Master the Future of Tech: Become a Cloud Native Applied Generative AI Engineer (GenEng and CNAI)

Version: 10.0 (Implementation and adoption starting from July 1, 2024)

Today’s pivotal technological trends are Cloud Native (CN) and Generative AI (GenAI). Cloud Native technology offers a scalable and dependable platform for application operation, while AI equips these applications with intelligent, human-like capabilities. Our aim is to train you to excel as a Cloud Native Applied Generative AI developer globally.

The Cloud Native Applied Generative AI Certification program equips you to create leading-edge Cloud Native AI solutions using a comprehensive cloud-native and AI platform.

This one-year program equips you with the skills to thrive in the age of Generative AI (GenAI) and cloud native computing (CN).

Why This Program?

What You’ll Learn:

Flexible Learning:

Program Structure (5 Quarters):

Generative AI is set to revolutionise our daily lives and work environments. According to McKinsey & Company, generative AI could contribute an annual economic value of $2.6 trillion to $4.4 trillion across various sectors by enhancing automation, bolstering decision-making, and providing personalised experiences. This revolution is pivotal for technology and job landscapes, making it essential knowledge in fast-evolving tech cycles. The rapid emergence of Gen AI-powered technologies and the evolving demand for skills necessitate extensive and timely professional training.

Cloud native is an approach in software development that enables application creation, deployment, and management in cloud environments. It involves constructing applications as a collection of small, interconnected services known as microservices, a shift from traditional monolithic structures. This modular approach enhances the agility of cloud-native applications, allowing them to operate more efficiently with fewer resources.

Technologies such as Kubernetes, Docker, serverless containers, APIs, SQL Databases, and Kafka support developers in swiftly constructing cloud-native applications. These tools offer a standardised platform for application development and management across various cloud services like Azure, Google Cloud, and AWS.

Advanced Specializations

Students will have the option of selecting one of the following specialisations after the completion of fifth quarter:

  1. Healthcare and Medical GenAI Specialization
  2. Web3, Blockchain, and GenAI Integration Specialization
  3. Metaverse, 3D, and GenAI Integration Specialization
  4. GenAI for Accounting, Finance, and Banking Specialization
  5. GenAI for Engineers Specialization
  6. GenAI for Sales and Marketing Specialization
  7. **GenAI for Automation and Internet of Things (IoT) Specialisation **
  8. GenAI for Cyber Security

Common Questions (FAQs) with Detailed Answers

  1. What is Cloud Native Applied Generative AI Engineering?

    Cloud Applied Generative AI Engineering (GenEng) is the application of generative AI technologies to solve real-world problems in the cloud.

  1. **How valuable are the Cloud Native Applied Generative AI developers? **Developers with expertise in Cloud Native Applied Generative AI were in extremely high demand due to the increasing adoption of GenAI technologies across various industries. However, the supply of developers skilled specifically in this niche area might not have been as abundant compared to more generalised AI or cloud computing roles.

    The demand for AI developers, especially those proficient in applying generative AI techniques within cloud native environments, has been rising due to the growing interest in using AI for creative applications, content generation, image synthesis, natural language processing, and other innovative purposes.

    According to some sources, the average salary for a Cloud Native Applied Generative AI developer in the global market is around $150,000 per year. However, this may vary depending on the experience level, industry, location, and skills of the developer. For example, a senior Cloud Applied Generative AI developer with more than five years of experience can earn up to $200,000 per year. A Cloud Applied Generative AI developer working in the financial services industry can earn more than a developer working in the entertainment industry. A Cloud Applied Generative AI developer working in New York City can earn more than a developer working in Dubai. In general, highly skilled AI developers, especially those specialising in applied generative AI within cloud environments, tend to earn competitive salaries that are often above the average for software developers or AI engineers due to the specialised nature of their skills. Moreover, as generative AI technology becomes more widely adopted and integrated into various products and services, the demand for Cloud Applied Generative AI developers is likely to increase.

    Therefore, Cloud Applied Generative AI developers are valuable professionals who have a bright future ahead of them. They can leverage their creativity and technical skills to create innovative solutions that can benefit various industries and domains. They can also enjoy very competitive salary and career growth opportunities.

  2. What is the potential for Cloud Applied Generative AI Developers to start their own companies?

    Cloud Applied Generative AI Developers have a significant potential to start their own companies due to several factors:

  3. Emerging Field: Generative AI, particularly when applied within cloud environments, is still an evolving field with immense potential for innovation. Developers who understand the intricacies of both generative AI techniques and cloud technologies can identify unique opportunities to create novel products, services, or solutions.
  4. Market Demand: There is a growing demand for AI-driven applications, especially those that involve generative capabilities such as image generation, content creation, style transfer, etc. Developers with expertise in this area can leverage this demand to create specialized products that cater to specific industries or consumer needs.
  5. **Innovation and Differentiation: **The ability to develop unique and innovative solutions using generative AI in the cloud can set apart these developers’ startups from more conventional companies. They can explore new ways of generating content, enhancing user experiences, or solving complex problems with AI-generated solutions.
  6. Access to Cloud Resources: Cloud platforms provide scalable and cost-effective resources that are crucial for AI development. Developers starting their own companies can leverage cloud services to access powerful computing resources, storage, and AI-related services without significant upfront investment.
  7. Entrepreneurial Opportunities: Developers with entrepreneurial spirit and a deep understanding of AI technologies can identify gaps in the market and build startups to fill those gaps. They can create platforms, tools, or services that simplify the adoption of generative AI for businesses or developers.

