The whole goal of using this tool is to speed up the conversations and decision making. You do not want to make this a long activity. This tool has a 1-3 weeks timebox to be completed. And the next step is to plan for this work Learn how to talk to your data scientists Discover business opportunities with AI Build your AI Strategy with the AI Canvas Launch your first AI project. Stop when you are ready to move forward or put a break on this idea. The way to get there is bumpy and challenging, though. Once you conclude that this idea is worth to be explored, you should extend your footprint to other areas that bring expertise and knowledge about solutions and work that needs to be done.Īny work that needs to be done in order to complete the canvas, is tracked on the Kanban board at the bottom of the canvas. SageMaker also allows our central data science team to collaborate and evaluate the models created by business users before publishing them to production.The Agile Project Canvas created by Ardita Karaj is a structured tool that helps project team thinking, discussing and defining the scope in the beggining of any project.Īccording to LeanPub when a business identifies a new project need, you should start having some initial discussions over the Agile Project Canvas and focus mostly on boxes 1, 2,3. You can work on it as a team or individually to quickly evaluate an existing project, develop an innovative new idea, or inform a funding application. With SageMaker Canvas, our business users can easily explore and build ML models to make accurate predictions without writing any code. We believe Amazon SageMaker Canvas can add a boost to our AI/ML, scaling across the BMW Group. In the past few years, we have industrialized many top BMW Group use cases measured by business value impact. The company already employs AI throughout the value chain, enabling it to generate added value for customers, products, employees and processes. "The use of Artificial Intelligence as a key technology is an integral element in the process of digital transformation at the BMW Group. It also provides premium financial and mobility services. The BMW Group, headquartered in Munich, Germany, is a global manufacturer of premium automobiles and motorcycles, covering the brands BMW, BMW Motorrad, MINI, and Rolls-Royce. The code and data can easily be sent to the data science team through Amazon SageMaker Studio, allowing them to integrate models into their model management system and see a full picture of models enterprise wide.”Ĭaleb Wilkinson, Lead Data Scientist, INVISTA The canvas focuses on both the business and data science side of AI Projects and holds. The intuitive user interface and easy-to-navigate options of Amazon SageMaker Canvas allow business users to import a variety of data, minimize the need to manually clean up data, and apply a variety of algorithms to find the model that best fits the data with a few clicks. The Project Initiation Canvas can be used to capture your first ideas on an AI Use Case. We foresee Amazon SageMaker Canvas empowering our business users and process engineers to start working on data science problems that were previously owned by the data science team. Equally important, however, was to ensure that our data science team had visibility into the models built so that they can support and productionize as needed. “Our business analysts are data savvy, and we needed the ability to let them create predictive models. The collaboration is important because it helps us productionalize more ML models and ensure all models adhere to our quality standards and policies.”ĭavood Naderi, Data Science Team Lead at Industrial Applications, Siemens EnergyĪ subsidiary of Koch Industries since 2004, INVISTA brings to market the proprietary ingredients for nylon 6,6 and recognized brands including CORDURA and ANTRON. We found Amazon SageMaker Canvas a great addition to the Siemens Energy machine learning toolkit, because it allows business users to perform experiments while also sharing and collaborating with data science teams. This enables us to increase the speed of innovation and digitalization of our energy solutions such as Dispatch Optimizer and Diagnostic services. “The core of our data science strategy at Siemens Energy is to bring the power of machine learning to all business users by enabling them to experiment with different data sources and machine learning frameworks without requiring a data science expert. They are transforming in key focus areas of environmental, social and governance (ESG), and their innovation is making the future of tomorrow different today, for both their partners - and their people.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |