No-code, low-code & automation The idea of “no-code” software has become increasingly popular in a variety of fields. The world of AI and Machine Learning (ML) development is no different. Platforms that attempt to make the process of developing AI and ML models more intuitive, less “code-heavy,” and more ubiquitous are gaining in popularity. The challenge of developing AI and ML models is one that screams for no-code or low-code solutions. AI failure rates are notorious – whether it’s VentureBeat reporting 87% failure rates for data science projects in 2019 or Gartner reporting in 2021 that only 53% of AI projects make it into production – even in AI-experienced organizations. While there are many challenges to successfully moving from “experiments” to “ROI” in the world of AI and ML, one of the biggest obstacles is the sheer complexity of the development process. In the world of AI and ML development, “No-Code” and “Low-Code” solutions…
Building an enterprise AI application is problematic. Scaling ML and getting user adoption is even more challenging. Here are the rules to follow. The big day is finally here! After months of squabbles, discussions, and fistfights, you have managed to get the budget allocated for evaluating machine learning. You have identified the right AutoML tool and are eager to get started on your first proof of concept. Where do you begin? This project’s success will determine the course of the digital transformation journey, production roll-out at other business units, and perhaps earn you a fancy new title! If you want to do ML the right way, you need to follow a set of commandments. In this two-part blog series, we’ll cover the ten rules. Here are the top 5 : Start with critical objectives for the project, and think about alignment with business goals. Identify top use cases, key performance…
This article was originally posted February 18, 2020 on Forbes Cognitive World - AI Contributor Group. dotData's Founder and CEO - Ryohei Fujimaki, PhD was an interviewed contributor for this important information share. In today's competitive global insurance market, insurers are striving to create new ways to successfully overcome two important and opposing forces: creating short-term revenue growth for the company, while also meeting customers' needs for product offerings and services that are personalized, relevant, and provide long-term value. In meeting these challenges, insurers realize the strategic importance of their data, and how AI and machine learning (ML) can help them better achieve their business goals. But while investments in AI are growing, challenges in resources, technology infrastructure, and the ability to operationalize models quickly and efficiently can prevent insurers from fully leveraging AI and data science to drive business impact. These were some of the challenges faced by leading global…
Today dotData is thrilled to announce dotData AI-FastStart™, our new exclusive program aimed at helping Business Intelligence professionals with the adoption of AI and Machine Learning (ML) powered Business Intelligence (BI) solutions - regardless of the amount of expertise or infrastructure readiness of the organization. With AI-FastStart™, BI teams can quickly move from zero to a fully operational AI/ML experience in ninety days (90) or less. AI-FastStart™ was born as a direct response to a rapidly changing BI & Analytics world. AI/ML has become a critical technology investment but most organizations still suffer from scaling AI/ML practices. BI+AI (a.k.a. citizen data scientists) is no longer a “nice to have” but must become the new approach to scale AI/ML for organizations. dotData AI-FastStart™ makes AI/ML adoption simple, easy and fast. The program was designed around four core principles: The right platform, education, providing fast time-to-value, and to be easy to deploy…
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…
Previously Published by: Forbes Cognitive World | Original Post: AI In Financial Services: Is Finance Ready For AutoML AI In Financial Services: Is Finance Ready For AutoML This past month I attended the AI for Finance show in New York where I was the host for a one-hour "tech talk" on the subject of AutoML in the world of financial services. With over 150 people in attendance, the audience was able to provide some pointed input on the topic of AI in Financial Services, and especially around the need for AutoML solutions in the space. The results were both surprising and exciting. A diverse audience Let's start with who was in attendance. While this may not have been a vast audience, the 150+ people that attended our presentation represented a relatively broad cross-sampling including many US top-tier financial services organizations - both from a technical and a business perspective - with…
conclusion from last week...Part 2 Beyond AutoML : Data Science Automation While the rise of AutoML platforms has provided for faster execution of "test and learn" ML development, it has also brought about additional challenges. In most ML and data science projects, ML development is only one part of the process. The earlier stages of the process that require handling multiple raw tables and manipulating them based on in-depth domain knowledge to create flat, aggregated feature tables is a far more complicated and time-consuming challenge. The data and feature engineering process in enterprise data science has to deal with such different data as relational, transactional, temporal, geo-locational, and text data, which never starts from a single, flat, aggregated and cleansed table. Data science automation provides for a full-cycle automation process that includes data and feature engineering, in addition to standard AutoML. The ability to automatically generate features from massive and…
Data Science: Complex and Time-Consuming Data science is at the heart of what many are calling the fourth industrial revolution. Businesses leverage Artificial Intelligence (AI) and Machine Learning (ML) across multiple industries and multiple use-cases to make more intelligent decisions and to accelerate decision-making processes. Data scientists play central roles in this revolution. However, according to a 2018 study published by LinkedIn, there is a national shortage of over 150,000 data science-related jobs. This severe shortage means that the race to improve the productivity of data scientists is leading to some exciting new technologies. One of the primary challenges is the sheer complexity, iterative, and highly manual nature of the data science process. Data scientists must sift through scores of raw data, typically found in highly complex systems with hundreds of tables. Integrating and transforming those tables to create "feature tables" is at the heart of the entire process. Perhaps…
continued from last week's post... dotData, Data Science Without The Headaches dotData is a brand new breed of AutoML product that provides what we call Full Cycle Data Science Automation. At the heart of our vision is the idea that the data science process should be fast, easy to perform, and easy to analyze and deploy, from raw business data to the business values. Our vision has led us to develop dotData Enterprise and dotData Py, two related platforms that leverage the same automation engine in uniquely different ways. dotData Enterprise is ideal for the citizen data scientist: fully automated, point-and-click driven, and ready to automate 100% of the data science process without requiring in-depth knowledge of how data science works. dotData Py, on the other hand, is ideal for data scientists. dotData Py provides a python library for Jupyter notebooks, one of the most popular data science platforms available.…
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