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Smarter Forecasting: How ML is Redefining Demand Prediction

Smarter Forecasting: How ML is Redefining Demand Prediction

Written by

Jacob Dink, AI/ML Director

Published

January 23, 2025

AI & Machine Learning
Forecasting & Prediction

Customers today are faced with more choices than ever, prompting businesses to step up their game in a fiercely competitive global market. To thrive, companies must not only provide exceptional value but also anticipate customer demand effectively. Traditional forecasting methods, which often rely heavily on historical data, can hinder growth and scalability.

This is where machine learning-powered demand forecasting and inventory optimization come into play. These advanced techniques enable businesses to predict demand accurately, allocate resources efficiently, adapt to market fluctuations, and foster long-term customer loyalty.

In this post, we’ll explore how leveraging demand forecasting and inventory optimization can streamline operations and why these strategies are essential for any modern business.

How Can Forecasting Unlock Business Agility and Accuracy?

Businesses need to stay ahead of shifting demands and unpredictable market changes. Demand forecasting and optimization empower organizations to predict future needs, align resources, and respond with confidence to evolving conditions.

Leveraging advanced analytics and AI-driven insights can help businesses sift through vast volumes of data, and make the most of the following multifaceted benefits:

  • Enabled Smarter Decisions: Leverage accurate demand forecasts to inform decisions about inventory, staffing, and production, reducing waste and optimizing resources.
  • Improved Agility: Use predictive models and optimization techniques to quickly adapt to market shifts, seasonal trends, and unexpected disruptions.
  • Cost Savings: Align supply with demand and streamline your manufacturing processes to minimize overstock and stockouts, enhance operational efficiency, and reduce carrying costs.
  • Enhanced Customer Satisfaction: Analyze massive datasets to meet customer demand with precision, improve fulfillment rates, and enhance customer experiences, fostering loyalty and trust.
  • Data-Driven Strategies: Utilize historical data and AI-driven insights to forecast future trends with accuracy, and gain a competitive edge in your industry.
Related Work
Optimizing restaurant inventory using demand forecasting for Relish Works
Read Case Study

Which Machine Learning Approaches Can You Use for Demand Forecasting?

Time-Series Forecasting

Time-series forecasting analyzes sequential historical data to predict future values. Techniques such as Autoregressive Integrated Moving Average (ARIMA) and exponential smoothing are commonly used to identify patterns like trends and seasonality, enabling businesses to anticipate demand fluctuations over time.

By integrating machine learning with time-series analysis, businesses can make informed decisions on inventory management and pricing strategies, ultimately reducing costs associated with overstocking or stockouts.

Regression Analysis

Regression models predict a continuous dependent variable, such as sales volume, based on one or more independent variables, like price or marketing spend.

These models enable businesses to quantify relationships between variables, helping them understand how different factors influence demand and make informed decisions accordingly.

Regression analysis can be enhanced and made more robust and adaptable through the following machine learning techniques:

  • Supervised Learning Framework: Regression falls under supervised learning, where models are trained on historical data to predict future outcomes. This involves splitting the dataset into training and testing sets to evaluate model performance.
  • Feature Engineering: Feature engineering plays a crucial role in creating new input features based on existing data and improving model accuracy. For instance, creating interaction terms between independent variables can capture complex relationships.
  • Algorithm Selection and Optimization: Algorithms such as decision trees and support vector machines can be utilized alongside traditional regression techniques to enhance predictive power. Automated machine learning (AutoML) platforms can streamline this process by optimizing model selection and hyperparameter tuning.

Neural Networks

Neural networks, particularly deep learning models, can identify complex, non-linear patterns and relationships within data. They can model intricate interactions between variables, making them powerful tools for capturing the multifaceted nature of demand influences.

What’s more, neural networks accurately model complex relationships within the data, enabling them to significantly outperform traditional forecasting methods.

For companies with large datasets across various regions and products, neural networks—such as long short-term memory (LSTM) networks and recurrent neural networks (RNNs)—can improve forecast accuracy and streamline inventory management.

Reinforcement Learning

Reinforcement Learning lets you make sequential decisions that maximize a long-term reward. In demand forecasting, this approach helps you continuously learn from outcomes and optimize strategies thereby improving decision-making processes over time.

Additionally, Reinforcement Learning is an approach that can help you:

  • Adjust prices in real-time based on predicted demand fluctuations and competitor actions.
  • Predict future demand to optimize inventory levels and determine the best restocking strategies.
  • Forecast the impact of promotional activities on demand to identify the most effective campaigns.
  • Enhance supply chain operations and ensure that they meet customer demands efficiently while minimizing costs.

Bayesian Analysis

Bayesian models incorporate prior knowledge and update predictions as new data becomes available to estimate the likelihood of various outcomes. This dynamic and flexible forecasting approach operates on the principle of updating beliefs about uncertain parameters through Bayes’ theorem.

Unlike traditional forecasting methods, Bayesian models produce a distribution of possible outcomes and enable businesses to understand the range of potential future demands and the associated risks.

In industries with intermittent demand patterns, Bayesian methods can effectively combine historical knowledge with sparsely observed data to estimate future needs thereby improving inventory management.

Hierarchical Forecasting

Hierarchical forecasting is used in scenarios with nested time series that together add up to a coherent whole. For instance, predicting sales at a national level can be broken down into regions, stores, and individual products. This method ensures consistency across different aggregation levels and leverages data from various hierarchy levels to improve accuracy.

In energy management, hierarchical forecasting can predict consumption patterns across different sources (e.g., solar, wind) and geographical areas, facilitating better grid management and resource allocation.

Forecasting patient admissions or medical supply needs across hospitals and regions is another application where hierarchical forecasting provides consistency across levels.

Hierarchical forecasting example

Multivariate Forecasting

In multivariate forecasting, multiple related time series are modeled together to capture the relationships between them. For instance, forecasting the demand for related product lines simultaneously can provide insights that improve the accuracy of each forecast by considering the interplay between products.

The multivariate forecasting approach also incorporates factors such as promotional activities, competitor pricing, and economic indicators to enable retail and sales forecasting. Moreover, this method considers lead times, supplier reliability, and market trends to optimize inventory levels and production schedules.

As organizations seek data-driven insights for decision-making, implementing multivariate forecasting will be essential for optimizing operations and enhancing competitiveness in dynamic markets.

Hybrid Forecasting

Hybrid forecasting combines multiple forecasting methods to leverage the strengths of each. Integrating different models enables businesses to achieve more robust and accurate predictions, accommodate various data patterns, and mitigate the limitations inherent in single-method approaches.

Retailers can combine historical sales data with promotions, and seasonality, to predict sales.

In healthcare, hybrid forecasting integrates historical usage data with factors such as seasonal illness patterns and demographic changes to optimize inventory levels for medical supplies.

For instance, a hybrid model can use the Autoregressive Integrated Moving Average (ARIMA) method to identify trends while also using a neural network to understand complex, non-linear influences, such as the effects of marketing campaigns.

Hybrid forecasting example

Key Takeaways

Mastering customer demand is no easy feat—it requires precision, insight, and adaptability.

Demand forecasters harness a range of tools, from time series analysis to advanced machine learning models, unlocking unparalleled accuracy and transforming raw data into actionable insights. These innovations empower businesses to not only analyze the resources available but also anticipate customer expectations in the future with confidence.

By adopting demand forecasting and optimization strategies, organizations can thrive in the present and scale and innovate for the future leading to:

  • Improved forecast accuracy
  • Significant cost savings
  • Enhanced efficiency
  • Scalability and agility

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Making AI More Human: The Power of Agentic Systems

Making AI More Human: The Power of Agentic Systems

Written by

Jack Teitel, Sr. AI/ML Scientist

Published

December 13, 2024

AI & Machine Learning
AI Agents & Chatbots
Snowflake

As AI advances, large language models (LLMs) like GPT-4 have amazed us with their ability to generate human-like responses. But what happens when a task requires more than just straightforward answers? For complex, multi-step workflows, agentic systems represent a promising frontier, offering LLMs the ability to mimic human problem-solving processes more effectively. Let’s explore what agentic systems are, how they work, and why they matter.

What are Agentic Systems?

Agentic systems go beyond traditional one-shot prompting — where you input a single prompt and receive a single response — by introducing structured, multi-step workflows. These systems break down tasks into smaller components, use external tools, and even reflect on their outputs to iteratively improve performance. The goal? Higher-quality responses that can tackle complex tasks more effectively.

Why Traditional LLMs Fall Short

In a basic one-shot prompt scenario, an LLM generates a response token by token, from start to finish. This works well for simple tasks but struggles with:

  • Complex Reasoning: Tasks requiring planning or iterative refinement.
  • Knowledge Limitations: Access to outdated or incomplete information.
  • Error Handling: Lack of self-correction during generation.

For example, if you ask a standard LLM to write an essay or debug a piece of code, it might produce a flawed output without recognizing or correcting its mistakes.

One method of correcting these limitations is to use multi-shot prompting, where the user interacts with the LLM, sending multiple prompts. By having a conversation with the LLM, a user can point out mistakes and prompt the LLM to provide better and more refined output. However, this still requires the user to analyze the output, suggest corrections, and interact with the LLM more than just the original prompt, which can be rather time-consuming.

One-Shot Prompting

  • Single prompt → single response
  • Response generated one token at a time
  • Tokens generated in order, from start to finish
  • Fast response time

Multi-Shot Prompting

  • Conversation with LLM
  • Includes context/history of previous conversation
  • Responses can be refined based on human feedback
  • Takes longer to get a final response
  • Requires human interaction/intervention for each prompt

Categories of Agentic Systems

Agentic systems address these limitations by employing four key strategies:

1. Reflection

Reflection enables an LLM to critique its own output and iteratively improve it. For instance, after generating code, a reflection step allows the model to check for bugs and propose fixes automatically.

Example Workflow:

  • Prompt the LLM to write code for a task.
  • Ask it to review the code and identify any issues.
  • Have it correct the code based on the reflection, iterating until no errors remain.

2. Tool Use

Tool use allows LLMs to call external APIs or perform actions beyond simple token generation (the only action within scope of a traditional LLM). This is essential for tasks requiring access to real-time information via web search or needing to perform specialized functions, such as running unit tests or querying up-to-date pricing.

Example Workflow:

  • Prompt the LLM to write code.
  • Execute unit tests through a CI/CD API.
  • Refine the code based on the test results.

3. Planning

Planning helps LLMs tackle complex tasks by breaking them into smaller, manageable steps before execution. This mirrors how humans approach large problems, such as developing an outline before writing an essay.

Example Workflow:

  • Generate a plan for solving a coding task.
  • Execute each step of the plan sequentially.
  • Combine the results to complete the task.

4. Multi-Agent Systems

Multi-agent systems distribute tasks among specialized agents, each with a defined role (e.g., planner, coder, reviewer). These specialized agents are often different instances of an LLM with varying system prompts to guide their behavior. You can also utilize specialized agents that have been specifically trained to perform different tasks. This approach mirrors teamwork in human organizations and allows each agent to focus on its strengths.

Example Workflow:

  • A planner agent creates a step-by-step plan.
  • A coder agent writes the code.
  • A reviewer agent identifies issues.
  • The coder agent corrects code based on issues found
  • The system iterates steps 2-4 until the desired result is achieved.

Why Agentic Systems Matter

Agentic systems offer several advantages:

  • Better Performance: Combining reflection, planning, and tool use can significantly enhance results, even when using older or smaller models like LLama-8B.
  • Explainability: The explicit multi-step nature provides transparency into how the model arrived at its solution — crucial for fields like healthcare or finance.
  • Flexibility: Agentic systems allow you to mix and match elements like reflection, planning, and tool use to suit your needs. You can deploy highly specialized agents for targeted tasks or more general agents for broader use cases, adapting seamlessly to different scenarios.

Practical Applications of Agentic Systems

Coding Assistance​

In software development, agentic systems can write code, test it, and debug autonomously. For example:

  • A planning agent outlines the steps required to build a program.
  • A coding agent writes the code.
  • A tool-use agent runs the code in a test environment.
  • A reviewer agent identifies bugs and suggests improvements.

Business and Healthcare

In domains where decision-making requires transparency and reliability, agentic systems excel. By providing clear reasoning and detailed workflows, they can:

  • Assist in medical diagnoses by integrating data from multiple sources.
  • Support business forecasting with transparent, step-by-step analysis.
  • Provide assurance in output accuracy through outlining specific logical steps/reasoning used to reach conclusions.

Real Time Information Analysis

Many businesses, such as finance, stock trading/analysis, e-commerce and retail, social media and marketing, rely on real-time information as a vital component of their decision-making. For these applications, agentic systems are necessary to extend the knowledgebase of stock LLMs beyond their original training data

  • Analyze current stock market trends in near real time.
  • Track and analyze current social media trends.
  • Evaluate return rates, customer interaction, and competitor pricing in near real time.

Creative Collaboration

From generating marketing campaigns to designing product prototypes, multi-agent systems can simulate entire teams, each agent offering specialized input, such as technical accuracy, customer focus, or business strategy.

Implementing Agentic Systems

Building agentic workflows may sound complex, but tools like LangGraph simplify the process. LangGraph, developed by the creators of LangChain, allows you to define modular agent workflows visually, making it easier to manage interactions between agents. Any code or LLM can act as a node (or agent) in LangGraph.

For example, if working in Snowflake, LangGraph can be combined with Snowflake Cortex to create an agentic workflow leveraging native Snowflake LLMs, RAG systems, and SQL generation, allowing you to build complex agentic workflows in the same ecosystem as more traditional data analytics and management systems while ensuring strict data privacy and security.

For simpler use cases, platforms like LlamaIndex also support agentic capabilities, particularly when integrating data-focused workflows.

The Future of Agentic Systems

As research evolves, agentic systems are expected to remain relevant, even as base LLMs improve. The flexibility of agentic workflows ensures they can be tailored to specific domains, making them a valuable tool for automating complex, real-world tasks. In addition, as base LLMs improve, you can keep your same agentic workflows in place, but swap out the individual agents for the improved LLMs, allowing you to easily improve the overall system performance. In this way, agentic systems not only improve accuracy of traditional LLMs, but can easily scale/adapt to the current rapidly changing LLM ecosystem.

In the words of AI pioneer Andrew Ng, agentic systems represent “the next big thing” in AI. They offer a glimpse into a future where AI doesn’t just respond — it reasons, plans, and iterates like a true digital assistant.

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Snowflake Cortex: Bringing ML and AI Solutions to Your Data

Snowflake Cortex: Bringing ML and AI Solutions to Your Data

Written by

Ross Knutson, Manager

Published

May 28, 2024

AI & Machine Learning
Data & App Engineering
Snowflake

Snowflake functionality can be overwhelming. And when you factor in technology partners, marketplace apps, and APIs, the possibilities become seemingly endless. As an experienced Snowflake partner, we understand that customers need help sifting through the possibilities to identify the functionality that will bring them the most value.

Designed to help you digest all that’s possible, our Snowflake Panorama series shines a light on core areas that will ultimately give you a big picture understanding of how Snowflake can help you access and enrich valuable data across the enterprise for innovation and competitive advantage.

What is Snowflake Cortex?

The Snowflake data platform is steadily releasing more and more functionality under its Cortex service. But, what exactly is Cortex?

Cortex isn’t a specific AI feature, but rather an umbrella term for a wide variety of different AI-centric functionality within Snowflake’s data platform. The number of available services under Cortex is growing, and many of its core features are still under private preview and not generally available. 

This blog seeks to break down the full picture of what Cortex can do. It’s focused heavily on what is available today, but also speaks to what’s coming down the road. Without a doubt, we will get a lot more new details on Cortex at Snowflake Data Cloud Summit on June 3-6. By the way, if you’ll be there, let’s meet up to chat all things data and AI.

ML Functions

Before Cortex became Cortex, Snowflake quietly released so-called “ML Powered Functions” which are now rebranded as just Cortex ML Functions. These functions offer an out-of-the-box approach for training and utilizing common machine learning algorithms on your data in the Snowflake Data Cloud.

These ML functions primarily use gradient boosting machines (GBM) as their model training technique, and allow users to simply feed the appropriate parameters into the function to initiate training. After the model is trained, it can be called for inference independently or configured to store results directly into a SQL table.

As of May 2024, there are 4 available ML Functions:

Forecasting

Use this ML function to make predictions about time-series data like revenue, risk management, resource utilization, or demand forecasting.

Anomaly Detection

This function looks to automatically detect outlier data points in a time-series dataset for use-cases like fraud detection, network security monitoring, or quality control.

Contribution Explorer

The Contribution Explorer function aims to rank data points on their impact to a particular output and is best used for use-cases like marketing effectiveness, program effectiveness, or financial performance.

Classification

To train a model that identifies some categorical value, like a customer segmentation, medical diagnosis detection, or a sentiment analysis.

In general, users should remember that these Cortex ML Functions are truly out-of-the-box. In a production state, ML use-cases may require a more custom model architecture. The Snowpark API, and eventually Container Services, allows users to import model files directly to the Snowflake data cloud, when they outgrow the limitations of the Cortex ML functions.

Overall, Cortex’s ML Functions provide a fast way for users to explore and test commonly used machine learning algorithms on their own data, securely within Snowflake.

LLM Functions / Arctic

Earlier this year, Snowflake made their Cortex LLM Functions generally available to select regions. These functions allow users to leverage LLM’s directly within a Snowflake SQL query. In addition, Snowflake also released ‘Arctic’ their open-source language model that is geared towards SQL code generation.

Below, direct from Snowflake documentation, shows how simple it is to call a language model directly within a SELECT statement with Cortex:

				
					SELECT SNOWFLAKE.CORTEX.COMPLETE('snowflake-arctic', 'What are large language models?');

In the first parameter, we defined the language model we want to use (e.g. ‘snowflake-arctic’), and in the second parameter, we feed our prompt. This basic methodology opens up a ton of possibilities for layering in the power of AI to your data pipelines, reporting/analytics, and ad-hoc research projects. For example, a data engineer could add an LLM function to standardize an free-text field during ETL. An BI developer could automatically synthesize text data from different Snowflake tables into a holistic 2-sentence summary for a weekly report. An analyst could build a lightweight RAG chatbot on Snowflake Streamlit to interrogate a large collection of PDFs.

Arctic

Arctic is Snowflake’s recently released open source LLM. It’s built to perform well in so-called ‘enterprise tasks’ like SQL coding and following instructions. It’s likely that Snowflake wants to position Arctic as the de facto base model for custom LLM business use-cases, particularly those that require fine-tuning.

Even more likely, the Arctic family of models will continue to grow. Document AI, which will give users a UI to extract data from unstructured data files, like a scanned PDF, directly into a structured SQL table. This feature is built on top of the language model ‘Arctic-TILT’.

Other Cortex / Future State

Naturally, Snowflake has joined the world is offering the Snowflake copilot to assist developers while they work with Snowflake through it’s web UI. Universal Search promises to offer an ‘augmeneted analytics’ experience where users can run a query by describing the intended result in natural language. While these features are exciting on their own, they aren’t a major focus for this blog.

Snowflake Streamlit provides a easy way to quickly build simple data applications, integrated with the Snowflake platform. Container Services opens up the possibility of hybrid architectures that leverage Cortex within external business application architectures. The VECTOR data type puts vector embeddings in columns alongside your structured data warehouse data, allowing for techniques like RAG that don’t require a new vector database like Pinecone.

Snowflake Cortex is far from fully materializing as a product, but seeing the foundational building blocks today helps paint a picture of a future data platform that enables companies to quickly and safely build AI tools at scale.

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Ensuring AI Excellence: Data Privacy/Security and Model Validation

Ensuring AI Excellence: Data Privacy/Security and Model Validation

Written by

Arturo Chan Yu, Senior Consultant

Published

August 29, 2023

AI & Machine Learning

Artificial Intelligence (AI) has revolutionized the way businesses operate, empowering them with unprecedented capabilities and insights. However, the success of AI models relies on several critical factors, ranging from data privacy and security to validation and testing. In this blog post, we will delve into the essential aspects of building robust AI models. 

