Identifying the Hidden Costs of Data Annotation Projects
Studies have shown that high-quality data annotations are one of most important factors in improving machine learning model accuracy. But selecting an annotation solution that meets your accuracy requirements, budget, and timeline can be overwhelming. There are often hidden costs that pop up when trying to scale annotations, resulting in project delays or additional expenses.
In this session we’ll highlight some of the hidden costs along with:
- The pros and cons of different annotation solutions (crowdsourcing, in-house, partners)
- Calculating the total cost of ownership of data annotation projects
- What to look for when selecting a data annotation partner
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Product Manager, 2D Image and Video, Sama
Megan McNeil combines her passion for delightful user experiences and innovative technologies in her role with Sama’s 2D image and video data labeling solutions. With 8 years of digital product development experience under her belt, Megan is well-versed in end-to-end product development implementation in both B2C and B2B enterprises with SaaS and custom solutions.
Sr. Solutions Engineer, Sama
At Sama, Ryan Tavakol helps ML teams improve their models through quality labeling and advanced data curation solutions. He has years of experience in consulting, solving complex AI and analytics problems across a variety of industries through innovative and data-driven solutions.
Lisa Avvocato is a veteran product marketer/moderator specializing in AI and ML technologies. She’s passionate about the interaction of machine learning and digital transformation strategies to reduce inefficiencies and drive sustainability. With over 15 years of experience in Enterprise SaaS technology, she has worked across a diverse set of industries including retail, education, manufacturing, and healthcare.