Azure data lab for data science: 2wk assessment

Rubicon B.V.

Azure data lab for AI and data science assessment. An approach and architecture for data science and advanced data analysis in a professional, exploitable and secure way.

You want to drive innovation, optimize business and obtain cutting-edge insights. A proven approach for data science is important to you, from experimenting to exploiting. In addition to a professional and flexible environment in Azure to accelerate data science, machine learning and AI.

A data lab assessment is the first step to start your data lab in Azure. Our proven assessment and data lab approach is based on experiences gained with Azure data environments at large corporations and governments.

Deliverables (assessment result):

  1. Architecture advice data lab
  2. Implementation plan
  3. Data science approach

Outcomes:

  1. Professional deployment of a data lab in Azure based on best-practices.
  2. Being prepared to take on future data challenges regarding big data, data science, AI and machine learning.
  3. Ensured continuity (IT management)
  4. Approach for data science, from experimenting to exploiting.

Approach: The data lab architecture and data science approach is set up in consensus with all stakeholders to meet business and scientists needs and to comply to your company and industry IT standards. Several workshops are provided in which the following topics are discussed, among other topics and points of attention.

  1. Demarcation data lab (define scope).
  2. Obtain requirements from future data lab users.
  3. Company and IT industry standards
  4. Positioning data lab within the current and future data architecture.
  5. Dependencies with other systems and infrastructure components
  6. Deployment approach and continuity plan
https://store-images.s-microsoft.com/image/apps.29670.7c6615e9-87b2-49ac-8d78-08a47c3d870b.116c527a-db05-46f7-9656-1e6155d9e5f6.f97fbeb2-6d51-4a7e-9434-6ceabea2eed4
https://store-images.s-microsoft.com/image/apps.29670.7c6615e9-87b2-49ac-8d78-08a47c3d870b.116c527a-db05-46f7-9656-1e6155d9e5f6.f97fbeb2-6d51-4a7e-9434-6ceabea2eed4