Data Privacy and Security

With the increasing reliance on data comes the paramount responsibility of safeguarding its privacy and security. Data privacy and security are two interconnected concepts, each playing a crucial role in protecting sensitive information: 

Data Privacy

Data privacy involves controlling and managing the access, use, and disclosure of personal or sensitive data. It ensures that individuals have the right to know how their data is being collected, processed, and shared and have the option to consent or opt-out. 

Data Security

Data security, on the other hand, focuses on safeguarding data from unauthorized access, breaches, and malicious attacks. It involves implementing technological and procedural measures to protect data confidentiality, integrity, and availability. 

Essential Measures to Protect Sensitive Data

To ensure robust data privacy and security, organizations must adopt a multi-faceted approach that includes the following measures: 

Anonymization Techniques

Anonymization involves removing or modifying personally identifiable information from datasets. Techniques like data masking, tokenization, and generalization ensure that even if the data is accessed, it cannot be traced back to specific individuals.

Encryption

Data encryption transforms sensitive data into an unreadable format using encryption keys. It adds an extra layer of protection, ensuring that even if data is intercepted, it remains unintelligible without the proper decryption key.

Access Controls

Implementing stringent access controls is essential to limit data access to authorized personnel only. Role-based access controls (RBAC) ensure that users can only access the data relevant to their roles and responsibilities. 

Regular Data Backups

Regularly backing up sensitive data is crucial in the event of a cyber-attack or data loss. Backups provide a means to restore data and minimize downtime. 

Employee Training

Employees play a vital role in data security. Regular training on data protection best practices and potential security threats helps in building a security-conscious organizational culture and reduces the risk of human errors. 

Compliance with Data Protection Regulations

Data protection regulations, such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and various other regional laws, impose legal obligations on organizations to protect the privacy and security of personal data. Non-compliance can lead to significant fines and reputational damage. Organizations must proactively adhere to these regulations, which often include requirements for data transparency, consent management, data breach notifications, and data subject rights. 

Validation and Testing

Before deploying AI models into production environments, it is essential to rigorously validate and test their performance. This iterative process not only ensures the models are optimized for accuracy but also addresses potential issues, guaranteeing their effectiveness in delivering valuable insights. Validation and testing serve as a litmus test for AI models, determining whether they can deliver the expected results and perform well under diverse conditions. The main goals of validation and testing are to: 

Assess Model Performance

By validating and testing AI models, data scientists can determine how well the models perform on unseen data. This evaluation is crucial to avoid overfitting (model memorization of the training data) and ensure that the models generalize effectively to new, real-world scenarios. 

Fine-tune the Models

Validation and testing provide valuable feedback that helps data scientists fine-tune the models. By identifying areas of improvement, data scientists can make necessary adjustments and optimize the models for better performance.

Ensure Reliability

Validation and testing help build confidence in the models’ reliability, as they provide evidence of their accuracy and precision. This is especially crucial in critical decision-making processes. 

To measure the performance of AI models during validation and testing, various metrics are employed:

Accuracy

Accuracy measures the proportion of correct predictions made by the model. It provides a broad overview of model performance but may not be suitable for imbalanced datasets.

Precision and Recall

Precision represents the proportion of true positive predictions out of all positive predictions, while recall calculates the proportion of true positive predictions out of all actual positive instances. These metrics are useful for tasks where false positives or false negatives have significant consequences. 

F1 Score

The F1 score is the harmonic mean of precision and recall, providing a balance between the two metrics. It is particularly valuable when dealing with imbalanced datasets.

Area Under the Receiver Operating Characteristic Curve (AUC-ROC)

AUC-ROC measures the model’s ability to distinguish between positive and negative instances, making it an excellent metric for binary classification tasks.

The Roadmap to AI-Ready Data

As AI continues to reshape industries and drive innovation, building robust AI models has become a crucial imperative for organizations. Safeguarding sensitive data and iterating AI models are vital steps in this journey. By prioritizing data privacy and security, validating and testing models effectively, and embracing ongoing data readiness, organizations can harness the full potential of AI.

To help you navigate the complexities of preparing your data for AI, OneSix has authored a comprehensive roadmap to AI-ready data. Our goal is to empower organizations with the knowledge and strategies needed to modernize their data platforms and tools, ensuring that their data is optimized for AI applications. 

Read our step-by-step guide for a deep understanding of the initiatives required to develop a modern data strategy that drives business results.

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Enhancing AI Precision: Data Cleaning, Feature Engineering, and Labeling

Enhancing AI Precision: Data Cleaning, Feature Engineering, and Labeling

Written by

Faisal Mirza, VP of Strategy

Published

August 16, 2023

AI & Machine Learning

Artificial intelligence (AI) has emerged as a transformative force, revolutionizing industries and driving innovation. Behind the scenes of these powerful AI systems lies a series of essential processes that ensure their accuracy, reliability, and effectiveness. In this blog post, we will explore the critical steps of data cleaning and preprocessing, the art of feature engineering, and the pivotal role of data labeling and annotation. Together, these practices form the foundation of accurate AI models, empowering organizations to make informed decisions, uncover meaningful insights, and gain a competitive edge in a rapidly evolving world.

Data Cleaning and Preprocessing: The Foundation of Accurate AI Models

In the pursuit of accurate AI models, data cleaning and preprocessing serve as fundamental building blocks. In this section, we will delve into the significance of data cleaning and preprocessing, the common challenges they address, and the techniques employed to achieve reliable data for AI training.

In the realm of AI, the quality of input data directly influences the accuracy and reliability of AI models. Data in its raw form may contain inconsistencies and imperfections that can lead to erroneous predictions and compromised decision-making. Data cleaning and preprocessing aim to transform raw data into a standardized and usable format, providing AI models with a solid and reliable foundation.

Essential Data Cleaning Techniques

Handling Missing Values

Missing data is a common challenge in datasets, and effectively addressing it is crucial for preserving data integrity. Techniques like mean/median imputation, forward/backward filling, or using predictive models can be employed to replace missing values.

Removing Duplicates

Duplicate entries can distort the analysis and lead to inflated results. Identifying and removing duplicates is a fundamental data cleaning step to ensure unbiased AI models.

Addressing Outliers

Outliers, or data points deviating significantly from the rest, can mislead AI models. Techniques like Z-score or IQR can help identify and handle outliers effectively.

Standardizing Data Formats

Data collected from different sources may be in varying formats. Standardizing the data ensures consistency and simplifies AI model development.

Transforming and Normalizing

By recognizing skewed data distributions and employing suitable transformation approaches or normalization methods, you can ensure uniform representation of data. This enhances the precision and efficiency of analysis and machine learning models.

Handling Inconsistent and Invalid Data

It’s important to identify the dataset entries that deviate from predefined standards. Set explicit criteria or validation measures to rectify these inconsistencies or remove erroneous data points.

Data cleaning and preprocessing offer numerous benefits that significantly impact AI model performance and efficiency. By improving accuracy, saving time and cost, enhancing decision-making, and increasing overall efficiency, these processes lay the groundwork for successful AI implementation.

The Basics of Feature Engineering

Feature engineering involves turning raw data into informative characteristics, enabling AI algorithms to capture complex relationships within the data. The process aims to optimize AI model predictive capabilities, leading to more accurate and robust predictions.

Key Techniques in Feature Engineering

Feature Selection

Identifying the most relevant variables that significantly contribute to the target variable is critical. Techniques like correlation analysis and feature selection algorithms help in making informed decisions about feature inclusion.

Feature Construction

Creating new features by combining or transforming existing ones provides better insights. Feature construction enhances AI model understanding and predictive capabilities.

Data Scaling

Scaling data ensures all features are on the same scale, preventing certain variables from dominating the model.

Dimensionality Reduction

Dimensionality reduction techniques like Principal Component Analysis (PCA) help compress data while preserving most of its variance, resulting in more efficient models.

Well-executed feature engineering leads to improved model performance, increased interpretability, robustness to noise, and better generalization to new data.

The Role of Data Labeling and Annotation

In certain AI applications, particularly supervised learning, data labeling and annotation play a crucial role. Data labeling is the process of manually assigning labels to the input data, providing AI models with a labeled dataset as the ground truth for training. This labeled data enables AI systems to learn from well-defined examples and generalize to new, unseen data.

Applications of Data Labeling and Annotation

Holistic Training & Support

Image Recognition and Computer Vision

Grasping the core segments of the customer base permits banks to align their strategies with the highest growth potential sectors, optimizing the return on investment.

Natural Language Processing (NLP)

Data labeling involves tagging text data with specific labels, aiding AI models in understanding language structure and meaning.

Speech Recognition

Data labeling enables AI systems to transcribe spoken words accurately, enabling seamless voice interactions.

Accurate data labeling results in improved model accuracy, adaptability, reduced bias, and human-in-the-loop AI development.

The Roadmap to AI-Ready Data

Data cleaning and preprocessing, feature engineering, and data labeling and annotation are pivotal processes in building accurate and efficient AI models. Organizations that prioritize these practices will be well-equipped to uncover valuable insights, make data-driven decisions, and harness the full potential of AI for transformative success.

 

To help you navigate the complexities of preparing your data for AI, OneSix has authored a comprehensive roadmap to AI-ready data. Our goal is to empower organizations with the knowledge and strategies needed to modernize their data platforms and tools, ensuring that their data is optimized for AI applications.

 

Read our step-by-step guide for a deep understanding of the initiatives required to develop a modern data strategy that drives business results.

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OneSix helps companies build the strategy, technology and teams they need to unlock the power of their data.

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Maximizing AI Value through Effective Data Management and Integration

Maximizing AI Value through Effective Data Management and Integration

Written by

Faizan Hussain, Senior Manager

Published

August 9, 2023

Data & App Engineering
AI & Machine Learning

Artificial Intelligence (AI) has become a game-changer for businesses worldwide, offering unparalleled opportunities to extract value from data and address complex challenges. To fully leverage AI’s potential, organizations must define clear use cases and objectives, assess data availability and quality, and implement effective data collection and integration strategies. In this blog post, we will explore how these crucial components work together to unlock the true power of AI and drive informed decision-making. 

Defining AI Use Cases and Objectives for Maximum Impact

The first step in leveraging AI effectively is to identify the specific business problem or opportunity that you aim to address. Whether it is streamlining operational processes, enhancing customer experiences, optimizing resource allocation, or predicting market trends, it is essential to pinpoint the use case that aligns with your organization’s strategic goals. Defining the use case sets the context for data collection, analysis, and model development, ensuring that efforts are concentrated on the areas that will provide the most significant impact. 

Once the use case is established, the next step is to set clear objectives for the AI project. Objectives outline the desired outcomes and define the metrics that will measure success. They help to focus efforts, guide decision-making, and monitor progress throughout the project lifecycle. Objectives should be specific, measurable, achievable, relevant, and time-bound (SMART), ensuring that they are realistic and attainable within the given constraints.

With the use case and objectives defined, the focus shifts to data preparation. Data is the lifeblood of AI systems, and the quality, relevance, and diversity of data play a critical role in the accuracy and effectiveness of AI models. By aligning data preparation efforts with the AI goals, businesses can ensure that the collected data variables are relevant and comprehensive enough to address the defined use case and objectives.  

Assessing Data Availability and Quality for AI-Readiness

To harness the power of AI effectively, it is essential to identify the data sources that contain the relevant information required to address the AI use case. This involves understanding the nature of the problem or opportunity at hand and determining the types of data that can provide insights and support decision-making. By identifying and accessing the right data sources, organizations can lay the groundwork for meaningful analysis and model development.

Data quality is measured by six key dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness.

Data completeness is a critical aspect of data quality. It refers to the extent to which the data captures all the necessary information required for the AI use case. During the assessment, it is important to evaluate whether the available data is comprehensive enough to address the objectives defined earlier. Are there any missing data points or gaps that may hinder accurate analysis? If so, organizations need to consider strategies to fill those gaps, such as data augmentation or seeking additional data sources.

 

The accuracy of data is paramount for reliable AI outcomes. During the assessment, organizations should scrutinize the data for any errors, inconsistencies, or outliers that may compromise the integrity of the analysis. This may involve data profiling, statistical analysis, or comparing data from multiple sources to identify discrepancies. By addressing data accuracy issues early on, organizations can ensure that their AI models are built on a solid foundation of reliable and trustworthy data. 

 

Data reliability pertains to the trustworthiness and consistency of the data sources. It is crucial to evaluate the credibility and provenance of the data to ensure that it aligns with the organization’s standards and requirements. This assessment may involve understanding the data collection methods, data governance practices, and data validation processes employed by the data sources. Evaluating data reliability helps organizations mitigate the risk of basing decisions on flawed or biased data. 

 

Based on the assessment results, organizations may need to undertake data cleansing and preprocessing steps to enhance the quality and usability of the data. Data cleansing involves identifying and resolving issues such as duplicate records, missing values, and inconsistent formatting. Preprocessing steps may include data normalization, feature engineering, and scaling, depending on the specific AI use case. By investing effort in data cleansing and preprocessing, organizations can optimize the performance and accuracy of their AI models.

The Power of Data Collection and Integration

Before embarking on the data collection and integration process, it is crucial to identify the relevant data sources. Once the relevant data sources have been identified, the next step is to collect data from these disparate sources. This process may involve using a combination of techniques such as data extraction, web scraping, or APIs (Application Programming Interfaces) to gather the required data. It is important to ensure the collected data is accurate, consistent, and adheres to any relevant data privacy regulations.

Data integration is the process of combining data from different sources into a unified repository or data warehouse. By consolidating data into a single location, organizations can eliminate data silos that often hinder comprehensive analysis. Siloed data is scattered across different systems or departments, making it difficult to gain a holistic view of the organization’s operations. Data integration allows for a holistic approach to data analysis, enabling cross-functional insights and fostering collaboration among teams. Data integration offers numerous benefits, including: 

Comprehensive analysis

Leveraging integrated data for deeper insights and decision-making

Enhanced data quality

Ensuring reliable and trustworthy data through integration

Real-time insights

Responding quickly to market trends and opportunities with timely data

Streamlined reporting

Automating reporting processes for efficient information dissemination

Data Governance for Ethical Data Handling

While data collection and integration offer numerous benefits, there are challenges that organizations must address: 

Data Governance

Establishing data governance policies and procedures is crucial to ensure data privacy, security, and compliance. Organizations need to define roles, responsibilities, and access controls to protect sensitive data and ensure ethical data handling practices. 

Data Compatibility

Data collected from various sources may have different formats, structures, or standards. Ensuring compatibility and standardization during the integration process is essential to maintain data integrity and facilitate seamless analysis.

Scalability

As data volumes grow, organizations need to ensure their data integration processes can handle increasing data loads efficiently. Scalable infrastructure and data integration technologies are necessary to support the expanding needs of the organization. 

The Roadmap to AI-Ready Data

Defining AI use cases, assessing data quality, and embracing integration are essential pillars of successful AI implementation. Organizations that strategically combine these aspects can unlock the true potential of AI, making informed decisions, identifying opportunities, and gaining a competitive edge in the data-driven era. 

