Giskard vs. MapleMBSE Comparison Chart

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.

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.

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.

Cycloid Sustainable Platform Engineering is a self-service portal that helps you streamline your software delivery, reduce the cognitive load of your engineering teams, and promote Green IT practices. Step 1. Modernize your infrastructure in sophisticated service catalog supported by Infra Import, a Terraform generator in a full GitOps-first approach. Step 2. Scale the adoption of your platform and lighten workload for end-users and DevOps with a UX-strong internal developer platform. Your tools, automation, and cloud will be accessible without expert knowledge while still keeping control and best practices. Step 3. Allow your teams to continuously optimize their projects with a 360° overview of CI/CD pipelines, automation, documentation, KPI’s, FinOps and GreenOps. With Cycloid, you will enable a future where technology and sustainability can coexist harmoniously, leaving a lasting positive legacy.

(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)

Testsigma is a low-code end-to-end software testing platform that works out of the box. It's built to enable modern Product teams to collaboratively plan, develop, execute, analyze, debug, and report on their automated testing—for websites, native Android and iOS apps, and APIs. Testsigma test scripts are code-agnostic. While the platform itself is built with Java, you can write test scripts in simple English or use the Test Recorder to generate airtight test scripts for websites and native mobile apps. You can also use the test recorder on your own device farm/grid or on Testsigma's built-in device lab on the cloud. The platform has built-in visual testing, parametrized or data-driven testing, 2FA testing, and more advanced features for easy test automation. Our AI automatically fixes unstable elements and test steps, identifies and isolates regression-affected scripts, and provides suggestions to help you find and fix test failures.

With customers across 70+ countries, organizations of all sizes rely on Device42 as the most trusted, advanced, and complete full-stack agentless discovery and dependency mapping platform for Hybrid IT. With access to information that perfectly mirrors the reality of what is on the network, IT teams are able to run their operations more efficiently, solve problems faster, migrate and modernize with ease, and achieve compliance with flying colors. Device42 continuously discovers, maps, and optimizes infrastructure and applications across data centers and cloud, while intelligently grouping workloads by application affinities and other resource formats that provide a clear view of what is connected to the environment at any given time. As part of the Freshworks family, we are committed to, and you should expect us to provide even better solutions and continued support for our global customers and partners, just as we always have.

The training data platform for AI teams. A machine learning model is only as good as its training data. Labelbox is an end-to-end platform to create and manage high-quality training data all in one place, while supporting your production pipeline with powerful APIs. Powerful image labeling tool for image classification, object detection and segmentation. When every pixel matters, you need accurate and intuitive image segmentation tools. Customize the tools to support your specific use case, including instances, custom attributes and much more. Performant video labeling editor for cutting-edge computer vision. Label directly on the video up to 30 FPS with frame level. Additionally, Labelbox provides per frame label feature analytics enabling you to create better models faster. Creating training data for natural language intelligence has never been easier. Label text strings, conversations, paragraphs, and documents with fast & customizable classification.

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.

Google Cloud’s Speech API processes more than 1 billion voice minutes per month with close to human levels of understanding for many commonly spoken languages. Powered by the best of Google's AI research and technology, Google Cloud's Speech-to-Text API helps you accurately transcribe speech into text in 73 languages and 137 different local variants. Leverage Google’s most advanced deep learning neural network algorithms for automatic speech recognition (ASR) and deploy ASR wherever you need it, whether in the cloud with the API, on-premises with Speech-to-Text On-Prem, or locally on any device with Speech On-Device.