Written by Ashir ZahidiJunior Engineer
Machine Learning Operations (MLOps) is a relatively new practice that revolves around models and automation. Therefore, an additional value asset is anything else required to make that model useful, including capabilities for an automated development and deployment pipeline, monitoring, lifecycle management, and governance.
When it comes to MLOps, the topmost priority is to make easily scalable models. Such models are also easy to deploy to production. Many platforms can help make the process easier, and one such platform is Kubeflow.
Kubeflow is the perfect platform to build and experiment with ML pipelines. It can work as an essential tool for ML engineers and operational teams to deploy ML systems to various environments for development, testing, and production-level serving purposes. One can easily use Kubeflow to deploy any machine learning project or model. However, during the process, one may encounter duplication and waste of effort, which requires storing vast amounts of data. For example, the team we worked with took daily screenshots of Apache Parquet files. It resulted in a lot of wasted data and also meant that every column in every file had to be manually changed retroactively to fix everything. Therefore, if your machine learning project will scale even moderately, we believe you should have a feature store. Watch this webcast to know more about Machine Learning Operations (MLOps) with Kubeflow.
Looking into a feast, first, we need to understand the goals of the feature store and how it could provide an additional advantage of using Feast with Kubeflow.
Share features between teams and use cases and reduces the duplicate effort
Reduce complexity when deploying a model in production
Decouples feature engineering from model development
Works with tools people are familiar with
Support real-time primitive feature
One of the most crucial and frequently underestimated parts of machine learning solutions is feature extraction and storage. Machine learning models rely on features to interpret and understand datasets for training and production. In modern machine learning solutions, a feature store is a trend that is becoming more common. A feature store, in theory, is a collection of features that can be used to train and evaluate machine learning models.
We believe you should have a feature store to scale your machine learning project even moderately. However, many projects do not require one. GO-JEK, like other fast-developing data science companies, is continually faced with feature extraction and discovery issues. You'd have to begin again if you wanted to use feature stores outside of a vast organization. Fortunately, the open-source community is already working to change that. However, many machine learning teams have their pipelines for fetching data, creating features, and storing and serving them. Still, in this article, I'll introduce Feast, an open-source feature store for ML, and show you how it resolves the difficulties.
To operate machine learning systems at large scale level, teams need access to the wealth of feature data to train their models and serve them in production. To resolve this issue, introducing the release of Feast, an open-source feature store that allows teams to manage, store, and discover features for use in machine learning projects and serve segments to models in production. The Feast is an essential component in building end-to-end machine learning systems.
In large teams and environments, how features are maintained and served can diverge significantly across projects, introducing infrastructure complexity and resulting in duplicated work. The difficulties are as follow:
A feast is a system to solve the critical difficulties with production machine learning. Feast solves these difficulties by providing a centralized platform that to standardizes the definition, storage, and access of options for coaching and serving. It acts as a bridge between information engineering and machine learning.
Feast handles the bodily function of feature information from each batch and streaming source. It additionally manages each warehouse and serves information basis for historical and also the latest data. Employing a Python SDK, users area unit ready to generate coaching datasets from the feature warehouse. Once their model is deployed, they'll use a consumer library to access feature information from the Feast Serving API.
The MLOps increases the quality, simplifies the management process and automates machine learning and deep learning models in large-scale production environments. Kubeflow also deploys ML systems to various settings for development, testing, and production-level serving. Also, Feast provides a consistent way to access features that can be passed into serving models and access features in batch for training. For more information about Kubeflow and its components, you can also reach out to us at [email protected] or visit www.royalcyber.com.