Check out these other great articles!
To qualify, businesses must be at least 51% Asian-owned and headquartered in one of these Texas counties: Collin, Dallas, Denton, Tarrant, and surrounding areas.
CareerBuilder + Monster files for bankruptcy and will sell parts of business. Traditional job boards are being flooded with low-quality applications. Hiring is shifting toward targeted sourcing and internal mobility strategies.
StaffDNA announced today that it will take over the per diem staffing business, including the active and passive candidate database of Seattle, WA-based Kevala.
Machine learning (ML) is transforming industries, from healthcare to finance, with applications like fraud detection, language translation and predictive analytics. However, despite its vast potential, ML comes with challenges that can compromise performance, accuracy and fairness. Experts highlight ten common pitfalls in ML projects. Here are three key risks—model bias, poor data quality and scalability issues—where Sheldon Arora, CEO of StaffDNA, shares his insights:
Bias in machine learning models occurs when systematic errors lead to inaccurate predictions, often due to unbalanced training data. Ensuring diverse and representative datasets is essential for fairness. Sheldon Arora emphasizes:
“Data used to train machine learning models must contain accurate group representation and diverse data sets. Too much representation from any one given group results in not accurately reflecting the population. Continuously monitoring model performance ensures equitable representation from all demographic groups.”
High-quality data is the foundation of effective machine learning. Inaccurate, incomplete, or biased data can lead to flawed models and unreliable outcomes. Many organizations struggle with data trust due to unreliability issues. Arora stresses the need for strong data-cleaning measures:
“Data should be regularly scrubbed, and preprocessing techniques need to be implemented to ensure accuracy. Good data is the key to training models effectively and receiving reliable output.”
As machine learning adoption grows, ensuring systems can scale efficiently is crucial. Without proper infrastructure, models may struggle with larger datasets and increased computational demands. Arora highlights the role of scalable resources:
“Unless a company is using scalable cloud computing resources, they won’t be able to handle fluctuating amounts of data. Depending on the size of data sets, more complex models may be required. Distributed computing frameworks allow for parallel computations of large datasets.”
Read about all ten common machine learning pitfalls here.