In this article, we shed light on why automation makes a significant contribution to the successful implementation of Data Science projects and thus why problems caused by a lack of automation can be elegantly circumvented. This is important because new or ongoing software projects can quickly fall into a less productive state during the integration phase. The solution is: automation.
Many of our customers tell us again and again about their bad experiences with software projects. Often the reason for the failure or lack of success is the lack of sufficient data know-how (Data Roles and Data Skills), lack of communication and complex processes and missing Automation in the integration of software. This usually leads to so-called "integration hell". This term describes a state in which several versions of the same software can only be merged and tested with considerable development effort.
Automation or DataOps - a process improvement approach in software development and system administration - are solution approaches in this context that provide clear Effort and cost saving for already running or new software projects. This saves an enormous amount of time and minimises the "time to market". The advantage of automation is that developers do not have to worry about code deployment, i.e. the software delivery process, every time they make changes to the code. This is why the term "continuous deployment" is used here.Continuous software development." Continuous Deployment is part of Continuous Delivery - originally defined by Jezz Humble and David Farley as follows:
"Continuous delivery: Reliable software releases through build, test, and deployment automation."
To this day, this is the basis for more efficiency and productivity in software projects.
Link tip: Using data properly: How to secure competitive advantages with the data journey model.
Goals, benefits and added value of automation
The automation of software projects offers various advantages:
- Increase efficiency
- Improving productivity
- Time saving
Today, software, programmes and code stand between the plans and development of new products and their implementation. That is why solutions that keep these processes efficient are important. One of the biggest challenges here is that there is a gap between the environment where the code is developed and the Deployment, or operation, can vary greatly. For this reason, one of the central goals of automation in the area of Software developmentto keep critical software projects up and running. The "bogging down" of running projects can be avoided or completely eliminated through automation.
This is made possible by one-click deployment, which combines quality control, integration and testing during development. At the same time, this measure leaves more Time for the actual further development of the software because manual processes are minimised. New software features can be delivered faster with the help of automation. Automation thus increases the overall Efficiency of software development and improves the Productivity.
The principle of "DevOps", i.e. the merging of Development and DataOps, combines several of the aspects relevant here, such as:
- Continuous Integration,
- Continuous Testing,
- Continuous Deployment and
- Continuous monitoring
The great advantage of this method is that faults can be detected and rectified more quickly and at an early stage. In addition to the error detection, in particular a better Scalability and Reliability this approach. The earlier experts introduce the relevant principles into a project based on their experience, the lower the risk for a project to get stuck at a later stage. Without approaches such as Continuous Delivery or Continuous Deployment, extreme time and budget expenditures can often occur in the course of a project, for example, to merge development progress or carry out tests.
What sets us apart
Continuous delivery, continuous deployment and automation in the development of software and data science projects are standard for us. This is mainly because we deal with the challenges described here on a daily basis. At Alexander Thamm GmbH, we mainly carry out data science and AI projects and develop use cases - in other words, usually prototypes. Only those use cases that are productive create real Added value. So after a proof-of-concept has been developed, which only marks the feasibility of a project in theory, it is essential to scale it up and then implement it successfully in an organisation.
Conversely, this means for us that we already include important principles such as automation and DataOps in the development of the use case. This is the only way to ensure the smoothest and most successful transfer of the analytical concept into the productive implementation of the data product. In this way, we have already been able to gain concrete experience with automation in the development and integration of software in numerous projects. That is why we are very familiar with the advantages and disadvantages of the technologies that can be applied here.
In Data Science Trainings and workshops, we pass on our knowledge and experience with the topics of continuous deployment and automation. We offer training on the following individual topics:
- Basic principles of DataOps
- Continuous Delivery & Deployment
In the form of coaching, we can provide our customers with best practices at the beginning of software projects or support them in concept development. In addition, we can provide support for projects that are already underway - for example, through pair programming, setup of continuous delivery & deployment or troubleshooting.