New machine learning solutions are being implemented in the automotive industry to gather data that can forecast various requirements of the vehicle models.
FREMONT, CA: Applied machine learning is widely acknowledged in the automobile sector and used to develop new intelligent products and better ways of working. The amount of data generated by connected vehicles are enormous.
Together with other vehicle data, this data can be used to create models that forecast when maintenance is required, for example, or to categorize driver behavior. The feature store is a new component of automotive machine learning (ML) systems and a new data science tool and method for developing and deploying improved ML models in the automobile industry.
Machine learning beyond just self-driving cars
Traditional business models are being disrupted by changes in consumer behavior and technological advancements. As a result, carmakers, dealers, and other organizations in the automotive ecosystem must react fast to the changing environment, accepting challenges and opportunities by leveraging data.
The current generation of automobiles are software-enabled, data-generating, connected gadgets, presenting new (data) product and service opportunities. Automotive data science does not only refer to self-driving cars. Data science and machine learning technology can help carmakers stay competitive by enhancing everything from research and design to manufacturing and marketing.
To obtain the maximum value of the money from the data generated by vehicles and consumers, automotive sector firms must innovate with data management. Data acquisition, unification, and insight are all essential parts of the innovation process. In addition, the Internet of Things (IoT) and connected technologies will have a significant impact on automobile development.
The need for a feature store for data management
Data complexities and associated risks of technical debt and inefficiencies will increase based on where the automotive companies are in their path of managing data pipelines, ML-platform integration, and effectively putting ML models into production in intelligent apps.
If companies look at the best practices of hyperscale AI businesses, they will see that they all had a similar need to create a feature store or a central repository/data warehouse for machine learning. By making ML features reusable, cost-efficient, verifiable, controlled, and searchable, the feature store allows an operating model that accelerates ML projects.
As features are searchable by all possible users in many business domains, a feature store supports the reusability of features across the organization. It becomes possible for model creation and model servicing teams to identify, store, and manage their different feature sets.