1) Web3, Blockchain, and GenAI Integration Specialization
2) Metaverse, 3D, and GenAI Integration Specialization
3) Healthcare and Medical GenAI 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) Specialization
8) GenAI for Cyber Security
Cloud Applied Generative AI Engineering (GenEng) is the application of generative AI technologies to solve real-world problems in the cloud.
By combining generative AI with cloud computing, businesses can solve a variety of problems, such as:
The potential applications of cloud-applied generative AI are endless. As generative AI and cloud computing continue to develop, we can expect to see even more innovative and groundbreaking uses for this technology.
Developers with expertise in Cloud 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 generalized AI or cloud computing roles.
The demand for AI developers, especially those proficient in applying generative AI techniques within cloud 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 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 specializing 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 specialized 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.
Cloud Applied Generative AI Developers have a significant potential to start their own companies due to several factors:
However, starting a company, especially in a specialized 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.
You are learning two programming languages in the first quarter of the GenEng certification program because they are both essential for developing smart applications with GenAI.
The length of the program is one year which is broken down into four quarters of three months each. The program covers a wide range of topics including TypeScript, Python, Front-end Development, GenAI, API, Database, Cloud Development, and DevOps. The program is designed to give students a comprehensive understanding of generative AI and prepare them for careers in this field. Nothing valuable can be achieved overnight, there are no shortcuts in life.
The Certified Generative AI (GenEng) Developer and Engineering Program teaches students to develop smart applications using both TypeScript and 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.
The difference between OpenAI Completion API, OpenAI Assistant API, Google Gemini Multi-Modal API, and LangChain is that they are different ways of using artificial intelligence to generate text, images, audio, and video based on some input, but they have different features and applications. Here is a summary of each one:
OpenAI Completion API is the most fundamental OpenAI model that provides a simple interface that’s extremely flexible and powerful. You give it a prompt and it returns a text completion, generated according to your instructions. You can think of it as a very advanced autocomplete where the language model processes your text prompt and tries to predict what’s most likely to come next. The Completion API can be used for various tasks such as writing stories, poems, essays, code, lyrics, etc. It also supports different models with different levels of power suitable for different tasks.
OpenAI Assistant API is an interface to OpenAI's most capable model (gpt-4) and their most cost-effective model (gpt-3.5-turbo). It provides a simple way to take text as input and use a model like gpt-4 or gpt-3.5-turbo to generate an output. The Assistant API allows you to build AI assistants within your applications. An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries. The Assistant API currently supports three types of tools: Code Interpreter, Retrieval, and Function calling.
Google Gemini Multi-Modal API is a new series of foundational models built and introduced by Google. It is built with a focus on multimodality from the ground up. This makes the Gemini models powerful against different combinations of information types including text, images, audio, and video. Currently, the API supports images and text. Gemini has proven by reaching state-of-the-art performance on the benchmarks and even beating the ChatGPT and the GPT4-Vision models in many of the tests. There are three different Gemini models based on their size, the Gemini Ultra, Gemini Pro, and Gemini Nano in decreasing order of their size.
LangChain is a platform that allows you to interact with various language models from different providers such as OpenAI, Google Gemini, Hugging Face Transformers, etc. You can use LangChain to create applications that leverage the power of natural language processing without having to deal with the complexity of APIs or SDKs. LangChain provides a user-friendly interface that lets you choose the model you want to use, customize the parameters you want to apply, and see the results in real-time.
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.
The Certified Generative AI (GenEng) Developer and Engineering Program teaches you how to use a variety of cloud services, including Google Cloud Run, Azure Container Apps, and Kubernetes, to deploy your applications to the cloud. You will also learn how to use Docker containersto package and deploy your applications, and how to use Terraform to manage your cloud infrastructure.
By the end of the program, you will be able to:
The Certified Generative AI (GenEng) Developer and Engineering Program teaches you how to use a variety of cloud services, including Google Cloud Run, Azure Container Apps, and Kubernetes, to deploy your applications to the cloud. You will also learn how to use Docker containersto package and deploy your applications, and how to use Terraform to manage your cloud infrastructure.
By the end of the program, you will be able to:
Web development technologies are essential for developing and deploying generative AI applications because they allow you to create user interfaces that allow users to interact with your applications. The Certified Generative AI (GenEng) Developer and Engineering Program teaches you how to use cutting-edge web development technologies, including TypeScript, React, Next.js, and Tailwind CSS, to build and deploy state-of-the-art web user interfaces. You will also learn how to use Vercel AI SDK, an open-source library for building AI-powered user interfaces.
APIs (Application Programming Interfaces) are used to connect different software applications and services together. They are the building blocks of the internet and are essential for the exchange of data between different systems.
In the third quarter of the Certified Generative AI (GenEng) Developer and Engineering Program, students will learn to develop APIs not just as a backend for their front end but also as a product itself. In this model, the API is at the core of the business's value.
In the third quarter of the Certified Generative AI (GenEng) Developer and Engineering Program, students will learn how to use Python-based FastAPI as a core library for API development.
Students will also learn about the following related technologies:
By the end of the quarter, students will be able to use Python-based FastAPI to develop APIs that are fast, scalable, and secure.
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:
Docker Containers are a fundamental building block for development, testing, and deployment because they provide a consistent environment that can be used across different systems. This eliminates the need to worry about dependencies or compatibility issues, and it can help to improve the efficiency of the development process. Additionally, Docker Containers can be used to isolate applications, which can help to improve security and make it easier to manage deployments.
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 specialized AI hardware. Access to these resources and the ability to optimize training processes are crucial.
Model architecture: Designing the LLM's architecture involves complex decisions about parameters, layers, and attention mechanisms. Optimizing 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 TypeScript, Python, Front-end Development, GenAI, API, Database, Cloud Development, and DevOps, which will give students a comprehensive understanding of generative AI and prepare them for careers in this field.
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.
The fourth quarter of the GenEng certification program offers six specializations in different fields:
Web3, Blockchain, and GenAI Integration: This specialization will teach students how to integrate generative AI with Web3 and blockchain technologies. This is relevant to fields such as finance, healthcare, and supply chain management.
Benefits:
Metaverse, 3D, and GenAI Integration: This specialization will teach students how to create and use 3D models and other immersive content manually and with generative AI. This is relevant to fields such as gaming, marketing, and architecture.
Benefits:
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 Accounting, Finance, and Banking: This specialization will teach students how to use generative AI to improve accounting, finance, and banking processes. This is relevant to fields such as fraud detection, risk management, and investment analysis.
Benefits:
GenAI for Engineers: This specialization will teach students how to use generative AI to improve engineering design and problem-solving. This is relevant to fields such as manufacturing, construction, and product development.
Benefits:
GenAI for Sales and Marketing: This specialization will teach students how to use generative AI to improve sales and marketing campaigns. This is relevant to fields such as advertising, public relations, and customer service.
Benefits:
GenAI for Automation and Internet of Things (IoT):
GenAI for Cyber Security:
