional logic and human intervention points. It also includes an AI agent framework that uses a specialized guidance language to embed domain expertise and professional business logic into aut

Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis

This project is a comprehensive educational resource and tutorial handbook for building, training, and deploying machine learning models using TensorFlow 2. It serves as a structured learning guide covering core deep learning concepts, including neural network architectures, automatic differentiation, and tensor operations. The handbook provides technical guidance on optimizing execution efficiency through GPU memory management, distributed training, and model quantization. It also includes detailed manuals for constructing high-performance data pipelines and exporting models for production s

Pulse is an AI-driven infrastructure monitoring platform that unifies observation of Docker, Kubernetes, and Proxmox environments. It uses historical baselines and anomaly detection to scan infrastructure for actionable issues, and offers a natural language interface for querying system state. The platform distinguishes itself with agent-based auto-discovery—a single binary automatically detects container and virtualization hosts without manual setup. It supports approval-based remediation workflows, where AI-proposed fix commands are presented to the user and executed only after explicit aut

This project is an MLOps architectural guide and framework for designing and deploying deep learning systems into production environments. It provides a structured approach to model inference deployment, ML pipeline orchestration, and the creation of production-level machine learning architectures. The project distinguishes itself through a focus on distributed deep learning and edge AI optimization. It covers methodologies for parallelizing model training across multiple GPUs to handle large datasets and applies techniques like quantization and distillation to reduce model size for embedded