Innovative companies strive for an AI factory to minimize the cycle time and cost of analytical models from ideation to production deployment. At the heart of these AI production lines is an Analytical Data Platform that provides the essential processes, environments and tools.
Achieving competitive advantages through the use of advanced analytics requires mature processes for the automated identification, development, deployment, and monitoring and maintenance of advanced analytics models. This will only succeed through standardization via use case blueprints, data and model governance (including data privacy), and efficient processes. The next key building block is the standardized implementation of sophisticated data integration processes through data pipelines. It is important to keep in mind that the requirements for model development and deployment are completely different. To map all these requirements, an analytical platform with extensive functionality is needed, which requires end-to-end integration into the system landscape of the respective customer.
Analytical Data Platforms as central services offer a number of advantages
Deploying an automated data science workplace to accelerate model development
Data ingestion through metadata driven ingestion and integration with a data catalog to provide data pipelining as a service
Data and data management are available centrally in one place: significantly shorter setup time for new projects, easier management of access permissions, firewall permissions are already set up, etc.
Simplified machine learning operations using orchestration, AI model and feedback loop services.
Provision of shared services for authentication, continuous delivery, continuous integration, logging & monitoring, data & model management
Log management and notifications to log all data accesses and make them available for auditing purposes
Integrated CI/CD pipelines for reliable, fast and reproducible deployment of machine learning models incl. integration into operational systems
Ensuring data privacy through strict separation of machine learning development and machine learning operations
At synvert saracus, we have a wide range of experience in designing, setting up and operating data analytics platforms. Regardless of whether you prefer an on-premise, or cloud solution, or you want to assemble your platform from various components. We have the relevant experts, best practices and the necessary experience to deliver.
As a vendor-independent company, we advise you comprehensively and independently of software manufacturer interests, solely geared to your goals and the concrete needs of your company. Through strategic partnerships with leading cloud platforms and tool vendors, we are able to offer you a wide repertoire of possibilities.
Especially because cloud-based solutions scale very well, costs need to be kept under control. synvert saracus has the necessary experience to select a technology and service stack (e.g. complementing with opensource components) to balance performance requirements and budget constraints.
When planning and implementing an Analytical Platform, synvert saracus is very meticulous about the automation and reusability of all system components.
The analysis of data opens a large window for optimizing existing business processes, saving costs or improving the customer journey. But to ensure that this window does not close again immediately, synvert saracus takes the topic of data privacy into account from the start of the project during all phases and in every decision.
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