How can you gather and analyze data for AI?

Gathering and analyzing data for AI involves a structured and meticulous approach. I start by clearly defining the problem and identifying relevant data sources. Ensuring the quality and integrity of the data collected is paramount, involving careful preprocessing and cleaning. I use statistical techniques and exploratory data analysis to understand patterns and anomalies. The choice of analysis method depends on the specific AI application, whether it’s supervised learning, unsupervised learning, or deep learning. This rigorous process is crucial for developing robust AI models.

In order to ensure our success, it is essential not only to clearly define our goals but also to establish the criteria by which we will measure that success. Success criteria encompass both technical and business aspects. A predictive model with high accuracy may still hold little value when forecasting stock market prices or the cost of deploying a deep learning model may overweigh its benefit,

Training data quality is by far one of the most significant factors impacting model quality. This was not just hypothesized by experts of the field, but have been shown emprically over and over again. Especially, large language model training experience showed us that the data quality is the king, particularly as we reached the few trillions of tokens of collective internet data limit. These recent experiences have also showed that readily avaliable competent LLMs can be prompted correctly to generate quasi infinite amount high quality artificial datasets. So, mastering this will become an important subfield itself.

Gathering and analyzing data for AI involves several steps to ensure the quality and relevance of the data used to train and improve AI models.

Involve your data protection officer as early as possible in this activity, to avoid issues later on in your project. Also be aware, that when it comes to the processing of personal data, you need the consent to use that data for your AI project.

The optimal solution is utilizing mass volumes of relevant information that makes robust and dynamic differential data that is validated through multiple processes. Over time, I have found combining web crawling with reinforcement learning allows you to build a model that learns from vast amounts of real-world data, adapting and optimizing its behavior over time in the worlds largest dictionary. Utilizing a matching based process with omni directional goals for RL Q-Agents is a feat to most, though the reward outweighs time, over time with no theta.

Following from my post above: 5. Perform feature engineering by selecting the most predictive variables, creating new derived features, transforming variables etc. This extracts maximum signal from the data. 6. Analyse feature importance to select impactful variables and remove redundant, irrelevant or noisy features. Methods like correlation analysis, recursive feature elimination etc. can be used. 7. Check for class imbalance and use techniques like oversampling or undersampling to handle it. Otherwise, models may be biased.

Understanding the importance of efficient data storage and movement is crucial. It ensures that data is ready for any downstream application. Looking at the big picture: 1. Implement rigorous data validation and monitoring. 2. Have alert mechanisms in place for any discrepancies. In the long run, such diligence prevents countless headaches. Many tech startups today face challenges with their Data Warehousing. They understand their data goals but often lack the necessary infrastructure. Proper planning could save them significant time and money—months or even years of effort.

Coordinating effectively with data engineers or having sufficient know how on data storage and transfer is paramount of importance. So, make sure to read resources on introductory data engineering and keep up with recent trends of frameworks. The cloud providers have proven themselves to be very dynamic with AI and LLM evolution, making sure they provide the best suitable tools for the jobs. With reasonable prices do not afraid to consider cloud providers for your application.

The approach to data management varies depending on the project size. For personal projects, organizing data with proper folder structures and file names can be effective. For larger projects, I strongly recommend employing a Database Management System (DBMS) to handle the data required for analysis and modeling, instead of relying on data warehousing ETL services.

Leveraging Spark's Dataframe for large datasets enhances data preparation for your AI project Missing Values: Employ na.drop() to remove incomplete rows or na.fill() to replace missing values Removing Duplicates: Rely on dropDuplicates() to eliminate duplicate entries based on chosen columns Outlier Detection: Use filters for outlier identification and handling as per your analysis Data Transformation: Convert categories to numerics with StringIndexer or OneHotEncoder Scaling/Normalization: Use StandardScaler or MinMaxScaler to align numerical features Text Handling: Apply Scala's string methods and Spark's split() to preprocess text data Testing Pipeline: Validate preprocessing on small data subsets with confidence in scalability

To complete the process as abovementioned: 8. Train and experiment with different AI algorithms - neural networks, random forest, SVM etc. Evaluate and compare their performance. 9. Optimise model hyperparameters based on validation set. Choose the best model for deployment. The key aspects are understanding the data, engineering predictive features, statistical analysis to remove noise, handling class imbalance, and leveraging different ML algorithms to create an accurate AI model.

Many professionals utilize Python for data cleaning and preprocessing. However, if your data is stored in a DBMS, you gain access to an array of tools that enable faster and more efficient data cleaning and preprocessing. SQL, as the most effective language for data manipulation, combined with a DBMS, offers the optimal platform for managing your data.

Data in it self can tell a story but with large datasets it can be hard to read without visualizing the data. By keeping the vizuals simple and clear you make it easier for user to focus on what is important and see what story is being told. Dont include or add unnecessary data to the the vizuals. Use the data to tell the story and highlight findings or focus areas. Use labels or additional information to make it easier to understand. Think about what kind of visuals is best suited for your data and. Review and get feedback from others.

