What skills to I need to retrain myself on amid the rise of generative AI?

In my opinion, any ML Model success depends on the quality of Data. LLM models are not any exception. The quality of Data plays an important role in quality of response from LLM /Generative AI. In order to learn the data quality skills, one should .know the tools used for 1, Data cleaning, 2.Data preprocessing, 3.Data prevention, 4. Data duplicate detection 5.Creation of Synthetic data. 6. Masking senistive information. across all the cloud providers 2.

As it's well known, data is the foundation of the knowledge pyramid. The better the data, the better the model including GenAI. Why is that? Because data contains hidden patterns and quality is defined for the set that has the real and optimum patterns that will be used by the autoencoder architecture of GenAI. The encoder needs patterns, the decoder also needs hidden patterns, the generator works with hidden patterns, and the discriminator applies the hidden patterns to detect fraud. So, I suggest you focus on the data quality as GenAI is largely unsupervised learning that focuses on patterns mostly. I hope you will find this useful, Best, Houman

IMO, Data quality is critical in generative AI for several reasons, and maintaining it is essential to ensure the success and reliability of the trained models: 1. Model Performance 2. Avoiding Bias 3. Generalization 4. Robustness 5. Reducing Overfitting To maintain data quality in generative AI, consider the following practices: 1. Data Cleaning and Preprocessing 2. Data Augmentation 3. Bias Detection and Mitigation 4. Data Validation and Verification 5. Documentation 6. Collaboration with Domain Experts 7. Continuous Monitoring 8. Ethical Considerations By maintaining high data quality through these practices, you can enhance the effectiveness of generative AI models, leading to more accurate and trustworthy generative outputs.

Ensuring the quality of your data is one of the most crucial skills you need to retrain yourself in. In order to learn the patterns and characteristics of the domain they are attempting to generate, generative AI models depend on vast volumes of data. But not every piece of data is made equal. It's important to be able to recognize and stay away from data that is irrelevant, noisy, biased, or incomplete. To make your data more appropriate for generative AI models, you also need to be able to preprocess and enhance it.

To adapt amid the rise of generative artificial intelligence (GenAI), focus on: 1. AI Literacy: Understand AI capabilities and limitations. 2. Data Skills: Learn data preparation and analysis. 3. Critical Thinking: Sharpen ability to interpret AI outputs. 4. Ethics and Bias Awareness: Understand ethical implications and biases in AI. 5. Creativity: Cultivate innovative thinking to leverage AI in problem-solving. 6. Continuous Learning: Stay updated with AI advancements. Emphasize skills that AI can't replicate, like creativity and ethical judgment.

The ability to choose the ideal model for your generative AI work is another ability you will need to retrain yourself in. Generative AI models come in a variety of forms, including transformers, generative adversarial networks (GANs), and variational autoencoders (VAEs). Based on the complexity, diversity, and realism of the data you wish to generate, each model has pros and cons of its own. It is imperative that you possess the capacity to assess and contrast various models according to their effectiveness, efficiency, and scalability.

Model selection is a critical skill in generative AI, where the choice of model directly impacts the quality of the generated data. Quality of Generated Data: Analyze the complexity, diversity, and realism of the generated data. GANs, for example, are known for generating very realistic images but might lack diversity, leading to mode collapse, where the model generates very similar outputs. Training Data Requirements: Consider the amount and type of data required for training the models. VAEs may perform better when you have limited data as they are more stable to train, whereas GANs might require more data to produce high-quality results. Choose AI models aligned with goals by leveraging past insights and technical details.

Selecting the appropriate model for your generative AI task is a critical skill. Various generative AI models, including variational autoencoders (VAEs), generative adversarial networks (GANs), and transformers, have unique advantages and drawbacks. The choice depends on factors like data complexity, diversity, and realism. You must be skilled in comparing and evaluating these models based on their performance, efficiency, and scalability to make an informed selection.

I think that "Model Selection" is about choosing the appropriate algorithms and techniques that best align with the objectives of your AI project. It involves understanding the strengths and limitations of various models, whether they're decision trees, neural networks, or something else, and selecting the one that will most effectively process data and provide the insights or predictions. This choice is critical because it can significantly impact the success and efficiency of the AI app.

The type of data you have, the difficulty of the task, and the particular needs of your application should all be taken into account when choosing a generative AI model. Evaluate other aspects as well, like the interpretability of the model, training efficiency, and the computational resources required for implementation. Experimenting with various models and architectures can help you decide which one best fits your specific use case.

Effectively training your generative AI model is the third skill you need to retrain yourself in. Training generative AI models can be difficult since they frequently call for a large amount of computing power, time, and fine tuning. Your model's parameters, including regularization, batch size, and learning rate, must be optimized. Additionally, you must be able to track the development of your model through the use of metrics, graphs, and samples. Additionally, you must be aware of the dangers and problems that come with using generative AI models, including mode collapse, overfitting, and moral dilemmas.

When training a generative AI model like the one you're working on, there are several key aspects to focus on for effective and efficient training: Monitoring Progress with Metrics and Visualizations: Using metrics like loss functions, accuracy, precision, recall, or F1 score, depending on your model's objective, is essential. Visualizing these metrics over time helps in understanding the model's learning curve. Sample outputs from the model at different stages of training can also provide insights into its performance and how it's improving. Effective generative AI training balances technical skills, monitoring tools, and awareness of ethical issues, ensuring robust, reliable, and responsible AI development.

