Search results for Business Models

Found 1451 documents, 5922 searched:

• Evaluating the Business Value of Predictive Models in Python and R

  In these blogs for R and python we explain four valuable evaluation plots to assess the business value of a predictive model. We show how you can easily create these plots and help you to explain your predictive model to non-techies.

  https://www.kdnuggets.com/2018/10/evaluating-business-value-predictive-models-modelplotpy.html

• The Case of Homegrown Large Language Models

  Recent developments in building large language models (LLMs) to boost generative AI in local languages have caught everyone’s attention. This post focuses on the needs and challenges of homegrown LLMs amid the fast-evolving technology landscape.

  https://www.kdnuggets.com/the-case-of-homegrown-large-language-models

• Are We Undervaluing Simple Models?

  Never underestimate any algorithms that we can use.

  https://www.kdnuggets.com/are-we-undervaluing-simple-models

• Strategies for Optimizing Performance and Costs When Using Large Language Models in the Cloud

  There are many cases where your LLM underperforms and costs you too much in the cloud platform. Simple strategies help you avoid that.

  https://www.kdnuggets.com/strategies-for-optimizing-performance-and-costs-when-using-large-language-models-in-the-cloud

• Introduction to Giskard: Open-Source Quality Management for AI Models

  To solve the conundrum of ensuring the quality of AI models in production — especially given the emergence of LLMs — we are thrilled to announce the official launch of Giskard, the premier open-source AI quality management system.

  https://www.kdnuggets.com/2023/11/giskard-introduction-giskard-opensource-quality-management-ai-models

• Building Data Pipelines to Create Apps with Large Language Models

  For production grade LLM apps, you need a robust data pipeline. This article talks about the different stages of building a Gen AI data pipeline and what is included in these stages.

  https://www.kdnuggets.com/building-data-pipelines-to-create-apps-with-large-language-models

• The Ultimate Guide to Mastering Seasonality and Boosting Business Results

  This post discusses the importance of media mix modeling and how it can be used to maximize the business impact of advertising. It also discusses the impact of seasonality on media advertising and how media mix modeling can be used to minimize the impact of seasonality on business outcomes.

  https://www.kdnuggets.com/2023/08/media-mix-modeling-ultimate-guide-mastering-seasonality-boosting-business-results.html

• Learn Data Science and Business Analytics to Drive Innovation and Growth

  This article provides an overview of data science and business analytics. It also provides a brief introduction to the importance of these topics for your business.

  https://www.kdnuggets.com/2023/08/learn-data-science-business-analytics-drive-innovation-growth.html

• Fine-Tuning OpenAI Language Models with Noisily Labeled Data

  Reduce LLM prediction error by 37% via data-centric AI.

  https://www.kdnuggets.com/2023/04/finetuning-openai-language-models-noisily-labeled-data.html

• Multimodal Models Explained

  Unlocking the Power of Multimodal Learning: Techniques, Challenges, and Applications.

  https://www.kdnuggets.com/2023/03/multimodal-models-explained.html

• Time Series Forecasting with statsmodels and Prophet

  Easy forecast model development with the popular time series Python packages.

  https://www.kdnuggets.com/2023/03/time-series-forecasting-statsmodels-prophet.html

• What Can AI-Powered RPA and IA Mean For Businesses?

  RPA and IA have stunned the business world by availing impressive, intelligent automation capabilities for scales of businesses across industries, which we'll know in this blog.

  https://www.kdnuggets.com/2022/12/aipowered-rpa-ia-mean-businesses.html

• How to Use Analytics to Accelerate Business Growth?

  Many organizations are establishing a Data Analytics team to reap the benefits of their key strategic asset i.e. data. The post explains how you can leverage the power of analytics to understand the end user and generate actionable insights.

  https://www.kdnuggets.com/2022/12/analytics-accelerate-business-growth.html

• Top Open Source Large Language Models

  In this article, we will discuss the importance of large language models and suggest some of the top open source models and the NLP tasks they can be used for.

  https://www.kdnuggets.com/2022/09/john-snow-top-open-source-large-language-models.html

• 6 Ways Businesses Can Benefit From Machine Learning

  Machine learning is gaining popularity rapidly in the business world. Discover the ways that your business can benefit from machine learning.

  https://www.kdnuggets.com/2022/08/6-ways-businesses-benefit-machine-learning.html

• Best Practices for Creating Domain-Specific AI Models

  Here are some best practices and techniques for domain-specific model adaptation that worked for us time and again.

  https://www.kdnuggets.com/2022/07/best-practices-creating-domainspecific-ai-models.html

• Ten Key Lessons of Implementing Recommendation Systems in Business

  We've been long working on improving the user experience in UGC products with machine learning. Following this article's advice, you will avoid a lot of mistakes when creating a recommendation system, and it will help to build a really good product.

  https://www.kdnuggets.com/2022/07/ten-key-lessons-implementing-recommendation-systems-business.html

• Prioritizing Data Science Models for Production

  Statistical performance metrics aren’t enough to pick the right models to bring to market.

  https://www.kdnuggets.com/2022/04/prioritizing-data-science-models-production.html

