With the rising popularity of AI applications, preparation of data to train the machine learning models is becoming increasingly important. In a recent Ovum survey, data scientists have said that they are spending up to 80 percent of their time finding, cleaning, and reorganizing data and only 20% in actual data analysis. Even before this data can be prepped for AI, it has to be accessed through a proper governance model to make sure only right people have access. Once the data has been processed, it has to be integrated with rest of AI life cycle. Doing it all in a single platform speeds up the model building and deployment process.
In this webinar, you will hear, what challenges data scientists are facing in preparing data for AI and how IBM's Data Refinery - built within Watson Studio and Watson Knowledge Catalog - is addressing the issue. You will also see a demo of key features of the product and how Watson Studio provides an integrated platform for complete AI life cycle.
Paige Bartley, Ovum
Paige is a Senior Analyst in Ovum's Data and Enterprise Intelligence team specializing in all aspects of the data lifecycle including creation, cleansing, security, privacy, and productivity. Working across the information management space, Paige researches how data use affects both large organizations and individuals alike. She provides insight and analysis into data ROI and successful organizational strategy. Paige’s other areas of expertise include data prep, regulatory and legal matters, data quality, unstructured data and NLP, master data management, records management, and neuroscience and cognitive science. Prior to joining Ovum in 2016, she worked in research and marketing for ZL Technologies.
Carmen Bommireddipalli is a Senior Offering Manager for IBM Watson Studio. In this role, Carmen works to build the next generation of self service data prep and analytics tools. Before moving to offering management, she led several software development teams in the data science and machine learning field.