Companies will explore and adopt the integration of DataOps across various data tools and data stakeholders to deliver faster business value. Along with this trend, we will see digitized data governance and containerization through automation and selfservice tools to place a greater focus on value delivery. As part of digitized data governance, we will see the integration of components like data lineage, data security and data quality, and environment abstraction with data and logic tests to support self-service and better quality of data services delivery.
To benefit from this trend, enterprises must align their entire data estate into DataOps pipelines, break the silos of data teams and data products and create a fully integrated data factory view. They must also evaluate and standardize tools and processes across the entire data estate. Adoption of modern technologies will help integrate DataOps and accelerate this journey.
A large retailer adopted DataSecOps as part of their modernization into the public cloud. They explored and adopted new technology capabilities to deliver faster business value from their data.
The AI/ML model’s lifecycle involves various stages – from data collection, data analysis, feature engineering and algorithm selection to model building, tuning, testing, deployment, management, monitoring and feedback loops. To improve DevOps maturity, AI/ML models are being integrated into the DevSecOps pipelines to be standardized, fully managed and controlled. Configuration management tools, data security and data privacy tools and services for AI/ ML models have also gained momentum. Other products like Amazon Macie, Pachyderm and TensorFlow are also being explored and experimented in DevSecOps pipelines.
A supply chain solutions company deployed AI and ML models on diverse platforms using an open-source software stack. This approach helped save 80% of deployment time, enabled elastic and containerized execution, thereby delivering better solutions for its customers.
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