TechTarget: Data preparation in machine learning: 6 key steps
Story originally appeared in the TechTarget on the Jan. 27, 2022. Excerpts from the story below. To see the full story visit TechTarget.com.
Managers need to appreciate the ways in which data shapes machine learning application development differently compared to customary application development. “Unlike traditional rule-based programming, machine learning consists of two parts that make up the final executable algorithm — the ML algorithm itself and the data to learn from,” explained Felix Wick, corporate vice president of data science at supply chain management platform provider Blue Yonder. “But raw data are often not ready to be used in ML models. So, data preparation is at the heart of ML.”
In The News
- iTWire (Australia): How Blue Yonder is Applying AI and Innovation To Solve Supply Chain Challenges
- Modern Materials Handling: Blue Yonder’s Logan Kluth on the Real Value of Cloud WMS
- SiliconANGLE: The Road to Intelligent Data Apps With Blue Yonder’s Chief Product Officer, Gurdip Singh
- FreightWaves Running On Ice: Prioritizing The Vaccine Supply Chain
- Sourcing Journal: Blue Yonder’s Sónia Peres On AI/ML’s Role in Fashion, Retail
Analyst Reports
- IDC Market Analysis Perspective: Middle East, Türkiye, and Africa Supply Chain Management Applications, 2024
- IDC Market Analysis Perspective: European Enterprise Applications, 2024
- IDC Worldwide Supply Chain Management Applications Market Shares, 2023: Maintained Focus
- IDC Worldwide Supply Chain Integrated Planning Applications Market Shares, 2023: Orchestration Extended
- IDC Worldwide Supply Chain Warehouse and Inventory Management Applications Market Shares, 2023: Warehouse Automation
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