Inspiration
we aim to explain what advanced technology in business daily life helps management efficiency using the Continuous Integration and Continuous Development (CI/CD) pipeline functionality.
What it does
Our key finding is that the cloud platform in the depth of utility industry with a high-level data administration after integrating with information from resources significantly provides support to the service quality regarding the modern requirements from the environment, the society and the sustainable development.
How I built it
A segmentation operation models the systematic procedures into trackable objects.
Challenges I ran into
Automation of the decision-making process varies depends on whether the strategic execution has flexibility when reacting to external economic changes on the base of empirical and statistical analysis.
Accomplishments that I'm proud of
Further, expected reconciliation keeps in focus on designing the business concept, deploying architectural changes, and constructing baseline infrastructure to the technical evolution in the scale of lifecycle management.
What I learned
The rise of cloud infrastructure and the growing popularity of automatic software development improve the management experience through quick deployment of cross-functional cooperation. Development and Operations (DevOps) provides a solution to enable requirements from business contexts, technical challenge and on-going demands. Continuous Integration and Development (CI/CD) is a standard software engineering in the application of DevOps. As the central element of DevOps, it will present business-driven approach to support complex practical workflows and develop a simplified automation from reading in information until generating statistical outputs.
What's next for Cloud-based Risk Assessment for Asset Management
There are a few important questions to look at, such as 1) the way to evaluate the outcomes. Although currently we are on the testing stage, The platform could not determine the size of compute cluster size for particular jobs which are is critical and sufficient to run the analytical products successfully. Costs vary depending on how the users allocate their workloads. 2) There are many benefits to share the knowledge and information through a cloud-based resource pool among departments. What would be an optimal service model is a question to leverage the high-expected performance and architectural trade-offs. 3) To improve the user experience, ongoing extension of the development is necessary. Two factors, scalability of the services and efficiency metrics need to be considered in an end-to-end scenario. One project can easily to achieve its objective purposes. An interesting aspect of replicating production process to other applications is that it might be overfitted to configurations on Databricks. Therefore, minimising the latency of modification requires future investment on technical infrastructure.
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