What is AutoML

What is AutoML?

Automated Machine Learning Uses and Approaches Explained As organizations seek to use machine learning in more diverse use cases, the amount of pre and post data processing and optimization scales exponentially. The difficulty to hire enough people to do all the tasks associated with advanced machine learning models makes  automated tools for machine learning a critical component for the future …

Where eBay Went Right—and Wrong—With AI: What You Measure Matters

Where eBay Went Right—and Wrong—With AI: What You Measure Matters

The following is adapted from Real World AI. I joined eBay back in 2006, and in 2009, the company was in very bad shape. Its share price was at a historical low, well off its near-$24 historical high; it was cutting costs, growth was negative, market share was shrinking, and the technology team wasn’t empowered to innovate. Put simply, the …

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The Current State of AI 2021: Report Now Available

What You Need to Know About the Seventh Annual Industry Report In our seventh edition of the State of AI and Machine Learning Report, we continue to evaluate an industry that is changing rapidly year-over-year. Our goal with the report is to provide a current picture of the trends, priorities, and challenges faced by businesses building AI technology. Once again, …

Appen State of AI 2021 – Making Machine Learning Work in The Real World

State of AI 2021 – Making Machine Learning Work in The Real World

The State of AI 2021 report is showing clear signs that companies are going all-in on AI. In this time of transition during the pandemic, we set out to explore why companies are considering AI to be core to survival. Amongst the findings, we saw that the maturing of AI projects, the desire for faster and greater ROI, and the …

Types of Errors We See with Training Data

Types of Errors We See with Training Data: How to Recognize and Avoid Common Data Error

It’s helpful to contrast AI development with traditional software development. In traditional software, you write code that’s deterministic (i.e., every time you run it with the same inputs, you’ll receive the same outputs). But with AI development, it’s not the code that’s most important, it’s the data. Specifically, the labeling of the data. High-quality, accurately-labeled data is crucial to building …

Appen Quality Controls Managed Services

Appen Quality Controls for Managed Services

Level up your training data quality by harnessing the combination of Appen’s expertise, thorough QA processes, and technology platform. At Appen, we provide a holistic approach to maintain and improve the quality of your annotated data by addressing quality drivers at every stage of the annotation process. With Appen Managed Services, your AI team can get quality training data without …

appen data annotation quality controls

Quality Controls for Appen Data Annotation Platform

High-quality training data is the foundation needed to run successful AI and ML models and yet quality assurance is often an overlooked critical component of the data labeling process. Appen Data Annotation Platform (ADAP) provides highly accurate training data for every use case leveraging industry-leading quality controls. Download the Quality Controls for Appen Data Annotation Platform data sheet to learn …

Responsible AI Across the Value Chain

Responsible AI Across the Value Chain: Ethical Approaches to AI from Data to Deployment—and Beyond

It can’t be stated enough: it’s the responsibility of every organization creating artificial intelligence (AI) to do so ethically. Responsible, or ethical, AI is AI that’s unbiased, equitable, and improves the quality of life of everyone it touches. In practice, it requires AI practitioners to apply an ethical framework to every AI endeavor they pursue, ensuring the people, processes, and …