Generative AI is a gateway to infinite possibilities. With generative AI, computers can learn from existing datasets to create documents, images, videos, and music. It’s the technology behind virtual assistants, personalized content, and automated code creation.
Generative AI is the key to new gateways of creativity, innovation, and opportunities. Explore Generative AI roadmap for career aspirants to follow and become experts in Gen AI.
Before we explain the Generative AI roadmap for success, here is a word of advice for those who want to learn Generative AI and build relevant skills.
Individuals planning a career switch to Generative AI can opt for Interview Kickstart’s Applied Gen AI course. Under the umbrella of Applied Gen AI, separate options are provided for software engineers and product managers.
Career aspirants can pursue the Advanced Gen AI course of Interview Kickstart to enhance their skills. The course provides a detailed understanding of LLMs, diffusion models, multimodal models, and reinforcement learning concepts.
Both courses are created and taught by tech leads and hiring managers of top-tier companies in the world.â€
Generative AI Roadmap: Why Learn Generative AI?
If individuals learn Generative AI they get positioned at the forefront of technological innovations. Generative AI equips them with skills and knowledge to contribute to significant advancements in the digital world.
- Tech Advancements: Generative AI brings with it a leap in technological advancements particularly in its ability to produce complex outputs in the forms of audio, video, images, and text documents.
- Cultivate Innovation: By leveraging the capabilities of Generative AI, creators can innovate in the fields of design, content creation, and product development.
- Problem-solving: Generative AI can be used to solve complex problems that occur while training models, automating repetitive tasks and optimizing workflows.
- Increase in Demand: As its demand increases with every passing day, learning Generative AI opens doors to diverse and rewarding career paths in industries like tech, entertainment, and healthcare.
- Stay Relevant: When we learn generative AI, we equip ourselves with skills that will remain in demand in upcoming years. So, by doing so, we remain relevant in this ever-evolving job market.
Also read: Generative AI Training: A Complete Guide to Upskilling Your Workforce
Generative AI Skills
The generative AI roadmap requires individuals to acquire specific generative AI skills needed to succeed in this field. Here are the skills individuals need to learn step-by-step to get started with Gen AI.
Generative AI Roadmap: Learn NLP Fundamentals
Start by understanding the basics of Natural Language Processing (NLP) fundamentals including different types of embeddings and vector spaces. NLPs form the basis for many advanced large language models (LLMs) so understanding their fundamentals is crucial for gaining relevant machine-learning skills.
A few embeddings are:
- Frequency-based embeddings: Work on the principle of proximity, which means words that occur together are likely to have similar meanings.
- Prediction-based embeddings: This embedding works on predicting the next word based on the context of the sentence.
A few vector spaces are:
- Sequence-to-Sequence Models (Seq2Seq): Works on encoder-decoder format wherein an encoder processes the input sequence into a fixed-length context vector, and the decoder subsequently generates the output sequence based on this vector.
- Transformers: Transformers work on self-attention mechanisms in which they allow the models to measure the importance of different words in a sentence when processing inputs.
Generative AI Roadmap: Understanding Large Language Models
Understanding the architecture and pre-training methodologies of LLMs is essential to know their capabilities and leverage their power effectively.
Start by understanding the functioning of different models such as GPT4, Dall-E 3, and Gemini and identify their key differences to choose the most suitable one for your use case.
Each LLM comes with a unique architecture and pre-training approach designed to fulfill specific objectives. By understanding these architectures, you can distinguish between LLMs from other AI models and get an understanding of their unique capabilities.
Generative AI Roadmap: Fine Tuning LLMs
As part of Generative AI skills, one must understand how the fine-tuning process involves retraining the pre-trained LLM on a smaller, domain-specific dataset or with task-specific examples. This retraining helps the model adjust its parameters to understand and generate outputs relevant to the target domain or task.
LLMs are trained on vast amounts of data and possess broad, generalized knowledge. Fine-tuning allows them to deliver specialized outputs and incorporate new information.
Despite its effectiveness, fine-tuning can be resource-intensive and time-consuming, especially when dealing with large models and datasets.
Also Read: The Impact of Large Language Models on Industry
Generative AI Roadmap: Retrieval Augmented Generation (RAG)
An alternative technique called Retrieval Augmented Generation (RAG) was proposed to address challenges faced in fine-tuning LLMs.
RAG quickly retrieves relevant information from external sources to supplement the generation process, thus reducing the computational burden of training and fine-tuning large models. This information source can be a retrieval system or a knowledge base which is integrated with the LLM.
This information source comes into use when a user query exceeds the LLM’s scope or needs specific datasets. The RAG system retrieves relevant information from this external source and appends information to the user query to enhance its context.
The LLM then processes this enhanced query. This approach gives LLM access to a wider knowledge base enabling it to generate more accurate and relevant responses.
Generative AI Roadmap: Prompt Engineering
Traditional queries don’t usually work with LLMs. When users communicate with LLMs they need to create effective prompts that guide LLMs in generating meaningful responses.
