In 2021, off-the-shelf datasets will be on the rise for AI model development

If there's one thing that companies large and small can agree on, it's that deploying effective artificial intelligence (AI) is challenging. Not every organization has the funds, specialized teams, and annotators required for a large-scale AI deployment, and even those that do struggle with collecting enough high-quality data to build accurate models quickly, or update them with the right frequency. Deploying and maintaining AI with speed is essential for a competitive advantage in this rapidly-evolving space, which is why many companies are looking to third-party options that enable them to scale quickly. In particular, organizations are increasingly relying on off-the-shelf, or pre-built, datasets to provide needed data conveniently with limited risk. These datasets are cost-effective alternatives that can accelerate deployments and provide that last percentage or two of accuracy required to meet desired confidence thresholds.