MLOps, or DevOps for machine learning, is a set of practices that aim to automate and improve the collaboration between data scientists and software engineers. It helps organizations better manage the complexities of developing and deploying machine learning models in production. In this article, we will review the top-5 most trending open source MLOps tools listed on OSSInsight.io in 2022.
Benefits of MLOps
One of the biggest benefits of MLOps is that it allows data scientists and engineers to work more closely together. Data scientists can focus on developing models, while engineers can focus on operationalizing them. This collaboration helps to ensure that models are deployed quickly and efficiently and that they meet the needs of the business.
Another benefit of MLOps is that it helps to automate the process of model development and deployment. This means that data scientists can spend less time on repetitive tasks, and more time on developing new models. Automation also helps to ensure that models are deployed consistently and with high quality.
Best 5 MLOps Tools in 2022
Implementing MLOps can be a challenge, but the benefits are clear. Organizations that adopt MLOps practices can improve the quality of their machine learning models and speed up the development and deployment process. In this article, we select the top-5 most trending open source MLOps tools in 2022 listed on OSSInsight.io, namely Jina (No.1), MLFlow (No.2), NNI (No.3), Kubefliow (No.4) and Label Studio (No.5). The rank is based on the stars, pull requests, pull request creators, and issues on the Github in 2022. OSSInsight is a powerful insight tool that can help one analyze in depth any single GitHub repository/developers, compare any two repositories using the same metrics, and provide comprehensive, valuable, and trending open source insights.
- Jina from Jina AI is the top-1 most trending MLOps tool according to OSSInsight. Jina is an MLOps framework for multimodal AI. It eases the building of neural search and creative AI on the cloud. Jina uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Despite the advanced cloud-native features that Jina offers, learning Jina is very straightforward. Document, Executor, and Flow are three fundamental concepts in Jina.
Document is the fundamental data structure. (This project is also an opensource project by the Linux Foundation)
Executor is a Python class with functions that use Documents as IO.
Flow ties Executors together into a pipeline and exposes it with an API gateway.
With these three concepts, one can easily build a semantic text search with sharding technology in just 45 lines of code!
With these three concepts, one can easily build a semantic text search with sharding technology in just 45 lines of code!
With Executor Hub, one can easily use LLMs or pretrained models on Hugging Face to embed Documents. However, in practice the performance is often suboptimal without proper domain adoption or knowledge transferring. Fine-tuning is an effective solution to improve the performance on neural search and embedding-related tasks. Jina AI also provides Finetuner tools makes fine-tuning easier, faster and performant by streamlining the workflow and handling all complexity and infrastructure on the cloud.
Finally, Jina AI also offers a hosting service for Jina projects, allowing one to deploy CPU/GPU-based Jina Flow with auto-provisioning in Kubernetes. It is in public beta and the hosting is for free at the moment.
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