AI Automation AI Modeling Data Quality 
Unite.AI
JUNE 12, 2024
The rapid advancement of AI raises questions about data protection, the integrity of AI models, and the safeguarding of proprietary information. Regulatory Needs : A substantial majority (88%) of respondents support increased government oversight of AI, particularly in areas related to security (72%) and privacy (64%).
Unite.AI
OCTOBER 22, 2024
The World Quality Report 2024-25 by OpenText sheds light on groundbreaking trends shaping Quality Engineering (QE) and testing practices globally. With over 1,775 executives surveyed across 33 countries, the report uncovers how AI, automation, and sustainability are transforming the landscape of quality assurance.
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Unite.AI
SEPTEMBER 18, 2024
AI can also track interactions with potential clients, ensuring that sales reps are reminded to follow up at the optimal time based on previous interactions and outcomes. This level of AI automation ensures no lead is neglected, maximising potential opportunities.
ODSC - Open Data Science
MARCH 5, 2025
Data Engineering: The infrastructure and pipeline work that supports AI and datascience. Data Management & Governance: Ensuring data quality, compliance, and security. Research & Project Management: Applying scientific methods and overseeing large-scale data initiatives.
Unite.AI
JUNE 17, 2024
You are known for emphasizing how empowering AI is, but most people fear losing their jobs. What are the skills that humans need to reinforce in order to not be replaced by AI? It's true that the specter of job losses due to AI automation is a real fear for many. Bias in AI models is a significant concern.
Pickl AI
DECEMBER 16, 2024
This blog will explore the intricacies of AI Time Series Forecasting, its challenges, popular models, implementation steps, applications, tools, and future trends. Key Takeaways AI automates complex forecasting processes for improved efficiency. Advanced algorithms recognize patterns in temporal data effectively.
ODSC - Open Data Science
MARCH 24, 2025
Challenges in Implementing AI atScale While AI presents exciting possibilities, integrating it into enterprise environments comes with significant challenges. Gary identified three major roadblocks: Data Quality and Integration AI models require high-quality, structured, and connected data to function effectively.
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