Bug Blog

Check out the latest news in software testing, design, development, AI and ML.

Data Preparation for AI and ML Models: The Key to Success

by 
Ash Conway
Oct 27, 2023
In the world of artificial intelligence (AI) and machine learning (ML), there’s an oft-repeated saying: “Garbage in, garbage out.” This phrase encapsulates the importance of feeding high-quality, well-prepared data into your models. Without proper data preparation, even the most sophisticated models can fail.
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The Benefits of Outsourcing Data Labelling and Data Annotation

by 
Ash Conway
Oct 26, 2023
In today's rapidly evolving digital age, data is the backbone of many technological advancements, especially in fields like artificial intelligence (AI) and machine learning (ML). The quality and accuracy of data fed into these systems can significantly determine their effectiveness. This is where data labelling and data annotation come into play.
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Differences between Data Labelling and Data Annotation

by 
Ash Conway
Oct 20, 2023
In the rapidly evolving world of machine learning and artificial intelligence, data stands as the backbone of every model and application. Properly structured and classified data can make the difference between an efficient AI model and a lackluster one.
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The 101 Guide to Training Large Language Models

by 
Ash Conway
Aug 29, 2023
In the realm of artificial intelligence, large language models have emerged as powerful tools for various applications, from chatbots and content generation to translation and data analysis. These models have revolutionised the way we interact with technology and process vast amounts of text data. However, behind the scenes, training these models is a complex endeavour that involves a range of considerations and challenges.
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The Pivotal Need for Continuous Training, Validation, and Testing in AI/ML Models

by 
Ash Conway
Aug 24, 2023
In the fast-evolving landscape of artificial intelligence and machine learning, the development of robust models goes beyond the initial build phase. Continuous training, validation, and testing have emerged as essential practices to address inherent challenges that can affect the reliability and fairness of AI/ML models.
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The Crucial Role of Accurate Data Annotation in AI and ML: Navigating the Pitfalls of Inaccuracies

by 
Ash Conway
Aug 20, 2023
In the realm of Artificial Intelligence (AI) and Machine Learning (ML), accurate data annotation is the bedrock upon which groundbreaking advancements are built. As these technologies continue to reshape industries and redefine human-machine interactions, the significance of reliable data annotation cannot be overstated.
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Validating Machine Learning Models: Ensuring Reliability and Performance

by 
Ash Conway
Aug 11, 2023
Machine Learning (ML) models have become powerful tools for making predictions and solving complex problems. However, the effectiveness and reliability of these models rely on thorough validation.
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Training Machine Learning Models: Unleashing the Power of Data

by 
Ash Conway
Jul 12, 2023
Machine Learning (ML) has become an indispensable tool in various domains, from healthcare to finance. At the heart of ML lies the training process, which involves leveraging data to create powerful models capable of making accurate predictions.
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The Importance of Testing in Machine Learning: Ensuring Accuracy, Robustness, and Ethical Deployment

by 
Ash Conway
Jun 26, 2023
Machine Learning (ML) algorithms have revolutionized numerous industries, from healthcare to finance. However, the effectiveness and reliability of ML models heavily rely on rigorous testing.
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