Artificial Inteliigence ( Machine Learning / Deep Learning )
Basics of AI, ML & DL
Artificial Intelligence aims to enable the machines to execute the cognitive functions of humans such as perceiving, learning, reasoning and solving the problems. The organization uses AI to automate mundane and repetitive tasks on relationship building, lead nurturing etc. Also, AI provides the cutting edge technology to deal with complex data that human cannot handle.
Machine Learning technology is used to train the machines to perform various actions such as predictions, recommendations, estimations etc., It enables the computers to behave like human beings by training them with the help of past experience and predicted data. ML techniques are divided as Supervised Learning, Unsupervised Learning and Reinforcement Learning.
Deep Learning is considered as a subset of ML. It has enabled the handling of large volume of structured and unstructured data in an efficient way. It is concerned with algorithms inspired by the structure and the function of the brain called artificial neural networks. It focuses on improving the AI process of having machines learn new things.
AI Practices & Usecases
AI play a critical role in the future of work. Implementing AI is an essential step towards creating optimized operational efficiencies that increase longevity. AI can resolve the issues in the organization like,
* AI help to create better products and services
* AI enhance process efficiencies
* AI will mitigate the risk and compliance
* AI improve the time to market
Once you allocate the resources in terms of time, costs, complexity and skillsets needed to build your AI models, then you can justify your business usecases. The optimized deployment of AI in each of the following areas,
* Machine Leaning (ML)
* Natural Language Processing (NLP)
* Natural Language Understanding ( NLU)
The success of AI implementation depends on the data samples that you collect for prediction. Think about the relation between your data and what you want to predict. AI is serving the business purpose by leveraging end to end automation process in diverse sectiors including healthcare, finance, automobile etc.,
AI Applications for Cloud-Native
Cloud native is an approach of develeping and operating the applications that encompasses the scalability, flexibility, and resilience of cloud computing delivery model. The Key principles of cloud-native development are Microservices, Containers, and Helm Charts. These cloud-native applications can be managed in production by Kubernetes. It allows the organisation to deploy, manage and scale the containerized applications. Each Solutions has specific configuration steps needed to ensure the cloud-native application can run effectively without issue. NVIDIA Fleet command helps to deploy the cloud-native application. It is a cloud service for managing the application across the edge locations. Once the application is built, there are 4 stpes to get that app on Fleet command,
Step 1. Containarize the Application
Step 2. Determine the application Requirements
Step 3. Build Helm Chart
Step 4. Deploy on Fleet Command
AI User Experience
Building AI based UX on diverse values and needs requires a thoughtful design. The technology you use should guide the UX that you want to achieve. It is to think about how people do the task today and figure out what's valuable, and you can enhance the experience. The user rely on your AI to make decisions. Ideally, the user should be able to trace the result back to the supporting data points. The layout of which data sources you use and the qualities the AI foucuses on etc., So, using the real user data for the prototypes helps you to build the ML on the right assumptions.