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AI transforms the automotive industry
With our approach we support companies in all project phases and make sure to place the highest value on quality, knowledge transfer and transparency. Long-term project success with effective AI strategies are the result of our collaboration with industry leaders.
AI Strategies for Automotive
Optimizing and personalizing the customer experience with AI has become a central goal of companies in the automotive sector. Therefore, long-term AI strategies are essential. As a multi-cloud service provider, we support companies in the automotive sector in evaluating existing and future AI use cases. Our aim is to ensure the consistent use of AI, to build an AI use case portfolio and to define evaluation criteria for various parameters such as business value or time-to-market - from strategy development to the implementation of cloud solutions.
At Reply, we empower industry leaders to deliver excellent solutions in Artificial Intelligence, platform business, and data strategies.
Creating an agile data-driven mindset with AI
In addition to the technical execution and the development of an AI use case roadmap, we conduct change management to engage employees and stakeholders in current developments. We give workshops on the overarching topics of "Artificial Intelligence" and "Agility" in order to create a data-driven and digital mindset among employees and stakeholders. In each sprint review, we ensure that potential project risks and impediments were transparently presented. In this way, they can be directly addressed to the right stakeholders and preventively averted.
Solving data science challenges through basic principles
We perform a technical "Platform Maturity Check" for our customers to verify whether the desired use case can be implemented: This involves checking the existing architecture for its maturity level with regard to nine different dimensions: data procurement, data preparation, labeling, model training, testing, deployment, model usage, authorization control & operation authorization.
This allows us to identify weaknesses in the architecture and define the areas where improvements need to be anticipated. For the go-live, we use four methods to reduce complexities. These include: "inheritance mechanism”, unification of data structure, use of component approaches, uniform deployment and monitoring.
Inheritance
mechanism
Unification of
data structure
Use of component
approaches
Uniform deployment
and monitoring
Availability and quality via monitoring and automation
Initially, we place a high priority on establishing reliable monitoring. This includes:
Monitoring of the application (logs, "golden signals", distributed tracing for performance monitoring).
Automation of monitoring via alarms (e-mail, SMS, pager)
Monitoring of predictions and quality control with labels
Control of the distribution of predictions
Monitoring of input data: Control of the distribution of features
Control of important metrics like the number of missing values
In the next step, we automate all processes that were previously performed manually:
Automatic deployment of the models, e.g., with Canary releases.
Automation of infrastructure provisioning (infrastructure-as-code)
Demand-oriented, automated retraining of the model
With our approach we support companies in all project phases and make sure to place the highest value on quality, knowledge transfer and transparency. Long-term project success with effective AI strategies are the result of our collaboration with industry leaders.