ML platforms are used for automating the delivery lifecycle of predictive applications that have the capabilities to process the data. ML platform allows scientists to build the blocks and find solutions for any of the data science or analytical problem. The platform thus helps the data scientists to get comfortable with the environment and the platform in order to find effective solutions that can be incorporated into various products. The various ML platforms are Neptune, Amazon SageMaker, Cnvrg.io, Iguazio, Spell, MLflow, TensorFlow, Kubeflow, Databricks, and many more, some of them are discussed below along with the advantages and disadvantages of each one of them.
Neptune: One of the best MLOps tools or platforms to prepare, deploy, and monitor various experiments is Neptune. It is a management tool which helps to keep track of the machine learning experiments and manage the metadata of the model. This is a flexible tool which works with many other frameworks, enabling great scalability.
· Pros:
o Neptune has a fast and beautiful UI which offers a lot of capabilities which would help to organize and manage the tasks in more effective ways and save the dashboard and work which could be shared with other teams.
o It is a robust software and platform that helps to store all the data at one place making it easier to collaborate and also flexible with experimenting the ML models.
o This software automatically records the code, the environment, keeps track of the parameters, the metadata, checks on the evaluation metrices generated after model building, and also has a great visualization capability that can keep track of the various models and experiments built in various environments.
· Cons:
o Neptune AI may not be sufficient when it comes to full-time projects, or relatively longer projects. They are designed in a way which handles various experiments and can track the experiments only.
o Another issue with Neptune AI in terms of technical aspect is that user needs to take care of the synchronization of the experiments between the offline and online mode or versions manually.
o Neptune AI is not completely or fully an open-source platform and an individual version of the same would be enough for private usage only.
Cnvrg.io: Cnvrg is an end-to-end ML platform which is used to build and deploy the AI models which helps the teams to manage, build and automate the machine learning algorithms and models. One can run and track the experiments in high speed with the advantage of using any compute environment, framework, programming language, or tool and requires no configuration.
· Pros:
o Cnvrg.io organizes the data all in one place and helps collaborate with the team in an effective way.
o The real-time visualization feature helps to visually track the models as they are being automated and that can be shared across the teams for further analysis.
o This ML platform stores the models and the meta-data which includes the parameters, code version, etc. in an efficient manner and tracks the changes to automatically record the code and parameters.
· Cons:
o Lack of proper documentation for the initial setup of the software.
o According to customer feedback, the software has missing features and need to work on the implementation of the same.
References:
Patel, B. (2022, November 7). 10 Best Machine Learning Platforms in 2022 [Comparison]. Homepage. https://www.spaceotechnologies.com/blog/machine-learning-platforms/
Kijko, P. (2022, October 11). 8 Best Data Science and Machine Learning Platforms For MLOps. neptune.ai. https://neptune.ai/blog/8-best-data-science-and-machine-learning-platforms-for-mlops
Neptune.ai | Metadata Store for MLOps. (2022, November 3). neptune.ai. https://neptune.ai/
Welcome to cnvrg.io. (2019, June 22). cnvrg.io Docs. https://app.cnvrg.io/docs/
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