Open Source MLOps: Platforms, Frameworks and Tools
Blog post from Neptune.ai
Open-source MLOps tools provide cost-effective solutions for integrating DevOps practices into machine learning projects, offering a wide range of platforms, frameworks, and tools that cater to various stages of the ML lifecycle, from data exploration to model deployment and monitoring. These tools, such as Kubeflow, MLflow, Metaflow, and others, offer functionalities like experiment tracking, model serving, data validation, and automated machine learning, enabling developers to build scalable and reproducible ML pipelines. However, while these open-source options are often free, they may involve hidden costs related to infrastructure, support, and maintenance, and they may lack 24/7 vendor support. Careful selection based on compatibility with existing tech stacks and a thorough examination of each tool's pros and cons are crucial for maximizing the benefits of open-source MLOps tools.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| Kubernetes | 14 | 1,556 | 225 | 86 | -31% |
| Real-time | 5 | 3,344 | 937 | 222 | -51% |
| LLM | 4 | 3,765 | 540 | 172 | -11% |
| AI Guardrails | 2 | 155 | 63 | 38 | -30% |
| AI Model Fine-tuning | 2 | 671 | 147 | 64 | -4% |
| Serverless | 2 | 855 | 188 | 75 | -47% |
| Data Pipeline | 1 | 435 | 181 | 80 | -40% |
| Observability | 1 | 1,696 | 379 | 123 | -20% |
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