Katya Dunets, the Data Science Practice Lead at Adastra uncovers insights from big, structured, and unstructured data as well as perform advanced and predictive analytics to help organizations derive value.
The ML development lifecycle consists of three key pipelines: data preparation, modeling, and operationalization. MLOps principles aim to standardize and automate each of these areas.
MLOps brings business interest back into the forefront of ML operations, where ML experts and data scientists work through the lens of an organizational interest with clear direction and measurable benchmarks.
Nearly 60% of supply chain professionals anticipate that their departments will rely on AI in some manner by 2025 - MHI.
The impact of COVID-19 caught everyone by surprise. Organizational leaders have been forced to swiftly adapt their business processes to adhere to their respective government’s mandates and recommendations.