Today's day-to-day business activities are fully intertwined with digital processes. The number of those digital processes and their implementation as workflows is increasing rapidly, not least because of the growing importance of machine learning applications. Nowadays, analyses and forecasts are only started manually in the prototype state; a productive system relies on automation. Here, the choice of the workflow management platform is a key factor for long-term success.
The challenge is that these digital processes must be centrally managed and organized. Especially for business-critical processes, reliable execution and flexibility in the workflow design are essential. In addition to pure execution, great importance is also attached to the monitoring and optimization of workflows and error management. Ideally, the processes are also designed in such a way that they can be easily scaled up.
Only if both the technical and professional side of the users is involved, acceptance and a sustainable integration of digital processes into the daily work routine can be achieved. The execution as workflows should therefore be as simple and comprehensible as possible.
The new major release of Apache Airflow offers a modern user interface and new functions:
Apache Airflow 2.0 further optimizes workflow management. The major release includes TaskGroups, the Taskflow API, Smart Sensors and much more.
Customer analyses are essential to align marketing strategies. Read how Machine Learning helps with customer segmentation using RFM-analysis
Corona has changed the day-to-day routine of companies. How does this affect your planning cycles and planning tools? Can machine learning models provide support here?
Data is the new currency these days. With SAP HANA Predictive Analysis Library (PAL) the full potential of enterprise data can be exploited. Read more about this in our blog article!
SAP HANA External Machine Learning (EML) as part of the SAP HANA Application Function Library (AFL) allows the execution of already trained TensorFlow models
SAP Analytics Cloud for Planning can be used to create time series forecasts in a story. We introduce the forecast function and refer also to a machine learning approach to improve forecasts
In this article we take a look at predictive analytics functions (Machine Learning, Augmented Analytics and Smart Predict) in the context of SAP Analytics Cloud
Machine Learning with SAP Predictive Analytics enables companies to forecast future events and make data-driven, informed decisions. In this article we will highlight the possibilities and limitations of this tool.
Find out in our white paper how you can bring machine learning and SAP BW together to generate added value for your company.
Our Recap of PyCon DE / PyData 2019. We can recommend the conference for all Python users in the data analytics environment and machine learning area.