How can you determine the best AI model for your task?

Deeply analyze the problem you aim to solve. Is it classification, regression, clustering, image recognition, or something else? This clarity is essential. However, you might disagree if your problem crosses several domains, making it hard to categorize. An example is a task that needs to identify objects in an image and predict their movement patterns.

To determine the best AI model for your task, start by clearly defining the problem you want to solve and the specific objectives you aim to achieve. Then, thoroughly explore and understand your data, considering factors like size, quality, and diversity. Based on your task and data characteristics, choose an appropriate AI model or algorithm that aligns with your objectives and data requirements. Next, train and fine-tune your selected model using relevant techniques such as hyperparameter tuning and cross-validation to optimize its performance. Finally, rigorously test your model's performance on unseen data and deploy it into production, ensuring ongoing monitoring and maintenance.

If you have your processes defined, including agents, interactions, entities, stakeholders etc. you can automate parts in principle. What parts are done by whom: AI, Algorhithms, Humans, Robotics, are chosen based on the scale of your operations, scaling plans and type of business. There are two ways to start something: Experimental trials, or structurally learning about your needs. The clearer you know what you need the more educated and quicker your decisions can be. Status quo analysis comes first. The explorative side is always great to get ideas and widen the horizon. What approach you select is majorly dependent on business size and available capital.

- Consider the specific problem that needs to be solved and the type of data available - Match the technology with the task at hand - Assess the type of machine learning algorithm appropriate for the task and data - Evaluate the scalability and production capabilities of the AI model - Discuss with engineers and experts before making a decision - Ensure access to ground truth data for supervised learning tasks - Conduct thorough evaluations to determine the most suitable model for the task.

Defining your task accurately is crucial - is it classification, forecasting, or something else? For structured data, traditional models like regression or decision trees may suffice. For unstructured inputs like text or images, deep learning models like CNNs, Transformers could be ideal. Evaluate model performance using accuracy, F1-score, mean squared error. Consider aspects like complexity, interpretability, and scalability requirements. Don't simply use the latest model. Objectively compare options based on your unique needs. Techniques like hyperparameter tuning can further optimize the chosen model's performance. Deploy the top performer, but closely monitor its real-world performance post-deployment.

Some challenges in data preparation for AI models include: -Data Relevance -Data Quality -Data Cleaning -Data Collection -Imbalanced Data -Lack of Data To overcome these challenges, organizations can leverage self-service data preparation tools that assist in merging data from various sources, conducting exploratory data analysis, preprocessing data, and addressing issues like duplicates and anomalies efficiently Additionally, implementing best practices in data collection, ensuring high-quality and relevant information is gathered, can significantly improve the effectiveness of AI model

While LLMs are powerful, they may not always be sufficient alone, especially for numerical or financial data. Marrying LLMs with traditional AI and data analytics techniques can be invaluable in these cases. For example, an LLM can extract insights from text data, while time-series analysis, regression or anomaly detection can better handle complex numerical relationships.

ata Analysis: Conduct an exploratory data analysis to understand the characteristics of your dataset, including its size, quality, diversity, and the types of features available. Data Suitability: Assess whether your data is suitable for the task at hand. The nature of your data might influence the type of AI model that will be most effective.

Before diving into model selection, it's essential to become intimately familiar with your data. Think of your data as a treasure map - you need to explore every nook and cranny to uncover its secrets and potential. By conducting a thorough exploratory data analysis, you can identify patterns, anomalies, and relationships that will guide your choice of AI model.

When choosing the best AI model for your task, factor in feature selection alongside considerations for underfitting and overfitting. Feature selection involves identifying the most relevant features from your dataset to improve model performance and generalization. By selecting informative features, you can reduce dimensionality, mitigate the risk of overfitting, and enhance the model's ability to capture essential patterns in the data. Combining feature selection techniques with model evaluation helps in building more effective and efficient AI systems tailored to your specific task.

To determine which models might work well for your project, find a model that supports the task you need to complete for your use case and that supports the language of the text you need to process. Also, consider model attributes, such as license, pretraining data, model size, and how the model was fine-tuned. After you have a short list of models that best fit your use case, you can test the models to see which ones consistently return the results you want.

Model Research: Investigate various AI models that are commonly used for your type of task. Look into recent research and case studies to understand the current state-of-the-art models. Model Complexity: Consider the complexity of different models in relation to your task and data. Sometimes simpler models may perform adequately and be more interpretable and faster to train.

There will always be a newer, better model. While it's important to carefully choose the best model for your current task by comparing performance, complexity, interpretability, and scalability, it's equally important to architect your system in a way that allows for easy model swapping and updating. This could involve designing modular pipelines where different models can be plugged in and out with minimal disruption, or using transfer learning techniques to quickly retrain models on new data. It may also require investing in robust monitoring and evaluation frameworks to continuously assess model performance and identify when an update is needed.

1. Look up the current model rankings on the HuggingFace Lymsys Leaderboards (these are human evaluated head to head comparisons) 2. Ask people what they think about using ___ model for your use case. 3. Post on LinkedIn asking for opinions on ___ model. 4. Ask ME for my opinion ?? As a general FYI, these are the models you will likely be evaluating: OpenAI’s GPT, Anthropic’s Claude, Google’s Gemma, Meta’s Llama, Mistral, Mixtral, Cohere

before investing time and money in a model, of those companies have detailed website and technical notes telling you right away what this model is good at. Some are good to code, even to code apps, some are better at doing content, conversation, and so. You'll need to take time to research and read all those websites. Then it could be useful to talk with people already working and writing articles and documentations about the model. Also surround yourself with people understanding this field, you may not be the best person to choose the model you want to work with.

