AI is increasingly playing a pivotal role in businesses and organizations, impacting the outcomes and interests of human users. Automated Machine Learning (AutoML) streamlines the machine learning model development process by automating repetitive tasks and making data-driven decisions, enabling even non-experts to construct high-quality models efficiently. This democratization allows more users (including non-experts) to access and utilize state-of-the-art machine-learning expertise. However, AutoML tools may also propagate bias in the way these tools handle the data, model choices, and optimization approaches adopted. We conducted an experimental study of User-interface-based open source AutoML tools (DataRobot, H2O Studio, Dataiku, and Rapidminer Studio) to examine if they had features to assist users in developing fairness-aware machine learning models. The experiments covered the following considerations for the evaluation of features: understanding use case context, data representation, feature relevance and sensitivity, data bias and preprocessing techniques, data handling capabilities, training-testing split, hyperparameter handling, and constraints, fairness-oriented model development, explainability and ability to download and edit models by the user. The results revealed inadequacies in features that could support in fairness-aware model development. Further, the results also highlight the need to establish certain essential features for promoting fairness in AutoML tools.

Last year was a time of excitement in the machine learning arena as a growing number of startups closed huge funding rounds in 2022. Emerging companies have implemented machine learning solutions to solve administrative and operational hurdles businesses encounter every day. While many machine learning products remain conceptual, machine learning tools are increasingly being adopted by companies worldwide. A number of machine learning companies entered their fourth or even fifth series of funding deals in 2022 and we are starting to see more tangible products, driven by machine learning algorithms, enter the market. With that in mind, let's look at the 10 biggest funding rounds achieved in the machine learning space in 2022, according to research firm GlobalData.

Artificial Intelligence (AI) is not just a buzzword, but a crucial part of the technology landscape and the most disruptive technology. From identifying our faces to recommending the best products, artificial intelligence is everywhere. AI is changing every industry and business function, which results in increased interest in its applications, subdomains, and related fields. This makes AI companies the top leaders driving the technology swiftly. AI helps us to optimize and automate crucial business processes, gather essential data, and transform the world, one step at a time.

Dataiku announced it raised $200 million in a Series F round this week at a $3.7 billion valuation. This latest valuation is a 20% decline from its August 2021 valuation of $4.6 billion, perhaps reflecting the changing macroeconomic tide. The company's latest round was led by a led by new investor, Boston-based Wellington Management, who seems confident in Dataiku's future: "Dataiku's proven track record, management team, growth trajectory, and customer roster, positions the company to scale AI to new heights. We are pleased to partner and contribute to their impressive journey," Matt Witheiler, consumer/technology sector lead, at Wellington Management, said in a release. "Dataiku has taken a leadership position helping enterprises put massive datasets to work at unprecedented speed and creating a culture of AI focused on delivering compounding business results."

NEW YORK, NY, Nov. 17, 2022 (GLOBE NEWSWIRE) -- Dataiku, the platform for Everyday AI, today announced Ben Taylor's appointment as its first Chief AI Strategist. Taylor, a visionary in the advancements of AI, machine learning, and data science, joins the company to help accelerate momentum as it continues to experience soaring demand amongst enterprise organizations and business users. "A simple truth we face is that AI will be part of every business, whether you like it or not. The only question is whether you want to be a leader or a laggard," said Taylor. "However, the technology itself is nothing without people asking the right questions and bringing what makes us intrinsically human to AI. "This is what makes Dataiku truly special - the company is not just about the technical aspects of its solid AI platform but is centered around collaboration and the people who create the types of jaw-dropping projects I hope to be a part of.

Dataiku, the platform for Everyday AI, today unveiled it has joined the Deloitte US Data and AI Alliance Ecosystem to help customers implement and scale AI and MLOps across their organizations. Deloitte's alliance ecosystem includes relationships with more than 60 of the world's leading companies focused on solving clients' most complex challenges and enabling them to shape new markets and drive measurable value. Together, Deloitte and Dataiku help enterprises build reusable AI projects that deliver value at scale. Deloitte's experience in automation, data transformation, integration with multiple complex technologies, optimizing performance, and enabling self-service platforms complement Dataiku's enterprise-ready plug-and-play capabilities for data scientists and everyday business users alike, all on a single platform. "Dataiku's alliance with Deloitte advances the mission of enabling Everyday AI," said David Tharp, SVP Ecosystems and Alliances, Dataiku.

Optimizing the customer experience with better resource allocation is often a critical objective for businesses or governmental agencies. The following case study concerns the Emergency Services Contact Center of a large European metropolitan city. Their objective was to accurately predict the volume of inbound calls to be able to improve agent rostering and ensure optimal response times for callers in serious need of assistance. The solution had to offer high reliability thresholds, as it would be fatal if there were peaks where the supply of operators (police, fire, ambulance, etc.) could not meet the demand for incoming calls. In this article, we will describe the solution used to achieve this purpose, based on real examples applied to the use case.

AI platforms are a boon for businesses who are constantly looking to improve their output. Artificial intelligence platforms allow businesses to maximize their efficiency and provide many benefits such as taking over redundant tasks, providing deeper insight into data for better decision making, providing data management capabilities and more. AI-powered platforms are a boon for businesses that are constantly looking for ways to reduce their output. This article lists the 10 most popular AI platforms in 2022. C3 AI is one of the most popular AI platforms.

December 13, 2021 / By Joy Looney - Dataiku -- The retail and CPG industries have been undeniably rocked by the recent global health crisis. Whether from the bottlenecking of supply chain flows, disruption of demand projections, or other problems, the issues that resulted from this intense instability, along with existing challenges in the sector, have pushed retail and CPG organizations to turn to AI applications for solutions. The next step for retailers is now identifying which specific AI platforms will meet their needs. Here enters Dataiku -- the all-in-one platform that helps retailers with use cases across their value chain, from supply optimization to customer engagement.

Connected multicloud data platform Teradata announced a new set of analytic integration components for the "everyday AI" platform Dataiku. The new Teradata Plugins for Dataiku are designed to enable analytics and data science teams that use Dataiku to implement a wide range of analytic functions within the Teradata Vantage platform. The upgrades will drive agility for analytics and machine learning initiatives, accelerating time-to-value for joint Teradata-Dataiku customers, Teradata said in a written statement. The integration adds to the existing in-database options available when using Dataiku's solution to design, deploy, and manage AI and applications with Teradata. JC Raveneau, senior director of product management at Dataiku, said, "a common challenge is the scale of data preparation and analytics processes that modern AI and machine learning platforms require. The new Vantage Plugins offers Dataiku users the ability to deliver more value from their analytic workflows with in-database processing."