  8. Collaboration and Partnerships: These developers can collaborate with other experts in AI, domain specialists, or businesses to create innovative solutions or explore new application areas for generative AI in the cloud.

    However, starting a company, especially in a specialised field like Cloud Applied Generative AI, requires more than technical expertise. It also demands business acumen, understanding market needs, networking, securing funding, managing resources effectively, and navigating legal and regulatory landscapes.

    Successful entrepreneurship in this domain involves a combination of technical skills, innovation, a deep understanding of market dynamics, and the ability to transform technical expertise into viable products or services that address real-world challenges or opportunities.

    Developers aspiring to start their own companies in the Cloud Applied Generative AI space can do so by conducting thorough market research, networking with industry experts, building a strong team, and developing a clear business plan that highlights the unique value proposition of their offerings.

    To sum up, the potential for Cloud Applied Generative AI Developers to start their own companies is high.

  1. Why don’t we use TypeScript (Node.js) to develop APIs instead of using Python?

    We will not use Typescript in GenAI API development because Python is a priority with the AI community when working with AI and if any updates come in libraries they will first come for Python. Python is always a better choice when dealing with AI and API.

  1. Why don’t we use Flask or Django for API development instead of FastAPI?
    • FastAPI is a newer and more modern framework than Flask or Django. It is designed to be fast, efficient, and easy to use. FastAPI is also more scalable than Flask or Django, making it a better choice for large-scale projects.
    • FastAPI is also more feature-rich than Flask or Django. It includes several built-in features that make it easy to develop APIs, such as routing, validation, and documentation.
    • Overall, FastAPI is a better choice for API development than Flask or Django. It is faster, more scalable, and more feature-rich.

  2. Why do we need to learn Cloud technologies in a Generative AI program?

    Cloud technologies are essential for developing and deploying generative AI applications because they provide a scalable and reliable platform for hosting and managing complex workloads.

  1. What does the API-as-a-Product model entail?

    API-as-a-Product is a type of Software-as-a-Service that monetizes niche functionality, typically served over HTTP. In this model, the API is at the core of the business’s value. The API-as-a-Product model is different from the traditional API model, where APIs are used as a means to access data or functionality from another application. In the API-as-a-Product model, the API itself is the product that is being sold.

    The benefits of the API-as-a-Product model include:

  1. Why in this program are we not learning to build LLMs ourselves? How difficult is it to develop an LLM like ChatGPT 4 or Google’s Gemini?

    Developing an LLM like ChatGPT 4 or Google Gemini is extremely difficult and requires a complex combination of resources, expertise, and infrastructure. Here’s a breakdown of the key challenges:

    Technical hurdles:

    Massive data requirements: Training these models requires an immense amount of high-quality data, often exceeding petabytes. Compiling, cleaning, and structuring this data is a monumental task.

    Computational power: Training LLMs demands incredible computational resources, like high-performance GPUs and specialised AI hardware. Access to these resources and the ability to optimise training processes are crucial.

    Model architecture: Designing the LLM’s architecture involves complex decisions about parameters, layers, and attention mechanisms. Optimising this architecture for performance and efficiency is critical.

    Evaluation and bias: Evaluating the performance of LLMs involves diverse benchmarks and careful monitoring for biases and harmful outputs. Mitigating these biases is an ongoing research challenge.

    Resource and expertise:

    Team effort: Developing an LLM like ChatGPT 4 or Google Gemini requires a large team of experts across various disciplines, including AI researchers, machine learning engineers, data scientists, and software developers.

    Financial investment: The financial resources needed are substantial, covering costs for data acquisition, hardware, software, and talent. Access to sustained funding is critical.

    Additionally:

    Ethical considerations: LLMs raise ethical concerns like potential misuse, misinformation, and societal impacts. Responsible development and deployment are crucial.

    Rapidly evolving field: The LLM landscape is constantly evolving, with new research, models, and benchmarks emerging. Staying abreast of these advancements is essential.

    Therefore, while ChatGPT 4 and Google Gemini have made impressive strides, developing similar LLMs remains a daunting task accessible only to a handful of organizations with the necessary resources and expertise.

    In simpler terms, it’s like building a skyscraper of knowledge and intelligence. You need the right materials (data), the right tools (hardware and software), the right architects (experts), and a lot of hard work and attention to detail to make it stand tall and function flawlessly.

    Developing similar models would be a daunting task for individual developers or smaller teams due to the enormous scale of resources and expertise needed. However, as technology progresses and research findings become more accessible, it might become incrementally more feasible for a broader range of organizations or researchers to work on similar models, albeit at a smaller scale or with fewer resources. At that time we might also start to focus on developing LLMs ourselves.

