The concept of a feature store in data science and artificial intelligence is relatively new but has its root in the field. In the early stages, features for AI models were typically hand engineered by data annotators and stored in various formats such as CSV, spreadsheet or databases. However, this way of storing data features found difficulties in data management, sharing and reusability.
In the early 2010s, the field witnessed the rise of the big data concept and the increasing popularity of MLOps, which led to a need for specialised data storage systems for data features, and this is how feature stores found their way to fit in between the data and the ML models.
What is a feature store?
In one of our articles, we explored this topic. Still, in simple terms, a feature store is a technology we use to manage data more efficiently, particularly for machine learning models or machine learning operations.
As we all know, MLOps is a way to compile or structure all technology pieces together in order to deploy, run, train and manage AI models. An MLOps system majorly consists of components of
- Machine learning
- DevOps (development operations)
- Data Engineering
Empowering a feature store in the MLOps can increase the craftsmanship of ML teams because the data flow can be more easily managed.
How do feature stores work?
Typically, data used to be stored somewhere on servers, and data scientists could access the data when going for data analysis, or server users could access it to display the data. But as big data came into the picture, such storage facilities and recalling became less feasible.
In the real world, we can see that AI and ML fulfil different kinds of use cases which leads to the requirement of different datasets in different formats. Fulfilment of this need requires a lot of data transformation and a combination of different tables to generate a standard feature set to serve a model.
These common data-related challenges that an AI project faces when putting ML models into production:
- Access to the right raw data
- Getting correct features from raw data.
- Compiling feature into training data.
- Managing features in the production
Feature store helps in solving these problems and fulfilling the data demands made by the models and ML workflow. Using the feature store, data scientists become capable of:
- Verify the validity of the data.
- Check the quality.
- Re-use the data.
- Versioning and control of data.
Nowadays, in MLOps, feature store has become a compulsory technology to fit between the data source and machine learning model because it accommodates the accessibility that a machine learning team seeks.
Where data is the lifeblood of any ML or AI model, a feature store helps ML and AI teams to:
- Collaborate efficiently
- Reduce data duplication
- Develop faster
- Compliance with better regulations
Features of a Feature Store
Till now, we have majorly discussed the need for a feature store in MLOps, and when we talk about the features of any feature store then, these are the following feature a feature store should consist of.
Capable of data consumption from multiple sources
In real life, it has been observed that there are multiple data sources of companies, and from those sources, only a few data are usable for AI and ML models. In that case, a feature store should be capable of extracting and combining important data from multiple sources; this means the feature store should be able to be attached by many sources. A feature store can consume data from
- Various streams
- Data warehouses
- Data files
Data transformation
One of the key benefits of applying a feature store in MLOps is that it helps data scientists easily get different types of features together to train and manage their ML models.
As we know, data is being gathered from different sources in different formats, while s feature store fit in between models and data sources transforms data and joins the models in consistent ways and also enables data monitoring. For example
Gathering data features from different sources is not the only task a feature store performs but also enables data teams to transform the data in the required form. One-hot encoding, data labelling and test data encoding are a few data transformation techniques which value only an ML engineer can understand.
Here feature store verifies the consistency of data transformations while data analytics and monitoring ensure data is applicable for modelling.
Search & discovery
Feature store is one of the ways to encourage collaboration among DSA and ML teams. It simply enhances the reusability of data features because once a set of features is verified and works well with a model, the feature set becomes eligible to be shared and consumed for other modelling procedures that can be built for completing different purposes.
A good feature store is always provided with a smart sharing setting that ensures the important features are standardised, and reusability is increased.
Feature Serving
Features stores should not only be capable of extracting and transforming data from multiple sources, but also they should also be able to pass data to multiple models. Generally, different APIs are used to serve features to the models.
Since models need consistency in features served to them, so in serving a check is important to verify the data fits the requirements of the models.
Monitoring
Finally, one of the most important features that should be applied to any block of code is accessibility to monitoring. A feature store should be provided with appropriate metrics on the data, which can discover the correctness, completeness and quality of the data that is passing through the feature store.
The aim of monitoring is to become updated, debugged and advanced about the system.
Conclusion
If you go through this article, then you will get to know that The MLOps is a set of many blocks and steps that need to work Parelally when a machine learning or AI model is going to be deployed into production. Serving data in these steps or blocks in one of the first steps of the whole procedure can define the reliability and accuracy of the whole procedure. So feature store becomes a requirement when you follow the practises defined under MLOps and require efficient results from it.
To give you a valuable experience of MLOps for real-life AI use cases, DSW | Data Science Wizards has come up with a state-of-the-art platform UnifyAI. This platform not only allows you to deploy AI and ML models into production seamlessly but also engages fewer technology components in your MLOps journey to avoid the complex engineering and get more focused on experimenting across all the models to get value-making data and AI-driven decisions.
The provided feature store with UnifyAI has all the key features that one optimised feature store should have and using this feature store, you can enhance the consistency, quality, serviceability and reusability of important features and can get high-quality results from your AI use cases.
About DSW
Data Science Wizards (DSW) aim to democratise the power of AI and Data Science to empower customers with insight discovery and informed decision-making.
We work towards nurturing the AI ecosystem with data-driven, open-source technologies and AI solutions and platforms to benefit businesses, customers, communities, and stakeholders.
Through our industry-agnostic flagship platform, UnifyAI, we are working towards creating a holistic approach to data engineering and AI to enable companies to accelerate growth, enhance operational efficiency and help their business leverage AI capabilities end-to-end.
Our niche expertise and positioning at the confluence of AI, data science, and open-source technologies help us to empower customers with seamless and informed decision-making capabilities.
DSW’s key purpose is to empower enterprises and communities with ease of using AI, making AI accessible for everyone to solve problems through data insights.
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