Evaluating MLOps Platform

An MLOps platform's goal is to automate tasks associated with developing ML-enabled systems and to make it simpler to benefit from ML. Building ML models and gaining value from them requires several stages, such as investigating and cleaning the data, carrying out a protracted training process, and deploying and monitoring a model. An MLOps platform can be considered a group of tools for carrying out the duties necessary to reap the benefits of ML.

Not all businesses that benefit from machine learning use an MLOps platform. Without a platform, it is absolutely possible to put models into production. Choosing and introducing a platform for a particular project might occasionally be an unnecessary expense. Where there are several initiatives, platforms tend to be most helpful since sharing talents and knowledge is made simpler.

How do you evaluate the platforms?

The vast majority of tools, including ClearML, Censius, neptune.ai, Dataiku, Datarobot, Iguazio, Sagemaker, Valohai, etc., that market themselves as end-to-end MLOps platforms cover the three domains of tracking, versioning, ML pipeline, and model deployment. While MLflow, Flyte, Metaflow, and Seldon each concentrate on a particular stage of the model lifetime, end-to-end MLOps platforms integrate most of the model lifecycle into a single process.

Comparing features is one method of comparing MLOps platforms. While the top-level features of many of these platforms are nearly identical, the actual implementation of those functions varies greatly. Consequently, one might contrast the platforms based on how they present themselves. A different approach would be to determine whether you should create something from scratch or purchase an existing MLOps platform. The best response will depend on your use case and team.

Factors for Comparison of MLOPs Platforms

Following are the various factors for Comparison of MLOPs Platforms

Based on traditional ML and deep learning

Traditional machine learning-focused products are created for structured data. MLOps platforms for deep learning, on the other hand, are designed to handle enormous amounts of unstructured data, such as photos, videos, or audio. We have solutions like Metaflow, which excels at handling tabular data, and Valohai, a deep learning platform with a strong emphasis on machine orchestration.

Based on supported libraries

To create ML models, data scientists use a variety of computer languages, libraries, and frameworks. As a result, we require an MLOps platform that is compatible with the project's libraries. Let’s pick up the top libraries of ML and then lists out the platform that is compatible with that respective library.

• Jupyter − Dataiku, KubeFlow, Valohai

• Scikit-learn − Dataiku, DataRobot, KubeFlow

• Tensorflow − Dataiku, H2O, DataRobot, KubeFlow, Valohai

• Keras − Dataiku, DataRobot, Valohai

• Pytorch − H2O, DataRobot, KubeFlow, Valohai

• XGBoost − Dataiku, H2O, DataRobot, KubeFlow

Based on Productionization and Exploration

Platforms that are more geared toward exploration place a greater emphasis on data analytics, experiment tracking, and working in notebooks, whereas platforms that are more geared toward productization give priority to machine learning pipelines, automation, and model deployment. Seldon, Flyte, and Metaflow are production-oriented.

Flyte and Metaflow concentrate on creating production pipelines, but Seldon is solely for model deployment and versioning, not model training. While Dataiku has a lot to offer in terms of data analysis, MLFlow is mainly concentrated on experiment tracking.

Based on CLI (Command-line interface) and GUI (Graphical-user interface)

Some MLOps platforms concentrate on features that require less engineering know-how to develop and deploy ML models. They concentrate on the GUI, a visual tool that enables access through a web client. Other platforms cater to highly qualified data scientists with engineering backgrounds. When connecting these platforms with pre-existing tools, they frequently use a command-line interface (CLI) or API; for skilled users, a web user interface (UI) may not be crucial.

Below is the list of some MLOps platforms that are GUI or CLI-oriented −

• GUI − Dataiku, H2O, DataRobot, Iguazio

• CLI − Kubeflow, Valohai

Based on end-to-end platform and specialized platform

The majority of the MLOps platforms mentioned in this article take an end-to-end approach to MLOps, which means that users should be able to automatically train, test, and deploy models on a single platform.

In this comparison, Seldon, Flyte, and Metaflow stand out because of their greater specialization in either pipelines or deployment. Except for AutoML use cases, Datarobot is not truly end-to-end, and MLFlow is only just beginning to transition to an end-to-end approach.

Conclusion

There are numerous platforms available where you can create, train, use, and manage a machine-learning model. Although most platforms share many aspects and are closely related to one another, there are major distinctions.

For beginners, some platforms are incredibly simple. Every platform has advantages and disadvantages. It's a personal decision because your model accuracy will be similar regardless of the platform you use. Although there are several workflows, you can import your algorithm. Pricing is a key issue here because most of them offer a pay-as-you-go option that lets you just pay for the features you actually utilize.