Before constructing any model, it is essential to conduct univariate analysis (including visualization) for all attributes (columns) and, at a minimum, perform bivariate analysis against the target variable. In many cases, this stage may lead you to discover your best model idea.

It's hard to characterise data exploration for AI training data in general, since much depends on the aims and nature of the AI system. In my own experience, I've found that looking for systematic gaps and biases in the data is perhaps the most important part.

Visualization is not about fancy looking graphs, and pretty colors, A simple bar chart with an explanatory narrative can be just as useful for decision makers, than a full fledged dashboard. You have to keep the end user in mind, while designing any visual, whether such a visual clearly highlights a piece of information that might help them in decision making.

Don't miss out on these points, when doing data visualization: [1] Clarity and effectiveness: Assess how well your charts and graphs communicate your insights. Use feedback from others. [2] Variety and storytelling: Track your use of different chart types and how effectively they support your narrative. [3] Accessibility: Ensure your visualizations are accessible for everyone, including those with color blindness or other visual impairments. Evaluate how well you present data in a way that's easy for others to understand. It's about creating charts, graphs, or other visuals that tell a clear story. Track things like whether people find your visuals helpful and if they grasp the main points without confusion.

In general training an offline model (like what we did for homework) is completely different from training an online model that serves product users. Models that perform well offline may not work as expected when we move it to production, so there are many tweaks of model weights/params you will have to do to make it really useful.

In my experience, the evaluation step is the most important (and the hardest) step. Training ML/AI models is getting easier and easier but evaluation on specific business applications in specific domains remains as challenging as it is important.

𝐒𝐞𝐥𝐞𝐜𝐭 𝐚𝐧𝐝 𝐚𝐩𝐩𝐥𝐲 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐦𝐨𝐝𝐞𝐥: There are many different types of AI models, each with its own strengths and weaknesses. The best AI model for your project will depend on your specific goal and data needs. Some common types of AI models include: • 𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥𝐬: These models learn from labelled data to make predictions about new data. • 𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥𝐬: These models learn from unlabelled data to identify patterns and relationships. • 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥𝐬: These models learn through trial and error to make decisions that maximize a reward signal.

A post-training model report on the holdout dataset can entail: 1. Model Performance: For a classification case, evaluate using metrics like accuracy, precision, recall, F1 score, along with ROC and P-R curves. Analyze residuals and the probability distribution plot for deeper insights. Explainable AI (XAI): 2. Global Understanding: Delve into Feature Importance, Partial Dependence Plots (PDP), and Model Surrogates to grasp the model's overall behavior. 3. Local Understanding: Investigate specific decisions with tools like LIME, SHAP, and Counterfactual Explanations. Some useful XAI Libraries: Consider SHAP, LIME, Dalex, InterpretML, eli5, and Alibi for model interpretation.

𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭 𝐚𝐧𝐝 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐞 𝐲𝐨𝐮𝐫 𝐫𝐞𝐬𝐮𝐥𝐭𝐬: Once we have trained and evaluated our AI model, we need to interpret the results and communicate them to others. This involves understanding what our model is learning and how it can be used to make predictions or decisions. We should also communicate the limitations of our model and the potential risks of using it.

I would emphasize on following additional points with respect to a successful AI application: 1. Pushing data in cloud costs money. Its like a rental space. So, only keep data in cloud that yo absolutely have to. Run some data prep steps to examine the strongest decision variables and shave off the least correlated variables. 2. Plug and play type AI tools cost money. If you learn some coding or have someone in your team that can do the coding, you will save money. 3. You need to be very intuitive to understand how best you can leverage the power of AI for your problem. Read newsletters and blogs on AI to get good ideas. There are plenty out there.

Think about risk early in the process and develop a risk mitigation strategy for your data sets especially those that contain personal and financial information. Is there data that you DON'T need that are in your data sets? Stripping it out will not only limit your exposure but cut down on your data storage and transfer costs. For sensitive data be sure you are following industry best practices in terms of encryption and multi-factor authentication.

Keep an eye on AI laws and regulations The EU's proposed AI law will regulate the use of artificial intelligence and will be the first comprehensive law of its kind in the world. The law proposes to assess and categorize AI systems based on risk, leading to different levels of regulation. The law identifies unacceptable risks such as manipulation of cognitive behavior and social rating systems, while categorizing high-risk AI into product sectors and specific domains. Generative AI models must be transparent, and minimal transparency requirements apply to AI with limited risk. Negotiations between the European Parliament and member states aim to reach agreement on the law by the end of 2023.

Keep in mind there there is a step which is very important in any AI project (my opinion - more than any other step), which is model deployment. It is the most difficult segment in AI product development and you need to think about it from the start. It involves a combination of Data Science, AI engineering, Software engineering / Web development specifically. It actually a whole model deployment pipeline and can take up to more than 50% of the resources for an AI project implementation and product development.

Sometimes you can't get the data you need. In those cases it can be useful to either start with synthetic data, or else to build a rule based system, deploy that as a first iteration, and use it to collect the data you need for training.