Prompts require consistent input formatting and are designed to be reusable and standardized. The same prompt can be reused without the need to rewrite them.
The objective of prompt engineering is to effectively communicate the requirement to the LLMs by minimizing ambiguity and maximizing relevance.
In addition to prompt engineering one can access the relevance and efficacy of prompts by following these evaluation techniques:
- Human Feedback: Assess the quality, relevance, and coherence of the LLM’s generated responses by taking feedback from humans.
- Perplexity: Measuring the uncertainty of responses generated by LLMs, with the lower the perplexity, the better the performance.
- BLEU Score: The BLEU score calculates the similarity between the LLM’s generated responses and responses of human evaluators.
By mastering prompt engineering and gaining appropriate information about evaluation techniques, developers can fine-tune their models, improve their performance, and ensure they meet desired quality standards.
Generative AI Roadmap: LLM Tools and Frameworks
An LLM framework or orchestration tool is a software designed to help developers to manage and work with Large Language Models (LLMs).
Developers need to understand the tools and frameworks used for creating and deploying Large Language Model (LLM)-based applications. These tools provide a structured environment and ready-to-use features that make the development process smoother and deployment easier.
The LLM framework is like the foundation of the development process. It helps bring together different parts of the project and ensures everything from development to deployment moves as per the initial plan.
Generative AI Roadmap: Deployment of LLMs
LLMs can be deployed by making them available through application programming interfaces (APIs) or by utilizing cloud hosting services. By doing so, users can interact with LLMs without being aware of their inner functionalities.
For instance, users interact with LLMs like GPT-4 or Gemini, without knowing about the inner workings of these tools.
APIs expose the functionalities of these LLMs through endpoints. Users communicate with these endpoints by sending requests which these models process and generate relevant outputs. APIs are used to integrate LLMs into web applications, mobile apps, or other software systems.
Cloud hosting services take away the need of end users by setting up the infrastructure required to deploy these LLMs. Instead, these services handle the deployment, scaling and monitoring of applications.
Such services, namely Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure are offered on subscription basis to end users.
Roadmap to Learn Generative AI
Once you’ve understood the fundamentals behind NLPs, LLM tools and frameworks, and prompt engineering basics, now lets look at the roadmap to learn generative AI:
- Step 1: Understand machine learning basics including supervised and unsupervised learning, neural networks, and handling, cleaning and preparing data for training of algorithms.
- Step 2:Â Master Python programming language including its tools and frameworks necessary to implement, manage, and optimize AI models. Practice to write cleaner and faster code.
- Step 3: Explore data science to analyze large datasets and gain relevant insights foundational for building effective generative models.Â
Learn techniques used in data visualization, feature engineering and machine learning pipelines. - Step 4: Work on fundamentals related to math and statistics including linear algebra, calculus and probability. These concepts are necessary to model uncertainty and make informed predictions.
- Step 5: Get comfortable with AI development tools to make you work faster, more efficiently and in a more collaborative manner. Emphasize debugging and testing to ensure that the output is accurate and correct.
- Step 6: Work on hands-on real-time projects that allow you the opportunity to work with generative models and further enhance your Gen AI skills.
- Step 7: Attend coding bootcamps that provide intensive knowledge and practical skills to succeed in the Generative AI role.
- Step 8: Embrace ethical AI development to ensure your projects are fair, unbiased and transparent.
- Step 9: Engage with online Gen AI communities to gain valuable insights about the latest trends and techniques.
Also Read: Essential Python MCQs with Answers for Aspiring Developersâ€
Learn With Interview Kickstart â€
If you’re a Machine Learning Engineer, a Data Engineer, or a Data Scientist, Interview Kickstart offers a range of courses.
There is a machine learning course, which teaches everything from basic machine learning concepts to advanced ML frameworks.
If you are looking to revisit or master data science skills, you can take up our data science course to learn everything from
Candidates seeking a career in data engineering can opt for this comprehensive data engineering that covers all basic and advanced data engineering concepts.
FAANG instructors teach these courses in which they cover everything from basic to advanced concepts related to these AI disciplines.
These are some ideal tech interview prep courses for aspirants planning to pursue a career in AI.
Read the success stories of candidates who have cleared tech-intensive interviews in FAANG+ companies.
FAQs: Learn Generative AI
â€Is Generative AI a Good Career?
Generative AI is quickly becoming a non-negotiable skill in the 2024 job market. Whether you’re a seasoned professional or just starting in your career, now is the time to acquire this skill and start your Gen AI career.
â€Which Language Is Used In Generative AI?
â€Python is an ideal programming language for Generative AI. Its simple, flexible, high-level, and interpreted language, making it preferred for rapid development and testing.
â€What Are Gen AI Tools?
Generative AI tools are software and platforms designed to create new content including text, audio, video, photographs and high-quality codes.
â€What Is the Salary Of Gen AI Developer in USA?
A Generative AI Engineer earns an annual salary of $115,864 a year or approximately $55.70 an hour.
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