We’ll examine which artificial intelligence platforms surveyed companies considered the best, as highlighted in our AI Software of Choice 2022 report.
Having the right AI software can help businesses make better decisions. Whether you’re a beginner or a professional, the right platform for you is out there.
Now, let’s take a look at the AI platforms that were most popular with this year’s respondents:
- Seldon Deploy
- Google Cloud AI platform
Konduto used its experience in e-Commerce, artificial intelligence, and payment gateways to create an online fraud-fighting platform that uses machine learning and browsing behavior tracking. In 2019, it had 4,000 merchants in Latin America, 175 million processed orders, and prevented $820 million in fraud.
This fraud analysis engine uses machine learning technology to prevent fraud in online shopping. By crossing the information from over 2,000 data points and consulting Konduto’s database, it gives buyers a more confident shopping experience.
The platform analyzes each buyer’s behavior, combing through searches, product views, product comparisons, and more to monitor and identify fraudulent profiles.
ATS Global is an automation, IT enterprise, and quality-focused company. Established in 1986, it has six business activities to support the digital transformation journey. It’s the Independent Solution Provider for smart digital transformation, with expertise in:
- Advanced Planning and Scheduling
- Application Lifecycle Management and CloudNXT
- Automation and IT
- Lean and Six Sigma
- Manufacturing Execution System
- Quality Management
- Smart Manufacturing and Industry 4.0
- Supply Chain Management
As the first-ever digital decision management system powered by explainable AI, Rulex combines data-derived knowledge and human expertise. The platform has a full no-code enterprise-grade software offering that allows business users to interact with developers to optimize, augment, and automate their decision-making processes.
Meta, Facebook’s new corporate name, has an organization that heads up the work on artificial intelligence: Meta AI. It focuses on proprietary AI research, featuring research papers and its own open-source tools. The social platform, Facebook, uses Meta AI’s neural network, image recognition, and more.
This open-sourced software system implements state-of-the-art object detection algorithms. Written in Python and powered by the Caffe2 deep learning framework, it aims to provide a high-performance and high-quality codebase for object detection research.
With Dectectron2, models can be exported to Caffe2 or TorchScript format for deployment. Compared to the first iteration, which had a throughput of 10 img/s, Detectron2 is much faster at 62 img/s. Available on GitHub.
The OpenMMLab team wants to provide advanced research and development models and systems for the industry. They also aim to become a worldwide leader in open-source algorithm platforms for computer vision. Widely known across the globe, OpenMMLab has a wide variety of open-source projects for industrial applications and academic research, offering over 20 open datasets owned by MMLab.
This Python toolbox was built as a codebase for object detection and instance segmentation. MMDetection is modular, built with PyTorch, and is part of the OpenMMLab project. As an object detection toolbox and benchmark, it has inference and training codes and offers weight for more than addition. Available on GitHub.
5. Seldon Deploy
Founded with the aim of accelerating the adoption of machine learning in order to solve some of the most challenging issues in the world, Seldon Technologies Ltd made its first open-source release in 2015. Having won awards in the industry, the company believes machine learning will soon be at the center of every connected business.
Offering governance and oversight for machine learning deployments, Seldon Deploy helps in deploying audited models with Gitops. This enterprise product aims at accelerating deployment management on top of the open-source tools Seldon Alibi, KFserving, and Seldon Core.
- Update models through Canary workflows.
- Guarantee a safe model deployment with Gitops paradigm.
- Audit model predictions with Black Model Explainers.
- Monitor your running models and search response/request logs.
- Deploy machine learning models effortlessly by using Seldon and KFServing open projects.
6. Google Cloud AI platform
As a multinational technology company, Google focuses on search engine technology, artificial intelligence, computer software, cloud computing, quantum computing, and more. Some of Google’s innovations include Sycamore (quantum computing), Google Brain (transformer models), Sidewalk Labs (smart cities), and more.
The Google Cloud AI Platform is a suite of services targeted at building, deploying, and managing machine learning models in the cloud. It’s created for ease of use by data scientists and engineers, helping to streamline workflows. It offers data labeling, BigQuery Datasets, Notebooks, AutoML, training, AI explanations, What-If Tool, Vizier, prediction, and TensorFlow Enterprise services. GitHub repositories are available.
Mainly developed by Facebook’s AI Research lab, now known as Meta AI, PyTorch is an open-source machine learning framework for natural language processing, computer vision, and more. Tesla Autopilot’s deep learning software was built on top of PyTorch. Available on GitHub.
PyTorch was built to be both modular and flexible for research, offering support and stability for production deployment. While its mobile support is still experimental, it supports an end-to-end workflow on iOS and Android, from Python to development. PyTorch offers two high-level features:
- Deep neural networks built on a tape-based automatic differentiation system.
- Tensor computing with a strong acceleration through GPU.
Developed by Guido van Rossum in the late 1980s, Python was created as the successor to the ABC programming language. It was first released in 1991 as Python 0.9.0, with future iterations added at later dates. It’s one of the most popular programming languages.
This high-level, general-purpose programming language highlights code readability with the utilization of significant indentation as its design philosophy. Python is garbage-collected (a form of automatic memory management) and dynamically typed, supporting a wide variety of programming paradigms like object-oriented and functional programming.
How about you, what are your top AI platform picks?
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