LLM API Integrations
We can integrate you applications and processes with state of the art LLMs.
While we have a proven track record with cloud platforms such as AWS, Microsoft Azure, and comparable services,
we specialize in OpenAI, Meta LLAMA and Google Cloud Architecture Framework (BigQuery, Vertex AI)
Programming Languages Python Java JavaScript C++ C# Ruby PHP Swift Go (Golang) R TypeScript SQL Perl MATLAB Libraries Scikit-learn (Python) TensorFlow (Python, C++) Keras (Python, runs on TensorFlow) PyTorch (Python) XGBoost (Python, R, C++, Java) LightGBM (Python, R, C++) CatBoost (Python, R, C++) Theano (Python) H2O.ai (Python, R, Java) MXNet (Python, C++, Scala) Spark MLlib (Apache Spark) fastai (Python, built on PyTorch) Caffe (C++, Python) Shogun (C++, Python, R, Java) NLTK (Python, for NLP tasks) Gensim (Python, for NLP and topic modeling) Statsmodels (Python, for statistical modeling) OpenCV (C++, Python, for computer vision) Turi Create (Python, for prototyping ML models) Deeplearning4j (Java) Here is a list of key machine learning terms along with their definitions: Algorithm: A set of rules or instructions given to a machine to help it learn patterns from data. Supervised Learning: A type of machine learning where the model is trained on labeled data (input-output pairs), learning to map inputs to the correct output. Unsupervised Learning: A type of machine learning where the model is trained on unlabeled data, and it tries to find hidden patterns or structures in the data. Reinforcement Learning: A type of learning where an agent learns to make decisions by interacting with an environment to maximize some reward. Classification: The task of predicting a categorical label from input data. Example: predicting whether an email is "spam" or "not spam." Regression: The task of predicting a continuous value from input data. Example: predicting housing prices based on various features. Clustering: A task where the algorithm groups a set of data points into clusters based on similarity, often used in unsupervised learning. Feature: An individual measurable property or characteristic of the data (also known as a variable). Label: The output variable in supervised learning that the algorithm is trained to predict. Model: A mathematical representation of a machine learning algorithm that learns from data to make predictions or decisions. Overfitting: A situation where a model learns the training data too well, capturing noise along with the signal, resulting in poor performance on new data. Underfitting: A situation where the model is too simple to capture the underlying patterns in the data, leading to poor performance both on the training and test data. Training Set: The subset of data used to train a machine learning model. Test Set: The subset of data used to evaluate the performance of a machine learning model after training. Validation Set: A set of data used to tune model hyperparameters and validate the model's performance during training. Confusion Matrix: A table used to evaluate the performance of a classification model, showing true positives, false positives, true negatives, and false negatives. Precision: The proportion of true positive predictions out of all positive predictions made by the model (used in classification). Recall: The proportion of true positive predictions out of all actual positives (used in classification). F1 Score: The harmonic mean of precision and recall, providing a balance between the two for classification tasks. ROC Curve (Receiver Operating Characteristic Curve): A graphical plot showing the performance of a classification model at different threshold values. AUC (Area Under the Curve): A measure of a model's ability to distinguish between classes, calculated from the ROC curve. Gradient Descent: An optimization algorithm used to minimize the cost function by iteratively adjusting model parameters. Cost Function: A function that measures the error between the predicted outputs and the actual outputs of a model, used to guide the learning process. Hyperparameters: Configurable parameters that are set before the learning process begins (e.g., learning rate, number of trees). Neural Network: A model inspired by the structure of the human brain, consisting of layers of nodes (neurons) that learn patterns from data. Activation Function: A function applied to the output of each neuron in a neural network to introduce non-linearity (e.g., ReLU, Sigmoid). Backpropagation: A process used in neural networks to update weights based on the error calculated from the output, using the gradient descent algorithm. Decision Tree: A model that makes predictions by learning simple decision rules from data, represented as a tree structure. Random Forest: An ensemble learning method that builds multiple decision trees and averages their predictions to improve accuracy and reduce overfitting. Bagging (Bootstrap Aggregating): An ensemble method where multiple models are trained on different subsets of the training data, and their predictions are averaged to improve performance. Boosting: An ensemble technique that trains models sequentially, where each subsequent model attempts to correct the mistakes of the previous ones. Feature Engineering: The process of transforming raw data into features that better represent the problem to improve model performance. Dimensionality Reduction: The process of reducing the number of input variables (features) in a dataset, often using techniques like PCA (Principal Component Analysis). PCA (Principal Component Analysis): A method for reducing the dimensionality of data by transforming it into a new set of variables (principal components) that capture the most variance. Cross-Validation: A technique for evaluating model performance by splitting the data into several folds and training/testing the model on different subsets. Regularization: A technique used to prevent overfitting by adding a penalty term to the cost function (e.g., L1, L2 regularization). Learning Rate: A hyperparameter that controls the size of the steps taken during gradient descent optimization. Epoch: One complete pass through the entire training dataset during the learning process. Batch Size: The number of training examples used in one iteration of the model’s learning process. Latent Variables: Variables that are not directly observed but inferred from the model (commonly used in unsupervised learning). Here’s a list of key data analytics terms along with their definitions: Data Analytics: The process of examining datasets to draw conclusions and extract useful information using statistical, computational, and analytical techniques. Data Mining: The process of discovering patterns, correlations, and trends in large datasets using machine learning, statistics, and database systems. Descriptive Analytics: A type of analytics focused on summarizing historical data to understand what has happened. It involves techniques such as data aggregation and visualization. Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Prescriptive Analytics: A type of analytics that provides recommendations for decision-making by using optimization and simulation models to evaluate multiple possible actions. Exploratory Data Analysis (EDA): The initial investigation of data to discover patterns, spot anomalies, and test hypotheses using visual and quantitative techniques. Data Wrangling (or Data Munging): The process of cleaning, structuring, and enriching raw data to make it ready for analysis. Data Cleaning: The process of detecting and correcting (or removing) errors and inconsistencies in the data to improve data quality. Data Normalization: A process in which data is organized to reduce redundancy and dependency, improving efficiency and consistency for analytics. Data Transformation: The process of converting data from one format or structure to another for the purpose of analysis. Data Aggregation: The process of gathering and summarizing data, often to display it in a more interpretable or useful form. Data Visualization: The representation of data in graphical form (e.g., charts, graphs, dashboards) to help users understand and interpret trends, outliers, and patterns. Correlation: A statistical measure that expresses the extent to which two variables are linearly related. Causality: The relationship between cause and effect, where one variable directly influences another. Outliers: Data points that are significantly different from other observations, which can indicate variability, error, or important insights. KPI (Key Performance Indicator): A measurable value that demonstrates how effectively an individual, team, or organization is achieving business objectives. Dashboard: A visual interface that displays the most important information needed to track and monitor key metrics and performance. Data Warehouse: A centralized repository of integrated data from multiple sources, structured for query and analysis. Data Mart: A subset of a data warehouse, typically focused on a specific business line or team’s needs. ETL (Extract, Transform, Load): A process that extracts data from different sources, transforms it into a proper format or structure, and loads it into a target system, such as a database or data warehouse. Business Intelligence (BI): The technology-driven process of analyzing data and presenting actionable information to help executives, managers, and other users make informed business decisions. Big Data: Extremely large datasets that cannot be easily processed or analyzed using traditional data processing methods. Big data often requires specialized tools like Hadoop or Spark. Structured Data: Data that is organized in a specific format, typically stored in relational databases (e.g., tables with rows and columns). Unstructured Data: Data that is not organized in a predefined format, such as text, images, videos, and social media posts. Semi-structured Data: Data that does not conform to a strict schema but has some organizational properties (e.g., JSON, XML). Data Lake: A large storage repository that holds a vast amount of raw data in its native format until it is needed for processing and analysis. Metadata: Data that describes other data, providing information about the content, context, or structure of data (e.g., file size, format, date created). A/B Testing: A method of comparing two versions of a variable (e.g., a web page, email, or app feature) to determine which performs better. Anomaly Detection: The identification of rare items, events, or observations that do not conform to the majority of the data, often used in fraud detection and quality control. Drill Down: The process of moving from summary information to more detailed views within a dataset to gain deeper insights. Time Series Analysis: The study of data points collected or recorded at specific time intervals to identify trends, seasonal patterns, or cyclical behavior. Forecasting: The process of predicting future values based on historical data, often using time series analysis and statistical models. Regression Analysis: A statistical technique used to model and analyze relationships between a dependent variable and one or more independent variables. Segmentation: The process of dividing a larger dataset into smaller groups or segments based on common characteristics or behaviors. Churn Rate: The percentage of customers who stop using a product or service over a given period, used to analyze retention and customer behavior. Data Governance: The set of processes and policies that ensure high-quality data management and security throughout its lifecycle. Data Integrity: The accuracy, consistency, and reliability of data over its lifecycle, ensuring it has not been altered in unauthorized ways. Data Literacy: The ability to read, understand, create, and communicate data as information. Sampling: The process of selecting a subset of data points from a larger dataset to analyze or draw conclusions about the entire dataset. Hypothesis Testing: A statistical method used to test assumptions (hypotheses) about a population parameter, based on sample data. P-value: A measure used in hypothesis testing to indicate the probability of observing results as extreme as those observed, assuming the null hypothesis is true. Confidence Interval: A range of values, derived from sample data, that is likely to contain the true population parameter with a certain level of confidence. Data Integrity: The accuracy and consistency of data over its lifecycle, ensuring the data remains uncorrupted and reliable. Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) used to reduce the number of input variables to make the dataset more manageable and interpretable. Data Monetization: The practice of generating measurable economic benefits from available data, whether through direct or indirect means.
Programming Languages Python Java JavaScript C++ C# Ruby PHP Swift Go (Golang) R TypeScript SQL Perl MATLAB Libraries Scikit-learn (Python) TensorFlow (Python, C++) Keras (Python, runs on TensorFlow) PyTorch (Python) XGBoost (Python, R, C++, Java) LightGBM (Python, R, C++) CatBoost (Python, R, C++) Theano (Python) H2O.ai (Python, R, Java) MXNet (Python, C++, Scala) Spark MLlib (Apache Spark) fastai (Python, built on PyTorch) Caffe (C++, Python) Shogun (C++, Python, R, Java) NLTK (Python, for NLP tasks) Gensim (Python, for NLP and topic modeling) Statsmodels (Python, for statistical modeling) OpenCV (C++, Python, for computer vision) Turi Create (Python, for prototyping ML models) Deeplearning4j (Java) Here is a list of key machine learning terms along with their definitions: Algorithm: A set of rules or instructions given to a machine to help it learn patterns from data. Supervised Learning: A type of machine learning where the model is trained on labeled data (input-output pairs), learning to map inputs to the correct output. Unsupervised Learning: A type of machine learning where the model is trained on unlabeled data, and it tries to find hidden patterns or structures in the data. Reinforcement Learning: A type of learning where an agent learns to make decisions by interacting with an environment to maximize some reward. Classification: The task of predicting a categorical label from input data. Example: predicting whether an email is "spam" or "not spam." Regression: The task of predicting a continuous value from input data. Example: predicting housing prices based on various features. Clustering: A task where the algorithm groups a set of data points into clusters based on similarity, often used in unsupervised learning. Feature: An individual measurable property or characteristic of the data (also known as a variable). Label: The output variable in supervised learning that the algorithm is trained to predict. Model: A mathematical representation of a machine learning algorithm that learns from data to make predictions or decisions. Overfitting: A situation where a model learns the training data too well, capturing noise along with the signal, resulting in poor performance on new data. Underfitting: A situation where the model is too simple to capture the underlying patterns in the data, leading to poor performance both on the training and test data. Training Set: The subset of data used to train a machine learning model. Test Set: The subset of data used to evaluate the performance of a machine learning model after training. Validation Set: A set of data used to tune model hyperparameters and validate the model's performance during training. Confusion Matrix: A table used to evaluate the performance of a classification model, showing true positives, false positives, true negatives, and false negatives. Precision: The proportion of true positive predictions out of all positive predictions made by the model (used in classification). Recall: The proportion of true positive predictions out of all actual positives (used in classification). F1 Score: The harmonic mean of precision and recall, providing a balance between the two for classification tasks. ROC Curve (Receiver Operating Characteristic Curve): A graphical plot showing the performance of a classification model at different threshold values. AUC (Area Under the Curve): A measure of a model's ability to distinguish between classes, calculated from the ROC curve. Gradient Descent: An optimization algorithm used to minimize the cost function by iteratively adjusting model parameters. Cost Function: A function that measures the error between the predicted outputs and the actual outputs of a model, used to guide the learning process. Hyperparameters: Configurable parameters that are set before the learning process begins (e.g., learning rate, number of trees). Neural Network: A model inspired by the structure of the human brain, consisting of layers of nodes (neurons) that learn patterns from data. Activation Function: A function applied to the output of each neuron in a neural network to introduce non-linearity (e.g., ReLU, Sigmoid). Backpropagation: A process used in neural networks to update weights based on the error calculated from the output, using the gradient descent algorithm. Decision Tree: A model that makes predictions by learning simple decision rules from data, represented as a tree structure. Random Forest: An ensemble learning method that builds multiple decision trees and averages their predictions to improve accuracy and reduce overfitting. Bagging (Bootstrap Aggregating): An ensemble method where multiple models are trained on different subsets of the training data, and their predictions are averaged to improve performance. Boosting: An ensemble technique that trains models sequentially, where each subsequent model attempts to correct the mistakes of the previous ones. Feature Engineering: The process of transforming raw data into features that better represent the problem to improve model performance. Dimensionality Reduction: The process of reducing the number of input variables (features) in a dataset, often using techniques like PCA (Principal Component Analysis). PCA (Principal Component Analysis): A method for reducing the dimensionality of data by transforming it into a new set of variables (principal components) that capture the most variance. Cross-Validation: A technique for evaluating model performance by splitting the data into several folds and training/testing the model on different subsets. Regularization: A technique used to prevent overfitting by adding a penalty term to the cost function (e.g., L1, L2 regularization). Learning Rate: A hyperparameter that controls the size of the steps taken during gradient descent optimization. Epoch: One complete pass through the entire training dataset during the learning process. Batch Size: The number of training examples used in one iteration of the model’s learning process. Latent Variables: Variables that are not directly observed but inferred from the model (commonly used in unsupervised learning). Here’s a list of key data analytics terms along with their definitions: Data Analytics: The process of examining datasets to draw conclusions and extract useful information using statistical, computational, and analytical techniques. Data Mining: The process of discovering patterns, correlations, and trends in large datasets using machine learning, statistics, and database systems. Descriptive Analytics: A type of analytics focused on summarizing historical data to understand what has happened. It involves techniques such as data aggregation and visualization. Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Prescriptive Analytics: A type of analytics that provides recommendations for decision-making by using optimization and simulation models to evaluate multiple possible actions. Exploratory Data Analysis (EDA): The initial investigation of data to discover patterns, spot anomalies, and test hypotheses using visual and quantitative techniques. Data Wrangling (or Data Munging): The process of cleaning, structuring, and enriching raw data to make it ready for analysis. Data Cleaning: The process of detecting and correcting (or removing) errors and inconsistencies in the data to improve data quality. Data Normalization: A process in which data is organized to reduce redundancy and dependency, improving efficiency and consistency for analytics. Data Transformation: The process of converting data from one format or structure to another for the purpose of analysis. Data Aggregation: The process of gathering and summarizing data, often to display it in a more interpretable or useful form. Data Visualization: The representation of data in graphical form (e.g., charts, graphs, dashboards) to help users understand and interpret trends, outliers, and patterns. Correlation: A statistical measure that expresses the extent to which two variables are linearly related. Causality: The relationship between cause and effect, where one variable directly influences another. Outliers: Data points that are significantly different from other observations, which can indicate variability, error, or important insights. KPI (Key Performance Indicator): A measurable value that demonstrates how effectively an individual, team, or organization is achieving business objectives. Dashboard: A visual interface that displays the most important information needed to track and monitor key metrics and performance. Data Warehouse: A centralized repository of integrated data from multiple sources, structured for query and analysis. Data Mart: A subset of a data warehouse, typically focused on a specific business line or team’s needs. ETL (Extract, Transform, Load): A process that extracts data from different sources, transforms it into a proper format or structure, and loads it into a target system, such as a database or data warehouse. Business Intelligence (BI): The technology-driven process of analyzing data and presenting actionable information to help executives, managers, and other users make informed business decisions. Big Data: Extremely large datasets that cannot be easily processed or analyzed using traditional data processing methods. Big data often requires specialized tools like Hadoop or Spark. Structured Data: Data that is organized in a specific format, typically stored in relational databases (e.g., tables with rows and columns). Unstructured Data: Data that is not organized in a predefined format, such as text, images, videos, and social media posts. Semi-structured Data: Data that does not conform to a strict schema but has some organizational properties (e.g., JSON, XML). Data Lake: A large storage repository that holds a vast amount of raw data in its native format until it is needed for processing and analysis. Metadata: Data that describes other data, providing information about the content, context, or structure of data (e.g., file size, format, date created). A/B Testing: A method of comparing two versions of a variable (e.g., a web page, email, or app feature) to determine which performs better. Anomaly Detection: The identification of rare items, events, or observations that do not conform to the majority of the data, often used in fraud detection and quality control. Drill Down: The process of moving from summary information to more detailed views within a dataset to gain deeper insights. Time Series Analysis: The study of data points collected or recorded at specific time intervals to identify trends, seasonal patterns, or cyclical behavior. Forecasting: The process of predicting future values based on historical data, often using time series analysis and statistical models. Regression Analysis: A statistical technique used to model and analyze relationships between a dependent variable and one or more independent variables. Segmentation: The process of dividing a larger dataset into smaller groups or segments based on common characteristics or behaviors. Churn Rate: The percentage of customers who stop using a product or service over a given period, used to analyze retention and customer behavior. Data Governance: The set of processes and policies that ensure high-quality data management and security throughout its lifecycle. Data Integrity: The accuracy, consistency, and reliability of data over its lifecycle, ensuring it has not been altered in unauthorized ways. Data Literacy: The ability to read, understand, create, and communicate data as information. Sampling: The process of selecting a subset of data points from a larger dataset to analyze or draw conclusions about the entire dataset. Hypothesis Testing: A statistical method used to test assumptions (hypotheses) about a population parameter, based on sample data. P-value: A measure used in hypothesis testing to indicate the probability of observing results as extreme as those observed, assuming the null hypothesis is true. Confidence Interval: A range of values, derived from sample data, that is likely to contain the true population parameter with a certain level of confidence. Data Integrity: The accuracy and consistency of data over its lifecycle, ensuring the data remains uncorrupted and reliable. Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) used to reduce the number of input variables to make the dataset more manageable and interpretable. Data Monetization: The practice of generating measurable economic benefits from available data, whether through direct or indirect means.