 

To help you navigate the complexities of preparing your data for AI, OneSix has authored a comprehensive roadmap to AI-ready data. Our goal is to empower organizations with the knowledge and strategies needed to modernize their data platforms and tools, ensuring that their data is optimized for AI applications. 

 

Read our step-by-step guide for a deep understanding of the initiatives required to develop a modern data strategy that drives business results.

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The Future of Snowflake: Data-Native Apps, LLMs, AI, and more

The Future of Snowflake: Data-Native Apps, LLMs, AI, and more

Written by

Ajit Monteiro, CTO & Co-Founder

Published

June 27, 2023

Data & App Engineering
AI & Machine Learning
Snowflake

OneSix is excited to be attending the world’s largest data, apps, and AI conference: Snowflake Summit. The opening keynote had a lot of exciting announcements for the world of data, and continued strategy of rolling out AI and Data-Native App capabilities to their platform . Below are some of the things we found most interesting:

A more complete Data-Native Apps Stack with Container Services

Streamlit and Snowpark have been available for a while now. However, the addition of Snowpark Container Services helps us fully realize Snowflake’s Data Native Apps goals.

Continuing their vision of moving all your company’s data into Snowflake as a governed secure environment, you can now use it in a more cloud platform centric way. Snowpark Container Services allows you to run Docker containers which can then be called by Snowpark; you now have a UI solution (Streamlit), a data-native coding solution (Snowpark) and a way to run legacy applications (Snowpark Container Services) in the Snowflake cloud. You can then easily distribute and monetize these apps through their marketplace.

Use Case Example: A client of ours wanted to use Python OCR services that leverage Tesseract. In the past this was difficult to do since you cannot install Tesseract in Snowpark, Snowpark Container Services will allow us to install Tesseract in a container, and use a wrapper Python library like Pytesseract in Snowpark to leverage it.

Large Language Models and Document AI

It seems like everyone has been talking about large language models (LLMs) lately, and it’s not surprising that Snowflake had some big announcements around it. It was interesting to learn about Snowflake’s partnership with Nvidia to power their Container Services, as well as their first party LLM service.

They also released a feature called Document AI that allows you to train their large language model with your documents and then ask questions against them. This UI based approach allows you to modify the model’s answers to your questions about the document. Those modifications feed back into the LLM, training it to work better on your company’s data.

Streamlit becoming a more robust app UI platform

Streamlit has been historically marketed as a ML focused UI tool. However new features are making it a more viable platform for hosting general apps on Snowflake. A notable feature that have been released this year are editable data frames, including copying and pasting from Excel, which will allow you to manage and cleanse data more effectively. Snowflake is also close to enabling you to host Streamlit in Snowflake, under the Data Native App Framework, furthering their one data cloud goals.

Streaming + Dynamic Tables

Snowflake announced the debut of Dynamic Tables, now available in public preview. Dynamic Tables allow users to perform transformations on real-time streaming data, for example via the Snowflake Kafka Connector, which is near general availability. Dynamic Table transformations are defined with a SELECT statement, allowing for flexible transformation logic that is applied directly after the streaming data lands in Snowflake. It’s as simple as defining a view definition, but with the cost efficiency of a table, all with real-time streaming data.

As a Snowflake Premier Partner, OneSix helps companies build the strategy, technology, and teams they need to unlock the power of their data. Reach out to learn more about Snowflake’s latest innovations and how we can help you get the most out of your investment.

Narrate IQ: Delivering AI-Fueled Data Insights through Slack

Narrate IQ: Delivering AI-Fueled Data Insights through Slack

Published

April 14, 2023

AI & Machine Learning
Snowflake

Traditionally, companies use dashboards and reporting-based visualization tools to analyze their data. These visualizations are prebuilt by developers and require technical resources to maintain and update. But executives and business users don’t always know what questions they will have about their data, and the reality is that decision-makers don’t have time to explore a dashboard. We believe the next evolution of data analytics is building a data architecture that can quickly leverage the latest artificial intelligence (AI) advancements for fast, on-demand analysis. That’s where the power of augmented analytics comes in.

Introducing Narrate IQ: Transform your dashboards into a narrative

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“It’s like having a conversation with your analytics team—right there in Slack.”

Narrate IQ is a powerful set of tools that sits on top of Snowflake and makes the data work for you. Now executives can get more out of their data, gain valuable insights, and make more informed decisions. It’s just one of the ways that OneSix is helping companies build their Modern Data Org by combining modern data tools with the latest advancements in AI. 

Role-specific use cases: How does it work for your team?

Narrate IQ can generate role-specific daily data summaries that answer these questions and send them to you in the tool of your choice, like Slack. Then, with our ChatGPT integration using Azure’s OpenAI Service, users can ask follow-up questions about their data and receive answers without opening a BI tool. Here are some role-specific example questions: 

Marketing

How is the recent campaign doing?
How is my Google Ads spend affecting web traffic?
What are my trending SEO keywords?

Sales

Which regions are performing well/poorly?
Who was my top sales rep last month?
What is my leading product so far this year?

Finance

How is revenue trending relative to last year?
How is my AR trending month over month?
Which department had the largest increase in expenses last month?

Human Resources

How is my staffing utilization looking last month compared to this month?
Is my recruiting pipeline growing compared to last year?

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