• Risk Management Framework for AI/ML Models

  How sound risk management acts as a catalyst to building successful AI/ML models.

  https://www.kdnuggets.com/2022/03/risk-management-framework-aiml-models.html

• 3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks

  While there may always seem to be something new, cool, and shiny in the field of AI/ML, classic statistical methods that leverage machine learning techniques remain powerful and practical for solving many real-world business problems.

  https://www.kdnuggets.com/2021/08/3-reasons-linear-regression-instead-neural-networks.html

• Data: The Most Valuable Commodity for Businesses

  Many companies have been capturing customer data in some form or another for decades. Petabytes of data are traversing networks worldwide every day, and all of that data means big money. Here's how companies can best utilize this data to influence positive outcomes.

  https://www.kdnuggets.com/2022/03/data-valuable-commodity-businesses.html

• Models Are Rarely Deployed: An Industry-wide Failure in Machine Learning Leadership

  In this article, Eric Siegel summarizes the recent KDnuggets poll results and argues that the pervasive failure of ML projects comes from a lack of prudent leadership. He also argues that MLops is not the fundamental missing ingredient – instead, an effective ML leadership practice must be the dog that wags the model-integration tail.

  https://www.kdnuggets.com/2022/01/models-rarely-deployed-industrywide-failure-machine-learning-leadership.html

• Why Do Machine Learning Models Die In Silence?

  A critical problem for companies when integrating machine learning in their business processes is not knowing why they don't perform well after a while. The reason is called concept drift. Here's an informational guide to understanding the concept well.

  https://www.kdnuggets.com/2022/01/machine-learning-models-die-silence.html

• KDnuggets™ News 22:n01, Jan 5: 3 Tools to Track and Visualize the Execution of Your Python Code; 6 Predictive Models Every Beginner Data Scientist Should Master

  3 Tools to Track and Visualize the Execution of Your Python Code; 6 Predictive Models Every Beginner Data Scientist Should Master; What Makes Python An Ideal Programming Language For Startups; Alternative Feature Selection Methods in Machine Learning; Explainable Forecasting and Nowcasting with State-of-the-art Deep Neural Networks and Dynamic Factor Model

  https://www.kdnuggets.com/2022/n01.html

• How AI/ML Technology Integration Will Help Business in Achieving Goals in 2022

  AI/ML systems have a wide range of applications in a variety of industries and sectors, and this article highlights the top ways AI/ML will impact your small business in 2022.

  https://www.kdnuggets.com/2021/12/aiml-technology-integration-help-business-achieving-goals-2022.html

• 5 Practical Data Science Projects That Will Help You Solve Real Business Problems for 2022">5 Practical Data Science Projects That Will Help You Solve Real Business Problems for 2022

  This curated list of data science projects offers real-life problems that will help you master skills to demonstration that you are technically sound and know how to conduct data science projects that add business value.

  https://www.kdnuggets.com/2021/12/5-practical-data-science-projects.html

• 7 Top Open Source Datasets to Train Natural Language Processing (NLP) & Text Models

  With a lot of excitement and research around NLP, there are growing opportunities to apply these technologies to real-world scenarios. It's not trivial to become familiar with NLP and these open-source data sets can help you increase your skills.

  https://www.kdnuggets.com/2021/11/top-open-source-datasets-nlp.html

• Serving ML Models in Production: Common Patterns

  Over the past couple years, we've seen 4 common patterns of machine learning in production: pipeline, ensemble, business logic, and online learning. In the ML serving space, implementing these patterns typically involves a tradeoff between ease of development and production readiness. Ray Serve was built to support these patterns by being both easy to develop and production ready.

  https://www.kdnuggets.com/2021/10/serving-ml-models-production-common-patterns.html

• Transforming your business with SAS® Viya® on Microsoft Azure

  Faster, trusted decisions are in the cloud. See how you can use the flexibility, scalability and agility of modern technologies to advance your organization’s goals. Read our blog with 3-part video demo.

  https://www.kdnuggets.com/2021/10/sas-viya-microsoft-azure.html

• Building and Operationalizing Machine Learning Models: Three tips for success

  With more enterprises implementing machine learning to improve revenue and operations, properly operationalizing the ML lifecycle in a holistic way is crucial for data teams to make their projects efficient and effective.

  https://www.kdnuggets.com/2021/10/building-operationalizing-machine-learning-models.html

• How to Find Weaknesses in your Machine Learning Models">How to Find Weaknesses in your Machine Learning Models

  FreaAI: a new method from researchers at IBM.

  https://www.kdnuggets.com/2021/09/weaknesses-machine-learning-models.html

• Choosing the Right BI Tool for Your Business

  Here are six questions to ask as you search for the best BI tool for your specific needs.

  https://www.kdnuggets.com/2021/05/choosing-right-bi-tool-business.html

• Shapash: Making Machine Learning Models Understandable">Shapash: Making Machine Learning Models Understandable

  Establishing an expectation for trust around AI technologies may soon become one of the most important skills provided by Data Scientists. Significant research investments are underway in this area, and new tools are being developed, such as Shapash, an open-source Python library that helps Data Scientists make machine learning models more transparent and understandable.

  https://www.kdnuggets.com/2021/04/shapash-machine-learning-models-understandable.html

