Our Data Sciences team is responsible for building, governing, and maximizing the use of Target’s data assets for elevated decision-making across our retail operations. We build certified data sets for analytics and decision modeling and develop data platforms for exploration, visualization and measurement. We create statistical forecasting models, optimize algorithms with machine learning techniques, and validate model performance – with both analytical AI and generative AI – all while delivering algorithmic decisioning at massive scale. We empower our Target team with the insights they need to improve product and system performance and further innovate to improve guest experience.
- Developing JupyterLab ExtensionsAugust 2, 2022By Arman ShahTarget’s technologists are encouraged to take advantage of “50 Days of Learning,” a program that enables engineers to spend time exploring new technologies or learning new languages and systems. I wanted to learn more about developing my own extensions and used some of my learning time to dive into the issue.
- Requirements for Creating a Documentation Workflow Loved by Both Data Scientists and EngineersApril 6, 2022By Colin DeanThis is an adaptation of a presentation delivered to conferences including Write the Docs Portland 2020, Ohio Linuxfest OpenLibreFree 2020, and FOSDEM 2021. The presentation source is available at GitHub and recordings are available on YouTube. This is a two-part post that will share both the requirements and execution of the documentation workflow we built that is now used by many of our teammates and leaders. Read part two here.
- Executing a Documentation WorkflowApril 6, 2022By Colin DeanThis post is the second in a two-part series about creating a documentation workflow for data scientists and engineers. Click here to read the first post. This is an adaptation of a presentation delivered to conferences including Write the Docs Portland 2020, Ohio Linuxfest OpenLibreFree 2020, and FOSDEM 2021. The presentation source is available at GitHub and recordings are available on YouTube.
- Using BERT Model to Generate Real-time EmbeddingsMarch 23, 2022By Pushkar Chennu and Amit PandeHow we chose and implemented an effective model to generate embeddings in real-time. Target has been exploring, leveraging, and releasing open source software for several years now, and we are seeing positive impact to how we work together already. In early 2021, our recommendations team started to consider real-time natural language input from guests, such as search queries, Instagram posts, and product reviews, because these signals can be useful for personalized product recommendations. We planned to generate representations of those guest inputs using the open source Bidirectional Encoder Representations from Transformers (BERT) model.
- Spring Boot Service-to-Service CommunicationDecember 18, 2018By Jeffrey Bursik and Pruthvi DintakurthiThis post will walk through our implementation of Spring Feign Client, our learnings, and how Spring Feign Client has helped manage our inner-service communication while reducing the amount of development time.