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1 & 2 APR `20 STATION BERLIN
10 Apr 2019 | 15:10 - 15:30 | Big-Data.AI Summit

From Ideas to Production - Lessons Learned from our Machine Learning Projects

Joan Clarke Stage
Stage BAS

Issue: Data science projects fail due to incompatible expectations of the team and stakeholders. Actions points: 1. Identify roles andskills in the team 2. Expectation Management. Data science is not a magic box 3. Data Strategy. Management and development team need to have an active role during the project and this needs to be created based on customers status and needs 4. Identify use cases. Data science can answer questions, but what are the questions we need to answer during the the project? Practical implementation: 1. Create the right environment: Data Lakes as foundation for data science projects 2. Create the right processes: a.Data Strategy: understand where the customer is at the moment and what journey he would like to start b. Data Science Lifecycle: Exploration → Feature extraction → Training Model → Evaluation /Prediction c. Production: Testing, Monitoring, Logging, Automation 3. Tools/Technologies: Python, Jupyter,Spark/Hadoop, Hive, Kafka → over the cloud S3, Kinesis and Athena,Kubernetes-Lessons

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