Machine Learning

Watch the recording to discover how to scale your data science projects, build efficient data pipelines, and automate ML workflows using Microsoft Fabric.

In today's highly competitive business environment, optimization is one of the most important keywords.

The concept of artificial intelligence (AI), which was popularized in science fiction and once portrayed as sentient robots with the ability to think and rationalize, has now found more realistic and practical applications. 

In this whitepaper, we look at some of the key myths and misconceptions that serve as roadblocks while organizations are evaluating the merit of AI/ML adoption.

Paying by mobile phone using NFC, usually Google Pay or Apple Pay has become a standard for many Czechs. Is there room to improve the UX when paying online?

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 digitization of banking does not mean that banks will spam their customers with product offers from morning to night.

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.