Simple & Transparent Pricing | Fully Signed NDA | Code Security | Easy Exit Policy
4 to 6 Years of Exp. Kotlin Developers
Kotlin is Google’s brilliant programming language for Java virtual machine, officially supported by Google and actively developed by JetBrains. It is an effective and efficient language that supports various solutions to diverse problems faced by Android developers. Kotlin is the statically typed open source programming language that perfectly fits in server-side web application development. From reduce in bugs to the improved the readability of code and lessen the development time, Kotlin is undoubtedly a rising star for the Android mobile application development.
Bacacny Technology holds a pool of skillful kotlin app development enthusiasts who have in-depth knowledge and extensive experience in building functional, user-friendly and tailor-made solutions.
- Server Side Kotlin is being used to build server-side applications in a more expressive and concise way. Programmers can deploy Kotlin apps to all the Java web application hosts
- Kotlin/Native Kotlin-Native allows the Kotlin code compilation into native binaries. It is specifically aiming those platforms where there is no requirement for virtual machine. Our adroit developers can build iOS apps using Kotlin because Kotlin -native is fully compatible with Objective-C.
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.