- Data Scientists look into the data, extract features and try to find models which best fit the data to achieve the predictive and prescriptive insights they seek out. They prefer a scientific approach by defining hypotheses and verifying or rejecting them based on the data. They need tools for data wrangling, parallel experimentation, rapid prototyping, data visualization, and for training multiple models at scale.
- Developers and machine learning engineers aim for a clear path to incorporate and use the models in a real application or service. They want to ensure that these models are running as reliably, securely, efficiently and as scalable as possible.
- Data engineers do the work needed to ensure that the right data is always up-to-date and accessible, in the required amount, shape, speed, granularity, with high quality, and minimal cost.
- Business representatives define the outcomes to guide the data scientists’ research and exploration, and the KPIs to evaluate if the machine learning system is achieving the desired results with the desired quality levels.
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Sato,Wider,Windheuser_2019_Continuous-delivery_thoughtworksesses in the workforce: To develop machine learning applications, experts with complementary skills are necessary, and they sometimes have contradicting goals, approaches and working processes: <span>Data Scientists look into the data, extract features and try to find models which best fit the data to achieve the predictive and prescriptive insights they seek out. They prefer a scientific approach by defining hypotheses and verifying or rejecting them based on the data. They need tools for data wrangling, parallel experimentation, rapid prototyping, data visualization, and for training multiple models at scale. Developers and machine learning engineers aim for a clear path to incorporate and use the models in a real application or service. They want to ensure that these models are running as reliably, securely, efficiently and as scalable as possible. Data engineers do the work needed to ensure that the right data is always up-to-date and accessible, in the required amount, shape, speed, granularity, with high quality, and minimal cost. Business representatives define the outcomes to guide the data scientists’ research and exploration, and the KPIs to evaluate if the machine learning system is achieving the desired results with the desired quality levels. Continuous Delivery for Machine Learning (CD4ML) is the technical approach to solve these challenges, bringing these groups together to develop, deliver, and continuously improve machin Summary
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