Domino Enterprise MLOps Platform vs. Rational BI Comparison Chart

Qloo is the “Cultural AI”, decoding and predicting consumer taste across the globe. A privacy-first API that predicts global consumer preferences and catalogs hundreds of millions of cultural entities. Through our API, we provide contextualized personalization and insights based on a deep understanding of consumer behavior and more than 575 million people, places, and things. Our technology empowers you to look beyond trends and uncover the connections behind people’s tastes in the world around them. Look up entities in our vast library spanning categories like brands, music, film, fashion, travel destinations, and notable people. Results are delivered within milliseconds and can be weighted by factors such as regionalization and real-time popularity. Used by companies who want to incorporate best-in-class data in their consumer experiences. Our flagship recommendation API delivers results based on demographics, preferences, cultural entities, metadata, and geolocational factors.

Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection.

Fraud.net delivers the world’s most advanced infrastructure for fraud management – powered by a sophisticated collective intelligence network, world-class artificial intelligence, and a modern, cloud-based platform that helps you: * Unify fraud data from any source with a single connection * Detect fraudulent activity for 99.5%+ transactions in real-time * Optimize fraud management by uncovering hidden insights in terabytes of data Recognized in Gartner’s Market Guide for Online Fraud Detection, Fraud.net is a real-time, enterprise-strength fraud prevention and analytics solution organized around its business customers’ needs. Through a single point of command, it unifies and analyzes data from disparate systems and sources, tracks digital identities and behaviors, and then deploys the latest tools and technologies to stamp out fraudulent activity while allowing good transactions to sail through. Contact us today for a free trial.

Product recommendations tailored to your customers' preferences in a fully-managed service. BytePlus Recommend draws from our expertise in machine learning to offer dynamic and targeted recommendations. Our industry-leading team has a track record of delivering recommendations for some of the world’s most popular platforms. You can learn from the data of your users to engage them better, and provide personalized suggestions based on granular customer behavior. BytePlus Recommend is easy to use — leveraging your existing infrastructure as well as automating the machine learning workflow. BytePlus Recommend leverages our research in machine learning to deliver personalized recommendations tailored to your audience’s preferences. Our experienced and talented algorithm team provides customized strategies that adapt to evolving business needs and goals. Our pricing is based on results from A/B testing. Optimization goals are determined based on business demands.

IBM SPSS Statistics software is used by a variety of customers to solve industry-specific business issues to drive quality decision-making. Advanced statistical procedures and visualization can provide a robust, user friendly and an integrated platform to understand your data and solve complex business and research problems. • Addresses all facets of the analytical process from data preparation and management to analysis and reporting • Provides tailored functionality and customizable interfaces for different skill levels and functional responsibilities • Delivers graphs and presentation-ready reports to easily communicate results Organizations of all types have relied on proven IBM SPSS Statistics technology to increase revenue, outmaneuver competitors, conduct research, and data driven decision-making.

BigQuery is a serverless, multicloud data warehouse that simplifies the process of working with all types of data so you can focus on getting valuable business insights quickly. At the core of Google’s data cloud, BigQuery allows you to simplify data integration, cost effectively and securely scale analytics, share rich data experiences with built-in business intelligence, and train and deploy ML models with a simple SQL interface, helping to make your organization’s operations more data-driven.

Domo puts data to work for everyone so they can multiply their impact on the business. Our cloud-native data experience platform goes beyond traditional business intelligence and analytics, making data visible and actionable with user-friendly dashboards and apps. Underpinned by a secure data foundation that connects with existing cloud and legacy systems, Domo helps companies optimize critical business processes at scale and in record time to spark the bold curiosity that powers exponential business results.

Derive insights from your images in the cloud or at the edge with AutoML Vision or use pre-trained Vision API models to detect emotion, understand text, and more. Google Cloud offers two computer vision products that use machine learning to help you understand your images with industry-leading prediction accuracy. Automate the training of your own custom machine learning models. Simply upload images and train custom image models with AutoML Vision’s easy-to-use graphical interface; optimize your models for accuracy, latency, and size; and export them to your application in the cloud, or to an array of devices at the edge. Google Cloud’s Vision API offers powerful pre-trained machine learning models through REST and RPC APIs. Assign labels to images and quickly classify them into millions of predefined categories. Detect objects and faces, read printed and handwritten text, and build valuable metadata into your image catalog.

(Bank CFO) “I don’t have confidence and trust in our data. We keep discovering hidden risks”. Since 70% of data initiatives fail due to unreliable data (Gartner research), are you risking your reputation by trusting the accuracy of your data that you share with your business stakeholders and partners? Data Trust Scores must be measured in Data Lakes, warehouses, and throughout the pipeline, to ensure the data is trustworthy and fit for use. It typically takes 4-6 weeks of manual effort just to set a file or table for validation. Then, the rules have to be constantly updated as the data evolves. The only scalable option is to automate data validation rules discovery and rules maintenance. DataBuck is an autonomous, self-learning, Data Observability, Quality, Trustability and Data Matching tool. It reduces effort by 90% and errors by 70%. "What took my team of 10 Engineers 2 years to do, DataBuck could complete it in less than 8 hours." (VP, Enterprise Data Office, a US bank)

Dialogflow from Google Cloud is a natural language understanding platform that makes it easy to design and integrate a conversational user interface into your mobile app, web application, device, bot, interactive voice response system, and so on. Using Dialogflow, you can provide new and engaging ways for users to interact with your product. Dialogflow can analyze multiple types of input from your customers, including text or audio inputs (like from a phone or voice recording). It can also respond to your customers in a couple of ways, either through text or with synthetic speech. Dialogflow CX and ES provide virtual agent services for chatbots and contact centers. If you have a contact center that employs human agents, you can use Agent Assist to help your human agents. Agent Assist provides real-time suggestions for human agents while they are in conversations with end-user customers.