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The Top 5 AI & Machine Learning Trends

By dotData

Updated The past couple of years have seen dramatic changes as the pandemic first drove the need for more online transactions and communications, then became the cause of worldwide shortages that have increased inflation and forced organizations around the globe to rethink their strategies. Machine Learning stands at the epicenter of these changes, but the long-term success of ML projects will be impacted by technology trends that continue to alter and shape the market for Machine Learning products and enabling technologies. This post is an updated version of an older post. Here are the latest trends we see as critical to ML practitioners: Augmented Analytics Transforming Business Intelligence Augmented Analytics will transform Business Intelligence – Augmented Analytics uses AI and ML technologies to assist with data preparation, insight generation, and explanation to expand how people explore and analyze data in analytics and BI platforms. AI is a critical enabling technology,…

Automated Feature Selection Methods to Assess Client Risk

By Hari Naryanan

In industries like Financial Services, Housing, and Insurance, the automated scoring of client risk can provide critical benefits. By quantifying the potential hazards for an organization, risk profiling helps organizations build sustainable long-term growth and minimizes losses in economic downturns. However, traditional client risk scoring methodologies are often not sufficient but can be augmented with machine learning-based models. Client Risk Profile Examples Popular use cases across different domains include: Which of my customers might default on loan payments? (Finance)Can a supplier deliver goods on time? (Manufacturing)Will my new tenant pay the rent on time? (Housing)What are the risks associated with a specific property or driver? (Insurance) Challenges with Traditional Risk Profiling Methods Traditionally, businesses solved these use-cases with rule-based approaches. A banker might have used a credit score and household income to determine loan eligibility. Simple rule-based logic is easy to implement but does not provide enough scalability and robustness…

An Improved Approach to Time-Series Forecasting

By dotData

New automated feature engineering tools remove the need to choose between accuracy and interpretability. Revenue and demand forecasting are among the most common use cases in data science, with abundant available data and clear business value across multiple industries. However, little agreement remains about the ‘best’ approach for building such forecasting models. New automated feature engineering tools are making that debate less relevant. Drawbacks of Traditional Forecasting Solutions Algorithms such as ARIMA (Autoregressive Integrated Moving Average), SARIMA (Seasonal Autoregressive Integrated Moving Average), or XGBoost, remain popular due to their flexibility and interpretability. In a search for superior predictive performance, practitioners have been learning and incorporating an ever-expanding catalog of AI-based neural network architectures into their forecasting. Despite often outperforming classical approaches in forecasting accuracy, neural networks are not a one-size-fits-all solution. Neural networks are mostly uninterpretable, leaving the business with limited insights into what’s driving the forecast, limiting business impact…

Five Critical Predictive Analytics Mistakes (and How to Avoid Them)

By Walter Paliska

The business world is increasingly in love with all things AI. Included in this is the increasing demand for predictive analytics among enterprise companies. In fact, according to research firm Markets & Markets, demand for predictive analytics is expected to grow to an impressive US$28B by the year 2026. Forecasts are often educated guesses, but if demand for data scientists (the specialists needed for most predictive analytics projects) is any indication, the estimates might just be on target. In fact, in 2021, the demand for data scientists, as measured by job openings, grew by over 250% over 2020. Yet, with all the need for machine learning and predictive analytics, the reality is that over 87% of machine learning projects still fail. The past five years have seen a flurry of activity in the world of machine learning and predictive analytics with new tools that promise to make predictive analytics simple…

AI for Small and Medium Businesses; not an oxymoron anymore

By Sachin Andhare

With dotData Cloud, a fully managed end-to-end AI solution, SMBs have AI-powered predictive analytics with zero coding at their disposal. The latest Worldwide Artificial Intelligence Spending Guide from IDC shows that spending on AI is projected to double over the coming years to $110 billion by 2024, growing at a CAGR of 20.1%. While AI is becoming a must-have technology for large enterprises, small and medium businesses are getting left out. According to Deloitte’s State of AI survey, the top two benefits that enterprise customers are seeking from AI are making processes more efficient and enhancing existing products and services.  However, these benefits are equally applicable and important to SMBs. In fact, SMBs will benefit more from creating new products and services, an area where small companies and startups shine, making employees more productive and enhancing the product portfolio by leveraging AI. Unfortunately, few SMBs are using AI. The perception…

The 10 Commandments of AI & ML (P2)

By Sachin Andhare

  Concerned about accelerating AI workflows, addressing multiple use cases, and scaling ML initiatives? Here are the rules to help you succeed. In this two-part blog series, we review the ten rules that will ensure success with your first AI/ML project paving the way for many more. In the first part, we discussed the alignment of business objectives with use cases, getting a head start on data preparation and the mechanics of feature engineering. We also talked about understanding AutoML tools’ capabilities and ensuring the right modeling approach while balancing model accuracy and interpretability. This second part will discuss why visibility to the ML processes and results are critical, the importance of data science education, real-time analytics, infrastructure compatibility, and ML operationalization. Here are the five best practices: Ensure the ML project has visibility. ML initiatives fail because they operate with a silo mentality, like a secret science experiment that…

Simplify Your ML Workflow With AutoML 2.0

By Walter Paliska

AutoML platforms offer “no-code” AI development, but the devil is in the details. For most Business Intelligence professionals, the world of AI and Machine Learning(ML) seems a bit out of reach. The challenge is not so much in whether the technology is useful or not, but rather in the effort required to add AI and ML technology to their BI stacks. The requirement from business users is undoubtedly there. Whether it’s to predict customer churn, model marketing campaign performance, forecast sales, identify clients at high risk of defaulting on receivables, or countless other applications, adding AI/ML to your BI stack can provide immense value. The average BI professional’s problem is that although they are highly skilled at manipulating data and creating sophisticated visualizations, applying the additional data optimization and statistical mathematics necessary to build effective AI/ML models is not within their skill-set. Enter AutoML Workflows The promise of AutoML is…

Accelerating AI Transformation in Manufacturing

By Sachin Andhare

Many experts agree that AI will have the most significant impact on manufacturing. According to McKinsey Research, AI can create $1.2 Trillion to $2 Trillion of value in supply-chain and manufacturing. Manufacturing processes generate enormous amounts of data, involve repetitive tasks, and present multi-dimensional problems beyond the scope of many conventional tools. The industry is also projected to face a workforce shortage due to skilled employees’ looming retirement. AI and Automation are key technologies that can address this gap while increasing operational efficiency, improving quality, and boosting productivity. However, AI has yet to gain significant momentum and reshape manufacturing. Manufacturing executives and plant leaders must overcome several challenges before AI-led digital transformation transitions from a select few to a broad market at scale : Legacy Infrastructure - The production sites typically have a wide variety of machines, tools, and systems that use disparate and often competing technologies. For example, discrete…

Shattering 5 Misconceptions about Automated Machine Learning

By Sachin Andhare

Ask data engineers about the most frustrating part of their job and the answer will most likely include “data preparation.”  Talk to a data scientist about the AI/ML workflow and what bogs them down, the answer invariably will be feature engineering. Analytics and data science leaders are well aware of the limitations of current AI/ML development platforms. They often lament about their team's ability to only manage a few projects per year. BI leaders, on the other hand, have been trying to embed predictive analytics in their dashboards but face the daunting task of learning how to build AI/ML models. Automated machine learning (AutoML) was built specifically to address some of the challenges of data science - the underlying practice at the heart of both problems. Like every new technology, there is a lot of confusion surrounding AutoML. Here are the top 5 misconceptions about AutoML: 1. AutoML means selecting the algorithms…