Abstract: To turn AI into a core part of your business, you’ll need to build the flywheel to scale your training data-creation and model-building processes. Whether you are training a self-driving car, building a voice assistant or a customer service chatbot, the steps needed to effectively train a machine learning model at scale and deploy in production with confidence remains the same.
In this session we will showcase how to successfully launch AI product with quality training data:
· Identify key success factors when scoping a machine learning project
· Determine what kind of source data you need to make it successful
· Select annotation tools that best fit your project
· Monitor and audit quality of your training data
· Automate your training data pipeline
Bio: Meeta is a passionate, customer-obsessed product leader with a track record of strategizing, building and launching innovative products that solve real business problems.
As VP Product at Appen she is building a machine learning data annotation platform focused on Computer Vision, Autonomous Vehicles, Conversational AI and NLP. Prior to Appen, Meeta held several product leadership roles in Cisco Systems, Tokbox/Telefonica and Computer Associates with a focus on AI, Chatbots, Voice/Video and Data Analytics. She has an MBA Degree from UC Davis and engineering degree from National Institute of Technology, India.