I think Model Training is essentially about teaching the AI system how to make decisions or predictions. It's a process where I would expose the chosen model to large amounts of data so that it can learn the underlying patterns and relationships. The goal is for the model to improve over time as it processes more data, enhancing its accuracy and reliability in whatever task it's being trained to perform.

In my experience. It's crucial to remember that depending on the size of the dataset and the complexity of the model, training times can change and substantial computational resources may be needed. To get the best outcomes, experimentation with various configurations and routine monitoring are crucial. Furthermore, be aware of ethical issues, such as possible biases in the training data and the effect generated content has on end users.

It is important to know that if you've trained your model and started to get the results, you need to be able to understand if these results represent a genuine improvement or it comes from mere luck. To ensure a reliable assessment of results, employing appropriate metrics coupled with statistical techniques such as k-fold cross-validation is imperative. This approach enhances your ability to gain a comprehensive understanding of the model's performance.

System Integration: Adapt the model to work seamlessly with various platforms. For web apps, use APIs for communication; for mobile apps, optimize with tools like TensorFlow Lite; for cloud deployment, leverage scalability and management tools offered by platforms like AWS or Google Cloud. Testing and Validation: Implement feedback mechanisms for continuous improvement, conduct thorough quality assurance to ensure model accuracy and reliability, and measure user satisfaction through surveys and usage analytics. Performance Monitoring and Maintenance: Use monitoring tools to track real-time performance, regularly update the model for improved efficiency, and ensure scalability to handle increased load.

The fourth ability you must relearn is the use of your generative AI model in practical situations. In addition to being helpful for research and experimentation, generative AI models can also be used to solve real-world issues and add value. It is imperative that your model can be integrated with various platforms, online apps, mobile apps, and cloud services. Additionally, you must be able to employ user satisfaction, quality assurance, and feedback to test and validate the outputs of your model.

Having a good environment set-up is key to a swift deployment. Developing your models in Docker for instance encapsulates the entire enviroment, ensuring consistency and reducing deployment errors. It also provides a better portability and scalability, so all cloud services have resources such as 'Container App' in Azure which lets you scale your resources on the go according to your model requirements. Getting to know what cloud resource fits best your model is definitely something you want to retrain yourself on.

I think Model Deployment involves putting the trained AI model into a live environment where it starts to perform and interact with real-world data. It's like taking a student who has learned a lot in a controlled environment to have study and give exams ,and then giving them a job in the real world. The model's education continues as it gains experience from actual use, and this step is important because no matter how well the model has learned from its training data, it's only valuable when it's working in the field and providing actionable insights or decisions.

One time at work. Recall that using generative AI models may raise ethical questions, particularly if user-facing content is produced. Be wary of any biases in the training data and the effects that user-generated content may have. Update the deployed model often to take advantage of new features and fix any problems that may occur when it is being used in production.

Model Improvement is the ongoing process of refining the AI model. It's like a cycle of continuous learning where the model is regularly updated with new data which can lead to better performance. This step is important because the world changes and new data can show that the model needs to adjust. More like a gardener who nurtures the plant as it grows

One thing I have found useful. A crucial component of developing generative AI models is iterative improvement. To gradually improve the model's capabilities, assess its performance on a regular basis, take user feedback into account, and investigate novel approaches.

Model innovation is characterized by three main pillars: - economics and energy, - ubiquity and access, - and trust and security. There's a shift towards smaller, open-source models that are more accessible and energy-efficient. Trust and security remain paramount as these models gain wider adoption. Organizations are increasingly using generative AI to derive business value, with a significant spike in adoption rates compared to previous years.

Asking Gen AI to teach you how to use Gen AI! By doing so, you can build interesting apps in different domains using the APIs.

The best way keep up to speed with generative AI, just as with any other new technology, is to keep building. Build and test models for yourself. Build your own mini genAI. Or use what's out there (e.g. GPT-4, Midjourney, Claude), and the tools that they provide (OpenAI API, custom GPTs) to innovate ideas and learn by doing. Many new and groundbreaking services that are currently on the rise are simply old ideas enabled by new technology. Get familiar with the new technology, learn by doing, and doors will open!

Many people talk about prompt engineering nowadays with the rise of LLMs. However, even though LLMs do indeed need a lot of prompt engineering to deliver top-notch results, being good at prompts does not necessarily only link to generative AI. Prompt engineering is not just LLM-specific. It is one's ability to frame a problem in an easily understandable manner, it is asking a question in a few words rather than a few paragraphs, it's in general the ability to compress information without losing its essence. With the rise of generative AI, it's important to improve those skills as well. On one hand, they do increase the quality of generated content. On another, they might also apply to other fields of business, software, and life as well.

Try to build a community of experts to stay in touch with the news (which move overwhelmingly fast to most of us if we are being honest!). Discord, Slack, Twitter (sorry, X), and of course, LinkedIn. There are many forums with vibrant communities out there!

As in any other software-related development, never go short with your own comments on why you are selecting the models you did, why the parametres and every single line you are writting. It is surprising how fast we forget what we write and down the line somebody else will have to use it back. A great tool that forces you to comment every line is Github's copilot, speeding up your development while leaving back your thoughts.