• Reducing the High Cost of Training NLP Models With SRU++

  The increasing computation time and costs of training natural language models (NLP) highlight the importance of inventing computationally efficient models that retain top modeling power with reduced or accelerated computation. A single experiment training a top-performing language model on the 'Billion Word' benchmark would take 384 GPU days and as much as $36,000 using AWS on-demand instances.

  https://www.kdnuggets.com/2021/03/reducing-high-cost-training-nlp-models-sru.html

• Data Science vs Business Intelligence, Explained">Data Science vs Business Intelligence, Explained

  Knowing the differences between the business intelligence and data science is more than just a matter of semantics.

  https://www.kdnuggets.com/2021/02/data-science-vs-business-intelligence-explained.html

• Backcasting: Building an Accurate Forecasting Model for Your Business

  This article will shed some light on processes happening under the roof of ML-based solutions on the example of the business case where the future success directly depends on the ability to predict unknown values from the past.

  https://www.kdnuggets.com/2021/02/backcasting-building-accurate-forecasting-model-business.html

• MLOps Is Changing How Machine Learning Models Are Developed

  Delivering machine learning solutions is so much more than the model. Three key concepts covering version control, testing, and pipelines are the foundation for machine learning operations (MLOps) that help data science teams ship models quicker and with more confidence.

  https://www.kdnuggets.com/2020/12/mlops-changing-machine-learning-developed.html

• Machine Learning’s Greatest Omission: Business Leadership

  Eric Siegel's business-oriented, vendor-neutral machine learning course is designed to fulfill vital unmet learner needs, delivering material critical for both techies and business leaders.

  https://www.kdnuggets.com/2020/10/machine-learning-omission-business-leadership.html

• 5 Best Practices for Putting Machine Learning Models Into Production

  Our focus for this piece is to establish the best practices that make an ML project successful.

  https://www.kdnuggets.com/2020/10/5-best-practices-machine-learning-models-production.html

• 5 Challenges to Scaling Machine Learning Models

  ML models are hard to be translated into active business gains. In order to understand the common pitfalls in productionizing ML models, let’s dive into the top 5 challenges that organizations face.

  https://www.kdnuggets.com/2020/10/5-challenges-scaling-machine-learning-models.html

• Your Guide to Linear Regression Models

  This article explains linear regression and how to program linear regression models in Python.

  https://www.kdnuggets.com/2020/10/guide-linear-regression-models.html

• The Insiders’ Guide to Generative and Discriminative Machine Learning Models

  In this article, we will look at the difference between generative and discriminative models, how they contrast, and one another.

  https://www.kdnuggets.com/2020/09/insiders-guide-generative-discriminative-machine-learning-models.html

• Top Online Masters in Analytics, Business Analytics, Data Science – Updated">Top Online Masters in Analytics, Business Analytics, Data Science – Updated

  We provide an updated list of best online Masters in AI, Analytics, and Data Science, including rankings, tuition, and duration of the education program.

  https://www.kdnuggets.com/2020/09/best-online-masters-data-science-analytics-online.html

• Scaling Computer Vision Models with Dataflow

  Scaling Machine Learning models is hard and expensive. We will shortly introduce the Google Cloud service Dataflow, and how it can be used to run predictions on millions of images in a serverless way.

  https://www.kdnuggets.com/2020/07/scaling-computer-vision-models-dataflow.html

• How to make AI/Machine Learning models resilient during COVID-19 crisis

  COVID-19-driven concept shift has created concern over the usage of AI/ML to continue to drive business value following cases of inaccurate outputs and misleading results from a variety of fields. Data Science teams must invest effort in post-model tracking and management as well as deploy an agility in the AI/ML process to curb problems related to concept shift.

  https://www.kdnuggets.com/2020/06/ai-ml-models-resilient-covid-19-crisis.html

• Evidence Counterfactuals for explaining predictive models on Big Data

  Big Data generated by people -- such as, social media posts, mobile phone GPS locations, and browsing history -- provide enormous prediction value for AI systems. However, explaining how these models predict with the data remains challenging. This interesting explanation approach considers how a model would behave if it didn't have the original set of data to work with.

  https://www.kdnuggets.com/2020/05/evidence-counterfactuals-predictive-models-big-data.html

• Explaining “Blackbox” Machine Learning Models: Practical Application of SHAP

  Train a "blackbox" GBM model on a real dataset and make it explainable with SHAP.

  https://www.kdnuggets.com/2020/05/explaining-blackbox-machine-learning-models-practical-application-shap.html

• A Beginner’s Guide to Data Integration Approaches in Business Intelligence

  An integrated BI system has a trickle-down effect on all business processes, especially reporting and analytics. Find out how integration can help you leverage the power of BI.

  https://www.kdnuggets.com/2020/03/beginner-guide-data-integration-approaches-business-intelligence.html

• Uber Unveils a New Service for Backtesting Machine Learning Models at Scale

  The transportation giant built a new service and architecture for backtesting forecasting models.

  https://www.kdnuggets.com/2020/03/uber-unveils-service-backtesting-machine-learning-models-scale.html

