Learn how CloudFactory’s managed workforce worked with 3 companies, each with a problem involving data, automation, and/or ML.
An incremental design approach to automation and machine learning affords strategic opportunities for choosing to route exceptions to machines or people.
Automation and AI hold great potential to innovate, improve, and make predictions. Here are three mistakes you’ll want to avoid.
What will 2021 bring to the world of AI and machine learning? CloudFactory CEO and founder Mark Sears shares our predictions.
How can you determine if a data labeling service will deliver quality work? How they communicate and handle quality control are key indicators.
How can you determine if a data labeling service will deliver quality work? It starts with their vetting, hiring, and training processes.
People have unconscious biases that affect hiring decisions. People also can hard-code their biases into an AI system. Humans in the loop can help.
People are involved in everything from training and testing algorithms to labeling data, conducting quality control, and monitoring automation.
Humans play a critical role throughout the AI lifecycle, from data cleaning and labeling to quality control and automation monitoring.
Developing ML models requires a lot of data and skilled people to work with it. Here’s our HITL approach for machine learning model development.
Whether you’re a seasoned pro or new to artificial intelligence, continued learning important. Here are a few of our favorite machine learning & AI books.
No matter how robust your initial training may be, keeping your machine learning models up-to-date is essential. Here are two retraining approaches.
Why is artificial intelligence just now taking off? These three forces are fueling AI techniques to build real-life applications.
The combination of big data and machine learning can unlock the value of data you already have to gain a competitive edge for your business.
Data Scientist is the Sexiest Job of the 21st Century...Just Don’t Tell Data Scientists. Data Scientists spend 60% of their time on cleaning up data, while another 19% is ...
