Agile Development & DevOps
Drastically changing, disruptive technologies and business landscapes are driving enterprises to make a rapid transition from legacy infrastructure. Organizations are looking for a viable solution that is agile, flexible and scalable to remain competitive. Over the past few decades, the software development life cycle (SDLC) has moved on from periodic maintenance and traditional project based implementations to rapid delivery and continuous improvements. DevOps is the new software engineering approach to increase collaboration and improve productivity with MadhuRebba DevOps offerings.
With Madhurebba DevOps solutions, enterprises can break down the barriers and won’t need to work in silos. Our IT solutions standardize, automate and orchestrate development as well as operational tasks for continuous improvement. Whether it is application monitoring, infrastructure deployment or rigorous testing, we offer revolutionary aspects of state-of-the art frameworks that are highly scalable and robust in nature. We thrive on fulfilling the requirement of our clients and it is our firm endeavor to deliver the most prudent development solutions most suitable for your business.
Develops unites the inaccessible functions of software development life cycle (Dev) and operations (Ops) into continuous, an integrated and single process. Continuous delivery tools prevent the development hurdles and allow web as well as mobile application providers to improve speed, quality, and responsiveness. Continuous Integration as well delivers functional and operational enhancements rapidly, compressing the development life-cycle to improve the delivery performance
MadhuRebba DevOps consulting services empowers a continuous workflow from development to operations by confirming more predictable releases. MadhuRebba thrive on fulfilling the expectation of our clients without compromising the quality. At MadhuRebba Technology, we enable engineering teams and organizations to deploy features faster with supreme quality. We work with your team to understand your organization’s maturity level, project goals, and analyze the current state before setting a roadmap. Hire DevOps developers from us to uncover previously masked people and process communication issues and generate favorable business outcomes.
Is 15 days risk-free trial period completely free?
Apart from prediction and classification, in what other projects I can use AI and ML?
- Image Processing (Correct image quality, Image Analysis, Image Synthesizing, Image Captioning)
- Text Generation (For Q&A, Chatbot Response, Text Summarization)
- Video Processing (Identifying actions and humans present in the video, Video summarization)
How much data is required to build an AI and ML-based solution?
What specific type of data is required to implement AI and ML?
- Tabular data
What are the limitations of AI and ML?
- Unavailability of a large number of training samples.
- Labeling of Data – As deep learning and conventional machine learning algorithms are supervised, i.e., they need data and their label to capture the semantics of work to be done. It is a manual process and eats up more time than actually building models. It also adds biases in data as humans are prone to error when it comes to accurately annotating data, e.g. Annotating car parts for detecting damage. Model build with such data generally doesn’t converse with reasonable accuracy.
- Adopting Generality – Ml/DL algorithms are not able to produce the same results when deployed to different scenarios than the scenario used while training. So, to make it work in a different situation, a retraining model is required.
- Unable to explain what is going on inside the model and hence challenging to debug. However, various analytical tools can help with this.