    To sum up, the focus of the program is not on LLM model development but on applied Cloud GenAI Engineering (GenEng), application development, and fine-tuning of foundational models. The program covers a wide range of topics including Python, GenAI, Microserices, API, Database, Cloud Development, and DevOps, which will give students a comprehensive understanding of generative AI and prepare them for careers in this field.

  2. Business wise does it make more sense to develop LLMs ourselves from scratch or use LLMs developed by others and build applications using these tools by using APIs and/or fine-tuning them?

    Whether it makes more business sense to develop LLMs from scratch or leverage existing ones through APIs and fine-tuning depends on several factors specific to your situation. Here’s a breakdown of the pros and cons to help you decide:

    Developing LLMs from scratch:

    Pros:

    Customization: You can tailor the LLM to your specific needs and data, potentially achieving higher performance on relevant tasks.

    Intellectual property: Owning the LLM allows you to claim intellectual property rights and potentially monetize it through licensing or other means.

    Control: You have full control over the training data, algorithms, and biases, ensuring alignment with your ethical and business values.

    Cons:

    High cost: Building and training LLMs require significant technical expertise, computational resources, and data, translating to high financial investment.

    Time commitment: Developing an LLM is a time-consuming process, potentially delaying your go-to-market with your application.

    Technical expertise: You need a team of highly skilled AI specialists to design, train, and maintain the LLM.

    Using existing LLMs:

    Pros:

    Lower cost: Leveraging existing LLMs through APIs or fine-tuning is significantly cheaper than building them from scratch.

    Faster time to market: You can quickly integrate existing LLMs into your applications, accelerating your launch timeline.

    Reduced technical burden: You don’t need a large team of AI specialists to maintain the LLM itself

    Cons:

    Less customization: Existing LLMs are not specifically designed for your needs, potentially leading to lower performance on some tasks.

    Limited control: You rely on the data and biases of the existing LLM, which might not align with your specific requirements.

    Dependency on external parties: You are dependent on the availability and maintenance of the LLM by its developers.

    Here are some additional factors to consider:

    The complexity of your application: Simpler applications might benefit more from existing LLMs, while highly complex ones might require the customization of a dedicated LLM.

    Your available resources: If you have the financial and technical resources, developing your own LLM might be feasible. Otherwise, existing options might be more practical.

    Your competitive landscape: If your competitors are using LLMs, you might need to follow suit to remain competitive.

    Ultimately, the best decision depends on your specific needs, resources, and business goals. Carefully evaluating the pros and cons of each approach will help you choose the strategy that best aligns with your success.

  3. What are Custom GPTs?

    “Custom GPTs” refers to specialised versions of the Generative Pre-trained

    Transformer (GPT) models that are tailored for specific tasks, industries, or data types. These custom models are adapted from the base GPT architecture, which is a type of language model developed by OpenAI. Custom GPT models are trained or fine-tuned on specific datasets or for particular applications, allowing them to perform better in those contexts compared to the general-purpose models.

    Here are some examples of what custom GPT models might be used for:

    1. Industry-Specific Needs: A custom GPT for legal, medical, or financial industries could be trained on domain-specific texts to understand and generate industry-specific language more accurately.

    2. Language and Localization: Models can be customised for different languages or dialects that might not be well-represented in the training data of the base model.

    3. Company-Specific Applications: Organisations might develop a custom GPT model trained on their own documents and communications to assist with internal tasks like drafting emails, generating reports, or providing customer support.

    4. Educational Purposes: Educational institutions might develop custom GPTs trained on educational material to assist in creating teaching materials or providing tutoring in specific subjects.

    5. Creative Writing and Entertainment: Custom models could be trained on specific genres of literature or scripts to assist in creative writing or content creation.

    6. Technical and Scientific Research: A custom GPT model could be trained on scientific literature to assist researchers in summarising papers, generating hypotheses, or even drafting new research.

    These custom models are created through a process of fine-tuning, where the base GPT model is further trained (or ‘fine-tuned’) on a specific dataset. This process allows the model to become more adept at understanding and generating text that is relevant to the specific use case. Fine-tuning requires expertise in machine learning and natural language processing, as well as access to relevant training data.

  4. What are Actions in GPTs?

    Actions are a way to connect custom GPTs to external APIs, allowing them to access data or interact with the real-world. For example, you can use actions to create a GPT that can book flights, send emails, or order pizza. Actions are defined using the OpenAPI specification, which is a standard for describing APIs. You can import an existing OpenAPI specification or create a new one using the GPT editor.

  5. What are the different specialisations offered at the end of the program and what are their benefits?

    At the end of the GenEng certification program we offer six specialisations in different fields:

    Healthcare and Medical GenAI: This specialization will teach students how to use generative AI to improve healthcare and medical research. This is relevant to fields such as drug discovery, personalized medicine, and surgery planning.

    Benefits:

GenAI for Automation and Internet of Things (IoT):

GenAI for Cyber Security: