Gartner Catalyst Conference

The Data Management Conference For Technical Professionals

16 - 17 September 2019 / London, UK

It is increasingly difficult to support evolving business operations, new initiatives and increasing technical requirements with existing data management infrastructure, practices and skills.

 

Technical professionals must build a data management architecture that can support changing and varied big data analytics needs. This architecture must accommodate both traditional and newer analytic techniques. It should be modular by design to accommodate mix-and-match configuration options as they arise.

 

At this year’s Gartner Catalyst Conference, learn how to build and deploy APIs in a productive and controlled manner, discover proven patterns and principles for flexible and agile connectivity among applications and deliver robust integration both on-premises and in the cloud. Plan your agenda, and leave with a blueprint for success.

Technical professionals looking to modernize their data management infrastructure must:

 

  • Modernize existing data technology investments for strategic and tactical benefit
  • Develop new data analytics capabilities to meet emerging challenges
  • Evaluate big data management products and technology directions
  • Deliver an information management foundation, build next-generation data integration architecture and implement a big data technology architecture
  • Enable digital dexterity with integration platforms and technologies

 

Gartner Catalyst Conference takes a deep dive into the above trends, topics and more, offering a dynamic live context to ask questions, vet ideas and proactively problem-solve with Gartner's experts and peers.

 

Transform your data management skillsets, achieve more with limited resources and guide the technology transformation of your organization.

As a big data architect, here are the relevant sessions for you:

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.

Register today to avoid disappointment