
Creating Web-Hosted Interactive Dashboards through Lovable
Build interactive, web-hosted dashboards effortlessly using Lovable without coding or complex tools.
ADaSci's Certified Generative AI Engineer program is an upskilling-linked certification initiative designed to recognize talent in generative AI and large language models. Participants engage in a structured learning track covering industry-relevant modules, culminating in an exam for certification by ADaSci. The program aims to elevate skills and provide recognition to professionals in this rapidly evolving field.
A person who is looking for recognition in the Generative AI field can appear and take this certification. This is a certification program offered by the premier global body of AI and data science professionals and is best suitable for the aspirants who want to start their careers in the data science field.
There are no fixed eligibility criteria for taking this certification. Anyone who is a Generative AI aspirant can take this progam. There is no limit on age, educational qualification, or working experience.
Yes. This is a course-linked certification program. You can undergo a Generative AI learning track included in this program and take the exam in the end for getting the certification
Yes, you can refer to all possible learning resources of your type for a widened knowledge of the field. However, the learning modules added to this program are sufficient to crack the exam.
You can register for this program online at any time by visiting the ADaSci website. Only you need to create your account, pay the program fees, complete the learning modules and take the exam for getting the certification.
We advise to take the exam within 1 year of registering. As the learning modules and resources may get time to time based on the development in the field.
Build interactive, web-hosted dashboards effortlessly using Lovable without coding or complex tools.
This article explores building a functional question generator using LangChain, Pydantic, Streamlit, and Python efficiently.
n8n is an open-source, low-code workflow automation platform that enables seamless integrations between applications using a visual, node-based system.
DAPO is an open-source RL framework that enhances LLM reasoning efficiency, achieving top-tier AIME 2024 performance with half the training steps.
SmolDocling, a 256M VLM, enables efficient document conversion using DocTags to preserve structure while reducing computation.
Chain of Draft (CoD) optimizes LLM efficiency by reducing verbosity while maintaining accuracy. It cuts token usage, lowers costs, and speeds up inference for real-world AI applications.
DeepSeek’s MLA reduces KV cache memory via low-rank compression and decoupled positional encoding, enabling efficient long-context processing.
OpenAI’s Agents SDK enables efficient multi-agent workflows with context, tools, handoffs, and monitoring.
Portkey enables observability and tracing in multi-modal, multi-agent systems for enhanced understanding and development.