• Introducing Generalized Integrated Gradients (GIG): A Practical Method for Explaining Diverse Ensemble Machine Learning Models

  There is a need for a new way to explain complex, ensembled ML models for high-stakes applications such as credit and lending. This is why we invented GIG.

  https://www.kdnuggets.com/2020/01/generalized-integrated-gradients-explaining-ensemble-models.html

• Activation maps for deep learning models in a few lines of code">Activation maps for deep learning models in a few lines of code

  We illustrate how to show the activation maps of various layers in a deep CNN model with just a couple of lines of code.

  https://www.kdnuggets.com/2019/10/activation-maps-deep-learning-models-lines-code.html

• Introducing AI Explainability 360: A New Toolkit to Help You Understand what Machine Learning Models are Doing

  Recently, AI researchers from IBM open sourced AI Explainability 360, a new toolkit of state-of-the-art algorithms that support the interpretability and explainability of machine learning models.

  https://www.kdnuggets.com/2019/08/introducing-ai-explainability-360-toolkit-understand-machine-learning-models.html

• Easily Deploy Deep Learning Models in Production

  Getting trained neural networks to be deployed in applications and services can pose challenges for infrastructure managers. Challenges like multiple frameworks, underutilized infrastructure and lack of standard implementations can even cause AI projects to fail. This blog explores how to navigate these challenges.

  https://www.kdnuggets.com/2019/08/nvidia-deploy-deep-learning-models-production.html

• Decentralized and Collaborative AI: How Microsoft Research is Using Blockchains to Build More Transparent Machine Learning Models

  Recently, AI researchers from Microsoft open sourced the Decentralized & Collaborative AI on Blockchain project that enables the implementation of decentralized machine learning models based on blockchain technologies.

  https://www.kdnuggets.com/2019/07/decentralized-collaborative-ai-microsoft-research-blockchains-transparent-machine-learning.html

• All Models Are Wrong – What Does It Mean?

  During your adventures in data science, you may have heard “all models are wrong.” Let’s unpack this famous quote to understand how we can still make models that are useful.

  https://www.kdnuggets.com/2019/06/all-models-are-wrong.html

• Customer Churn Prediction Using Machine Learning: Main Approaches and Models

  We reach out to experts from HubSpot and ScienceSoft to discuss how SaaS companies handle the problem of customer churn prediction using Machine Learning.

  https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html

• “Please, explain.” Interpretability of machine learning models

  Unveiling secrets of black box models is no longer a novelty but a new business requirement and we explain why using several different use cases.

  https://www.kdnuggets.com/2019/05/interpretability-machine-learning-models.html

• Best US/Canada Masters in Analytics, Business Analytics, Data Science

  In the final part of this series, we provide an updated list of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics from across the US and Canada.

  https://www.kdnuggets.com/2019/05/best-masters-data-science-analytics-us-canada.html

• Comparing MobileNet Models in TensorFlow

  MobileNets are a family of mobile-first computer vision models for TensorFlow, designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application.

  https://www.kdnuggets.com/2019/03/comparing-mobilenet-models-tensorflow.html

• Comparing Machine Learning Models: Statistical vs. Practical Significance

  Is model A or B more accurate? Hmm… In this blog post, I’d love to share my recent findings on model comparison.

  https://www.kdnuggets.com/2019/01/comparing-machine-learning-models-statistical-vs-practical-significance.html

• Data Science Projects Employers Want To See: How To Show A Business Impact">Data Science Projects Employers Want To See: How To Show A Business Impact

  The best way to create better data science projects that employers want to see is to provide a business impact. This article highlights the process using customer churn prediction in R as a case-study.

  https://www.kdnuggets.com/2018/12/data-science-projects-business-impact.html

• Applied Data Science: Solving a Predictive Maintenance Business Problem Part 3

  In this post we will expand our analysis to multiple variables and then see how intuitions we develop during the exploration phase, can lead to generating new features for modelling.

  https://www.kdnuggets.com/2018/10/applied-data-science-solving-predictive-maintenance-business-problem-3.html

• Leveraging Agent-based Models (ABM) and Digital Twins to Prevent Injuries

  Both athletes and machines deal with inter-twined complex systems (where the interactions of one complex system can have a ripple effect on others) that can have significant impact on their operational effectiveness.

  https://www.kdnuggets.com/2018/08/leveraging-agent-based-models-digital-twins-prevent-injuries.html

• Reinforcement Learning: The Business Use Case, Part 2

  In this post, I will explore the implementation of reinforcement learning in trading. The Financial industry has been exploring the applications of Artificial Intelligence and Machine Learning for their use-cases, but the monetary risk has prompted reluctance.

  https://www.kdnuggets.com/2018/08/reinforcement-learning-business-use-case-part-2.html

• Autoregressive Models in TensorFlow

  This article investigates autoregressive models in TensorFlow, including autoregressive time series and predictions with the actual observations.

  https://www.kdnuggets.com/2018/08/autoregressive-models-tensorflow.html

• IoT on AWS: Machine Learning Models and Dashboards from Sensor Data

  I developed my first IoT project using my notebook as an IoT device and AWS IoT as infrastructure, with this "simple" idea: collect CPU Temperature from my Notebook running on Ubuntu, send to Amazon AWS IoT, save data, make it available for Machine Learning models and dashboards.

