Welcome to our ADW

We can be a great way to minimize the costs and time required for each stage of ML development. It is easy to collect and annotate the massive amounts of data required for training machine learning (ML) models with human supports. Building an efficient machine learning model also requires continuous iterations and corrections. We analysis about ML development is human-in-the-loop (HITL), where human feedback isused to help validate and retrain your model. An example is drawing bounding boxes to build high-quality datasets for computer vision models, where the task might be too ambiguous for a purely mechanical solution and too vast for even a large team of human experts.

A large, seemingly overwhelming task can sometimes be transformed into a set of smaller, more manageable micro tasks that can each be accomplished independently. We can be an efficient organizational strategy to harness innovation and agility by distributing work to Internet users. Businesses or developers can use to access thousands of on-demand workers and then integrate the results of that work directly into their business processes and systems. Common examples include the moderation of web and social media content, categorization of products or images, and the collection of data from websites or other resources.