Training and tuning is only necessary if you’re doing some hardcore machine learning. It’s overkill for many simple proof of concept use cases involving LLMs. There’s surprisingly a lot you can do with just prompting and MAYBE RAG.

Baseline Model: Start with a simple model to establish a baseline performance. This can help you understand the minimum expectation before moving on to more complex models. Hyperparameter Tuning: Experiment with different hyperparameters to optimize your model’s performance. Tools like grid search or random search can be helpful in this process.

Begin by dividing your data into training, validation, and test sets to evaluate model performance accurately. Use an appropriate loss function and optimization algorithm that align with your task. Use techniques like grid search or random search to find the best combination for optimal performance. Implement cross-validation to ensure your model's robustness and generalizability across different data subsets. Monitor overfitting by comparing training and validation performance; apply regularization techniques or adjust model complexity as needed. This iterative process of training and tuning not only enhances model performance but also provides insights into the model's suitability for your task.

Similar to what Albert said. This step should really be avoided for the vast majority of tasks. It's all about evaluating the results/outputs from each model in a consistent and measurable way. E.g. if you were deciding on the best AI model to reply to a customer support query, you'd want to setup a mini-experiment to see how the different models compare. 1. Find 3-5 example customer support questions 2. Write (or get) the best responses to these questions (this is what you will compare the AI response to) 3. Decide what you're inputting to the AI model (ChatGPT) e.g. question, extract from relevant help docs, tone of voice guidelines 4. Run the same request on a few different models and ask your team to pick their favourite responses

1. You can achieve incredible results just through prompt engineering. Craft your prompts carefully to steer pre-trained models towards your desired behaviour, no extra training needed. Pro tip: Use prompt templates and inject dynamic information such as dates, locations, weather, important recent company announcements etc. 2. Retrieval Augmented Generation (RAG) is hands-down, the best way for injecting business knowledge to your AI applications. Retrieve relevant info from your various data sources, feed it to the model, and watch the magic happen. 3. Fine-tuning? It is really a last resort. Use it sparingly and only if you need to dramatically alter an existing models behaviour.

To effectively launch your AI model, focus on strategic testing and deployment: ?? Test Thoroughly: Beyond training, evaluate your model with new data to gauge its real-world performance. Use methods like A/B testing for nuanced insights. ?? Match Test to Risk: Scale your testing strategy to the project's risk. For high-stakes models, consider a phased approach to mitigate potential issues. ?? Deploy Wisely: Choose a deployment strategy, from A/B testing to gradual rollouts, that allows for performance monitoring and easy adjustments based on feedback. ?? Embrace Real-World Dynamics: Real-world application might uncover unexpected challenges. Be ready to adapt, possibly reverting to previous solutions if needed for stability.

This step identifies any issues not apparent during training, such as overfitting or underfitting. For deployment, consider the model's integration with existing systems and its scalability, latency, and resource requirements. Choose a deployment environment that supports your model's computational needs and ensures data privacy and security compliance. Continuously monitor the model's performance post-deployment to catch any degradation or changing data patterns. Iterative testing and deployment, followed by monitoring and updates, ensure your model remains effective and indicates when adjustments or replacements are necessary to meet your task's goals.

Capture a mix of explicit user feedback and implicit behavioural data to truly gauge your AI model's performance. Ratings are a start, but dig deeper. Monitor task completion times, engagement, and retention to see your model's real impact. Collect and act on this data. Consider A/B testing different approaches, refine, and iterate. The best AI applications evolve with their users. Remember, your model is out in the wild, interacting with real people. Make user feedback - both kinds - your guiding light. That's how you build AI that makes a real difference.

Evaluate the performance of your trained model using a separate dataset reserved for testing. Assess metrics such as accuracy, precision, recall, and F1-score to measure the model's effectiveness. Once satisfied with the performance, deploy the model into production or integrate it into your application environment. Monitor its performance in real-world scenarios and iteratively improve as needed.

Evaluating performance of Large Language Models on complex tasks should never be left to automatic tests only! Customers of LLM-powered solutions should involve a team of human experts with verified expertise in the domain (as well as any appropriate target languages in use), and run multiple rounds of structured quality evaluations from different angles, using sophisticated protocols suitable for the task at hand, followed by rigorous analysis of evaluation results. AI product developers can also apply the same approach on their side (perform in house and/or independently commissioned human-expert evaluation of output quality) in order to produce clear & compelling evidence that helps AI system buyers make their choice.

To choose the best suitable AI model, first define your problem and evaluate your data. Consider model performance metrics and the balance between complexity and explainability. Experiment with various models, keeping practical constraints in mind. Refine your choice based on results, and stay updated on AI advancements. This approach helps identify the most suitable model for your task, considering accuracy, efficiency, and deployability.

If a brilliant model gets deployed into production and no one cares to use it, does it count? Every solution should be designed with a balance between transparency, accuracy, and model performance. Taking a design led approach with stakeholders from the get go to understand the importance and weights around these dimensions when designing models and AI ecosystems is critical. All things being equal a more transparent model is more likely to be adopted by an enterprise, particularly if the enterprises operational maturity to integrate AI into workflows/processes and experiences is low. More often than not your model selection process should educate stakeholders on the trade offs between models so they can help with the decision

Consider as well the materiality / criticality of the use case and balance the cost / return when choosing the modeling technique from options (e.g., AI even if effective may not be the most desirable technique if you cover a non critical use case with limited impact)

When choosing a model for a task you also have to know you budget. Always when you draft a solution design, put a realistic figure of the cost for each solution: - cost of AI: API or HW/cloud - cost and scarcity of AI top tier specialists - AI fine tunning, training and reoccurring tasks because the "immaturity" of the tech at this point