  https://www.kdnuggets.com/2018/06/zimbres-iot-aws-machine-learning-dashboard.html

• Business intuition in data science

  Data Science projects are not just use of algorithms & building models; there are other steps of the project which are equally important. Here we explain them in detail.

  https://www.kdnuggets.com/2017/10/business-intuition-data-science.html

• Making Predictive Models Robust: Holdout vs Cross-Validation">Making Predictive Models Robust: Holdout vs Cross-Validation

  The validation step helps you find the best parameters for your predictive model and prevent overfitting. We examine pros and cons of two popular validation strategies: the hold-out strategy and k-fold.

  https://www.kdnuggets.com/2017/08/dataiku-predictive-model-holdout-cross-validation.html

• What Is Optimization And How Does It Benefit Business?

  Here we explain what Mathematical Optimisation is, and discuss how it can be applied in business and finance to make decisions.

  https://www.kdnuggets.com/2017/08/optimization-benefit-business.html

• DataScience.com Releases Python Package for Interpreting the Decision-Making Processes of Predictive Models

  DataScience.com new Python library, Skater, uses a combination of model interpretation algorithms to identify how models leverage data to make predictions.

  https://www.kdnuggets.com/2017/05/datascience-skater-python-package-interpreting-predictive-models.html

• Models: From the Lab to the Factory

  In this post, we’ll go over techniques to avoid these scenarios through the process of model management and deployment.

  https://www.kdnuggets.com/2017/04/models-from-lab-factory.html

• Top mistakes data scientists make when dealing with business people">Top mistakes data scientists make when dealing with business people

  There are no cover articles praising the fails of the many data scientists that don’t live up to the hype. Here we examine 3 typical mistakes and how to avoid them.

  https://www.kdnuggets.com/2017/04/top-mistakes-data-scientists-make-business.html

• 6 Business Concepts you need to become a Data Science Unicorn">6 Business Concepts you need to become a Data Science Unicorn

  Are you a data science professional and want to advance your career as Data Science Unicorn? Here we provide important business concepts and guidelines required for a data science techie to become a Unicorn.

  https://www.kdnuggets.com/2017/03/6-business-concepts-data-science-unicorn.html

• Stacking Models for Improved Predictions

  This post presents an example of regression model stacking, and proceeds by using XGBoost, Neural Networks, and Support Vector Regression to predict house prices.

  https://www.kdnuggets.com/2017/02/stacking-models-imropved-predictions.html

• Bad Data + Good Models = Bad Results

  No matter how advanced is your Machine Learning algorithm, the results will be bad if the input data
  is bad. We examine one popular IMDB dataset and discuss how an analyst can deal with such data.

  https://www.kdnuggets.com/2017/01/bad-data-good-models-bad-results.html

• Bringing Business Clarity To CRISP-DM

  Many analytic projects fail to understand the business problem they are trying to solve. Correctly applying decision modeling in the Business Understanding phase of CRISP-DM brings clarity to the business problem.

  https://www.kdnuggets.com/2017/01/business-clarity-crisp-dm.html

• Doctor of Business Administration/Data Analytics, Online at Grand Canyon University

  Offered in a convenient online format, this doctoral program empowers expert data analysts to spark new industry-wide innovation.

  https://www.kdnuggets.com/2017/01/gcu-doctor-business-administration-data-analytics.html

• How To Stay Competitive In Machine Learning Business

  To stay competitive in machine learning business, you have to be superior than your rivals and not the best possible – says one of the leading machine learning expert. Simple rules are defined here to make that happen. Let’s see how.

  https://www.kdnuggets.com/2017/01/stay-competitive-machine-learning-business.html

• KDnuggets Consulting – expert advice on Business Analytics, Data Mining, and Data Science

  Gregory Piatetsky-Shapiro Experience Education Presentations & Tutorials Recent Publications Contact: [my first name] at kdnuggets.com, with a brief description of the issues. Gregory Piatetsky-Shapiro, Ph.D., Read more »

  https://www.kdnuggets.com/data-mining-consulting.html

• The Best Metric to Measure Accuracy of Classification Models

  Measuring accuracy of model for a classification problem (categorical output) is complex and time consuming compared to regression problems (continuous output). Let’s understand key testing metrics with example, for a classification problem.

  https://www.kdnuggets.com/2016/12/best-metric-measure-accuracy-classification-models.html

• Practical Data Science: Building Minimum Viable Models

  Data Science for startups based on data: Minimum Valuable Model, a new concept to avoid a full scale 95% accurate data science model. Want to know more about MVM? Have a look at this interesting article.

  https://www.kdnuggets.com/2016/11/practical-data-science-building-minimum-viable-models.html

• Embedded Analytics: The Future of Business Intelligence

  An overview of the evolution of Business Intelligence, and some insight into where its future lie: embedded analytics.

  https://www.kdnuggets.com/2016/09/embedded-analytics-future-business-intelligence.html

• Big Data Business Model Maturity Index and the Internet of Things (IoT)

  This post explores how organizations could use the Big Data Business Model Maturity Index (BDBMMI) to exploit the Internet of Things (IoT).

