Overcoming Obstacles to Achieve Successful Self-Service Analytics
- Self-service analytics， for many organizations， is a longstanding goal. However， the path toward self-service is paved with treacherous obstacles. Technical professionals must overcome user reluctance， skill gaps， restrictive governance models， process immaturity and platform limitations.
- Technical professionals must find creative ways to get around roadblocks to success. Come to this session to learn how technical professionals are rising to the challenge of adopting self-service analytics in the organization.
Workshop: Boosting Your Machine Learning Efforts With Crowdsourcing
Human-in-the-loop crowdsourcing involves the integration of pools of human workers into the AI and ML process to improve data quality and algorithm training. In this workshop learn step-by-step how to integrate a crowdsourcing process into your machine learning efforts, using computer vision tasks as the primary example but with design principles and best practices that apply to a range of tasks.
Please Note: based on availability and eligibility you may sign-up for this session via Events Navigator after you register for this event.
Create a Data Strategy for Machine Learning Initiatives That Empowers Data Scientists
- Organizations struggle to use data effectively and efficiently to support machine learning and advanced analytics initiatives due to growing diversity in data projects.
- This session guides technical professionals on developing a data strategy to support successful machine learning deployments and answers the following questions: How to manage data when developing ML applications; What roles support the execution of the data strategy; How to support ML projects that that is democratized through APIs, AutoML, open-source frameworks and cloud services.
Don't Stumble at the Last Mile: Leveraging MLOps and DataOps to Operationalize ML and AI
- Organizations are investing huge time and resources in solving data science problems from hiring to choosing platforms to developing algorithms. However 80% to 85% of them are running into the last mile problem with model deployment and management.
- This session discusses key factors to mitigate the operationalization aspects of model deployment and management using MLOps and DataOps techniques.