  https://www.kdnuggets.com/2016/06/big-data-business-model-maturity-index-iot.html

• Businesses Will Need One Million Data Scientists by 2018

  Deepening shortage of Data Science talent and cybersecurity challenges are trends shaping business in 2016.

  https://www.kdnuggets.com/2016/01/businesses-need-one-million-data-scientists-2018.html

• New Standard Methodology for Analytical Models

  Traditional methods for the analytical modelling like CRISP-DM have several shortcomings. Here we describe these friction points in CRISP-DM and introduce a new approach of Standard Methodology for Analytics Models which overcomes them.

  https://www.kdnuggets.com/2015/08/new-standard-methodology-analytical-models.html

• Interview: Daqing Zhao, Macys.com on Building Effective Data Models for Marketing

  We discuss the challenges in identifying the fair price of ad media, recommendations for building effective models for online marketing, unique challenges of Mobile channel, selection of Big Data tools, and more.

  https://www.kdnuggets.com/2014/12/interview-daqing-zhao-macys-data-models-marketing.html

• 3 Ways to Test the Accuracy of Your Predictive Models

  3 different methods for testing accuracy of predictive models from 3 leading analytics experts - Karl Rexer, John Elder, and Dean Abbott explain using lift charts, randomization testing, and bootstrap sampling.

  https://www.kdnuggets.com/2014/02/3-ways-to-test-accuracy-your-predictive-models.html

• The Generative AI Bubble Will Burst Soon

  Due to unsustainable hype, unrealistic valuations, limitations of current technology, and unproven business models.

  https://www.kdnuggets.com/the-generative-ai-bubble-will-burst-soon

• Blockchains and APIs

  Major technological advances are providing opportunities for new business models, based on blockchain, which will see an increase in the number of connected devices in our day-to-day lives.

  https://www.kdnuggets.com/2018/03/blockchains-apis.html

• Navigating Today’s Data and AI Market Uncertainty

  It’s more important than ever to think long-term about the analytics partnerships you forge. Are you choosing technologies that will stand the test of time? Are you choosing companies with proven track records?

  https://www.kdnuggets.com/2024/02/altair-navigating-todays-data-ai-market-uncertainty

• Beyond Skynet: Crafting the Next Frontier in AI Evolution

  The article discusses the value of reliable data, democratizing access to valuable data insights, and consumer privacy’s impact on the competitive edge of big tech.

  https://www.kdnuggets.com/beyond-skynet-crafting-the-next-frontier-in-ai-evolution

• The Data Maturity Pyramid: From Reporting to a Proactive Intelligent Data Platform

  This article describes the data maturity pyramid and its various levels, from simple reporting to AI-ready data platforms. It emphasizes the importance of data for business and illustrates how data platforms serve as the driving force behind AI.

  https://www.kdnuggets.com/the-data-maturity-pyramid-from-reporting-to-a-proactive-intelligent-data-platform

• Who Will Make Money from the Generative AI Gold Rush?

  Buckle up for the Generative AI gold rush! Will BigTech rule with its picks and shovels? Which startups will strike it rich? Will “copilot for X” be the business strategy to hit pay dirt? How can startups dig moats to keep out other prospectors? And will the US once again have the richest gold seams?

  https://www.kdnuggets.com/2023/08/make-money-generative-ai-gold-rush.html

• 5 Ways to Deal with the Lack of Data in Machine Learning

  Effective solutions exist when you don't have enough data for your models. While there is no perfect approach, five proven ways will get your model to production.

  https://www.kdnuggets.com/2019/06/5-ways-lack-data-machine-learning.html

• MLOps: The Key To Pushing AI Into The Mainstream

  In this blog, we will aim at discussing the reasons that make MLOps an essential aspect of pushing AI mainstream. Besides, we will highlight the capabilities of MLOps as a catalyst for AI implementation.

  https://www.kdnuggets.com/2022/07/mlops-key-pushing-ai-mainstream.html

• 5 Ways To Use AI For Supply Chain Management

  Using AI to help optimize supply chain management is becoming more prevalent across industries. Early adopters are more resilient and prepared for the inevitable future of artificial intelligence within the supply chain management industry.

  https://www.kdnuggets.com/2022/02/5-ways-ai-supply-chain-management.html

• Should You Become a Freelance Artificial Intelligence Engineer?

  Take the first step towards your machine learning engineering career and explore the UC San Diego Extension Machine Learning Engineering Bootcamp today. Those with prior software engineering or data science experience are encouraged to apply.

  https://www.kdnuggets.com/2021/12/ucsd-become-freelance-artificial-intelligence-engineer.html

• How Data Scientists Can Get the Ear of CFOs (And Why You Want It)

  Hey, data scientists! Here’s how to bend your CFO’s ear, equip your company with high-quality analysis, and boost your value and career in the process.

  https://www.kdnuggets.com/2021/12/data-scientists-get-ear-cfos-want.html

• Including ModelOps in your AI strategy

  The strategic power of AI has been established thoroughly across many industries and companies, leading to surges in model creation. Investments in the people, processes, and tools for operationalizing models, referred to as ModelOps, lag. This function of operationalizing, integrating, and deploying AI models in line with businesses value expectations is growing into a core business capability as global use of AI matures.

  https://www.kdnuggets.com/2021/08/modelops-ai-strategy.html

• How to use Machine Learning for Anomaly Detection and Conditional Monitoring

  This article explains the goals of anomaly detection and outlines the approaches used to solve specific use cases for anomaly detection and condition monitoring.

  https://www.kdnuggets.com/2020/12/machine-learning-anomaly-detection-conditional-monitoring.html

• 9 Developing Data Science & Analytics Job Trends

  With so much disruption in 2020 already, a recent report by Burtch Works looks ahead to next year and beyond, and shares insights about how today's hiring market trends may impact our work lives for years to come.

  https://www.kdnuggets.com/2020/09/data-science-analytics-job-trends.html

• Free Data Analytics Courses

  Wherever your skills are at today, check out these top course recommendations for 2020 to help you master data analytics.

  https://www.kdnuggets.com/2020/06/free-data-analytics-courses.html

• Top 10 Data Visualization Tools for Every Data Scientist">Top 10 Data Visualization Tools for Every Data Scientist

  At present, the data scientist is one of the most sought after professions. That’s one of the main reasons why we decided to cover the latest data visualization tools that every data scientist can use to make their work more effective.

  https://www.kdnuggets.com/2020/05/top-10-data-visualization-tools-every-data-scientist.html

• Managing Machine Learning Cycles: Five Learnings from comparing Data Science Experimentation/ Collaboration Tools

  Machine learning projects require handling different versions of data, source code, hyperparameters, and environment configuration. Numerous tools are on the market for managing this variety, and this review features important lessons learned from an ongoing evaluation of the current landscape.

  https://www.kdnuggets.com/2020/01/managing-machine-learning-cycles.html

• AI, Analytics, Machine Learning, Data Science, Deep Learning Technology Main Developments in 2019 and Key Trends for 2020">AI, Analytics, Machine Learning, Data Science, Deep Learning Technology Main Developments in 2019 and Key Trends for 2020

  We asked leading experts - what are the most important developments of 2019 and 2020 key trends in AI, Analytics, Machine Learning, Data Science, and Deep Learning? This blog focuses mainly on technology and deployment.

  https://www.kdnuggets.com/2019/12/predictions-ai-machine-learning-data-science-technology.html

• The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization">The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization

  As a data scientist, your most important skill is creating meaningful visualizations to disseminate knowledge and impact your organization or client. These seven principals will guide you toward developing charts with clarity, as exemplified with data from a recent KDnuggets poll.

  https://www.kdnuggets.com/2019/10/4-quadrants-data-science-skills-data-visualization.html

• Data Preparation for Machine learning 101: Why it’s important and how to do it

  As data scientists who are the brains behind the AI-based innovations, you need to understand the significance of data preparation to achieve the desired level of cognitive capability for your models. Let’s begin.

  https://www.kdnuggets.com/2019/10/data-preparation-machine-learning-101.html

• 6 Tips for Building a Training Data Strategy for Machine Learning

  Without a well-defined approach for collecting and structuring training data, launching an AI initiative becomes an uphill battle. These six recommendations will help you craft a successful strategy.

  https://www.kdnuggets.com/2019/09/6-tips-training-data-strategy-machine-learning.html

• Proptech and the proper use of technology for house sales prediction

  Using the ATTOM dataset, we extracted data on sales transactions in the USA, loans, and estimated values of property. We developed an optimal prediction model from correlations in the time and status of ownership as well as the time of the year of sales fluctuations.

  https://www.kdnuggets.com/2019/08/proptech-technology-house-sales-prediction.html

• Big Data for Insurance

  The insurance industry has always been quite conservative; however, the adoption of new technologies is not just a modern trend but a necessity to maintain the competitive pace. In the modern digital era, Big Data technologies help to process vast amounts of information, increase workflow efficiency, and reduce operational costs. Learn more about the benefits of Big Data for insurance from our material.

  https://www.kdnuggets.com/2019/07/big-data-insurance.html

• The Death of Big Data and the Emergence of the Multi-Cloud Era">The Death of Big Data and the Emergence of the Multi-Cloud Era

  The Era of Big Data is coming to an end as the focus shifts from how we collect data to processing that data in real-time. Big Data is now a business asset supporting the next eras of multi-cloud support, machine learning, and real-time analytics.

  https://www.kdnuggets.com/2019/07/death-big-data-multi-cloud-era.html

• How To Get Funding For AI Startups

  What are the biggest challenges AI startups have when pitching to investors? Learn how to grab their attention with these recommendations on how to start building your AI company.

  https://www.kdnuggets.com/2019/06/funding-ai-startups.html

• Top R Packages for Data Cleaning

  Data cleaning is one of the most important and time consuming task for data scientists. Here are the top R packages for data cleaning.

  https://www.kdnuggets.com/2019/03/top-r-packages-data-cleaning.html

• How AI can help solve some of humanity’s greatest challenges – and why we might fail

  AI represents a step change in humanity’s ability to rise to its greatest challenges. We explore three areas in which AI can contribute to the UN’s Global Goals - and why we could fall short.

  https://www.kdnuggets.com/2019/02/ai-help-solve-humanity-challenges.html

• The Big Data Game Board™">The Big Data Game Board™

  Move aside “Monopoly,” “Risk,” and “Snail Race!” Time to teach the youth of the world of an important, career-advancing game: how to leverage data and analytics to change your life! Introducing the “Big Data Game Board™”!

  https://www.kdnuggets.com/2018/11/big-data-game-board.html

• Data Science “Paint by the Numbers” with the Hypothesis Development Canvas

  Now you are ready to take the next step from a Big Data MBA perspective by building off of the Business Model Canvas to flesh out the business use cases – or hypothesis – which is where we can become more effective at leveraging data and analytics to optimize our the business.

  https://www.kdnuggets.com/2018/11/data-science-paint-by-numbers-hypothesis-development-canvas.html

• A Winning Game Plan For Building Your Data Science Team">A Winning Game Plan For Building Your Data Science Team

  We need to understand the responsibilities, capabilities, expectations and competencies of the Data Engineer, Data Scientist and Business Stakeholder.

  https://www.kdnuggets.com/2018/09/winning-game-plan-building-data-science-team.html

• The Economics and Benefits of Artificial Intelligence

  In this article, focus on current AI, which is mostly based on the algorithms that can do predictions, and discuss how the economics of AI works and how it may affect business.

  https://www.kdnuggets.com/2018/09/economics-benefits-artificial-intelligence.html

• Top SAS Courses Online

  High quality SAS training for beginners is out there and I’ll help you find it.

  https://www.kdnuggets.com/2018/05/top-sas-courses-online.html

• Top 10 Technology Trends of 2018">Top 10 Technology Trends of 2018

  In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.

  https://www.kdnuggets.com/2018/04/top-10-technology-trends-2018.html

• Calculating Customer Lifetime Value: SQL Example

  In order to understand how to estimate LTV, it is useful to first think about evaluating a customer’s lifetime value at the end of their relationship with us.

  https://www.kdnuggets.com/2018/02/calculating-customer-lifetime-value-sql-example.html

• Exclusive Interview: Doug Laney on Big Data and Infonomics

  We discuss 3Vs of Big Data; Infonomics and many aspects of monetizing information including promising analytics methods, successful companies, main challenges; Information marketplaces and why data ownership concept is misguided, and more.

  https://www.kdnuggets.com/2018/01/exclusive-interview-doug-laney-big-data-infonomics.html

• Democratizing Artificial Intelligence, Deep Learning, Machine Learning with Dell EMC Ready Solutions

  Democratization is defined as the action/development of making something accessible to everyone, to the “common masses.” AI | ML | DL technology stacks are complicated systems to tune and maintain, expertise is limited, and one minimal change of the stack can lead to failure.

  https://www.kdnuggets.com/2018/01/democratizing-ai-deep-learning-machine-learning-dell-emc.html

• How to build a Successful Advanced Analytics Department">How to build a Successful Advanced Analytics Department

  This article presents our opinions and suggestions on how an Advanced Analytics department should operate. We hope this will be useful for those who want to implement analytics work in their company, as well as for existing departments.

  https://www.kdnuggets.com/2018/01/build-successful-advanced-analytics-department.html

• Best Masters in Data Science and Analytics – Asia and Australia Edition

  The fourth edition of our comprehensive, unbiased survey on graduate degrees in Data Science and Analytics from around the world.

  https://www.kdnuggets.com/2017/12/best-masters-data-science-analytics-asia-australia.html

• Why Every Company Needs a Digital Brain

  As emerging technologies like AI/machine learning are adopted across different parts of the business, enterprises require a “digital brain” to coordinate those efforts and generate systemic intelligence.

  https://www.kdnuggets.com/2017/07/why-every-company-needs-digital-brain.html

• The Artificial ‘Artificial Intelligence’ Bubble and the Future of Cybersecurity

  What’s going on now in the field of ‘AI’ resembles a soap bubble. And we all know what happens to soap bubbles eventually if they keep getting blown up by the circus clowns (no pun intended!): they burst.

  https://www.kdnuggets.com/2017/06/kaspersky-artificial-intelligence-bubble-future-cybersecurity.html

• Must-Know: How to determine the influence of a Twitter user?

  The influence of a Twitter user goes beyond the simple number of followers. We also want to examine how effective are tweets - how likely they are to be retweeted, favorited, or the links inside clicked upon. What exactly is an influential user depends on the definition.

  https://www.kdnuggets.com/2017/05/must-know-determine-influence-twitter-user.html

• The Internet of Things in the Cloud

  Cloud computing is the next evolutionary step in Internet-based computing, which provides the means for delivering ICT resources as a service. Internet-of-Things can benefit from the scalability, performance and pay-as-you-go nature of cloud computing infrastructures.

  https://www.kdnuggets.com/2017/05/internet-of-things-iot-cloud.html

• A Brief History of Artificial Intelligence">A Brief History of Artificial Intelligence

  This post is a brief outline of what happened in artificial intelligence in the last 60 years. A great place to start or brush up on your history.
   
   

  https://www.kdnuggets.com/2017/04/brief-history-artificial-intelligence.html