AI & Machine Learning Operationalization reviews by real, verified users. Find unbiased ratings on user satisfaction, features, and price based on the most reviews available anywhere.
AI & machine learning operationalization (MLOps) software allows users to manage and monitor machine learning models as they are integrated into business applications. In addition, many of these tools facilitate the deployment of models. With these tools, businesses can take machine learning models and algorithms built by data scientists and machine learning developers and put them into action. The software provides a way to automate deployment, monitor the health, performance, and accuracy of models, and iterate on those models. Some of these products provide tools for doing this in a collaborative manner. This enables businesses to scale machine learning across the entire company and make a tangible business impact.
Additionally, these products may provide security, provisioning, and governing capabilities to ensure that only those authorized to make version changes or deployment adjustments can do so. Some AI & machine learning operationalization solutions may offer a way to manage all machine learning models across the entire business, in a single location. Although similar to data science and machine learning platforms, this software differs inasmuch as it is focused on the maintenance and monitoring of models, as opposed to deployment.
Finally, these tools are usually language agnostic so that no matter how an algorithm is built, it can be successfully deployed. However, some may focus specifically on languages like R or Python, among others. Some of these products are dedicated to tracking machine learning experiments to better understand the performance of models. In addition, some of these products provide the ability to augment one’s training dataset, in order to improve the model training.
To qualify for inclusion in the AI & Machine Learning Operationalization category, a product must:
Algorithmia wants every organization to achieve its full potential through the use of artificial intelligence and machine learning. We understand that productionizing ML requires a different set of IT architecture and workflows than traditional programming, and we provide a frictionless route to deployment, serving, and management of your models. We host an algorithm marketplace with more than 8,000 models, and we power ML workloads for Fortune 100 companies, intelligence agencies, and private
Cortex Certifai generates the first-ever composite trust score, the AI Trust Index, that measures data and model risks related to Performance, Data Quality, Robustness, Explainability, Fairness, and Compliance. Certifai can be applied to any black-box model including machine learning models, statistical models, business rules, and other predictive models and works with a variety of input data.
Spell is a platform that makes Machine Learning and Deep Learning accessible to teams of any size. An end-to-end data science and machine learning platform, Spell’s infrastructure and tools allow anyone to prepare, train, deploy, and manage machine learning projects. Accelerate Productivity Don’t be held back by infrastructure. With Spell the best CPU and GPU machine types and frameworks for experiments are accessible in seconds. Save time on setup and management with simple command line tools
If you are a data scientist or engineer, at some point you want to bring your algorithm to production. And that means installing libraries, managing dependencies, deploying your scrips and models, versioning, serving, and running out of compute. Let’s be honest: deployment is hard. The tools we use are not as helpful as they could be, because they are not designed for our specific needs. And we lose ourselves in time-consuming model deployments and infrastructure management. That is not what w
Valohai is the only machine learning platform built for private installations with company’s intellectual property’s safety at the core. Boost your data scientists’ productivity by letting them concentrate on model building while Valohai automates your MLOps. Build a regulatory compliant audit-trail from experiment to inference, with Valohai’s automatic version control. Valohai comes with integrations to your existing tools, from TensorFlow to Jupyter notebooks and active directory. And being
Abzu was born from a desire to challenge the fundamental assumptions of contemporary AI, believing that the future of AI lies in the more lifelike simulation of self-organizing systems. If it’s AI, it’s Abzu. Abzu’s pioneering machine learning technology is inspired by quantum mechanics and neural networks, resulting in a transparent and trustworthy AI. Easy-to-interpret models are trained in Abzu’s proprietary QLattice via an innovative process called Recombination Learning, resulting in expla
DarwinAI, an explainable AI company, enables enterprises to build AI they can trust. Founded by renowned academics at the University of Waterloo, DarwinAI’s Generative Synthesis technology makes explainability real, allowing developers to understand, interpret and quantify the inner workings of a deep neural network. Based on years of distinguished scholarship, the company’s patented explainability technology accelerates advanced deep learning design and unlocks new possibilities for the commerc
Determined AI takes a pragmatic, results-driven approach to deep learning, with a goal of dramatically improving the productivity of deep learning developers. Its integrated AutoML platform simplifies the entire deep learning workflow from data management to model training and deployment.
Devo unlocks the full value of machine data for the world’s most instrumented enterprises by putting more data to work now. With Devo, IT executives finally realize the transformational promise of machine data to drive breakthrough projects that move the entire business forward. Born for today’s fully instrumented world, the Devo platform is purpose-built for both the sheer volume of data generated today, and the crushing demands of automation and the millions of algorithms that need to consume
We provide bank and insurance sales and customer engagement teams with AI based recommendations that generate new sales opportunities and engage better customers Financial services business teams trust DreamQuark Explainable AI to increase their revenues.
dunnhumby Model Lab is an application that provides automated pipelines for deploying machine learning algorithms. When developing models, data scientists go through many steps that are often repetitive and time consuming. Model Lab automates most of the time-consuming steps, allowing data scientists to focus on the modelling phase that delivers value. The product provides automated hyper-parameters tuning, allowing the users to focus on exploring many different algorithms. Thanks to our paral
ExB offers an AI and ML Driven Cognitive Process Automation platform that allows insurance companies to convert any form of text into actionable information and insights for input management and process automation. Insurers can implement ready-to-use pre-trained policy management, claims management, report mining, and invoice assessment modules, request us to train ad-hoc models for their unique workflows, or directly utilize our Cognitive Workbench to independently create and train any sort of
Hopsworks is an open-source Enterprise platform for the development and operation of Machine Learning (ML) pipelines at scale, based around the industrys first Feature Store for ML. You can easily progress from data exploration and model development in Python using Jupyter notebooks and conda to running production quality end-to-end ML pipelines, without having to learn how to manage a Kubernetes cluster.
IBM Spectrum Conductor Deep Learning Impact is add-on software to IBM Spectrum Conductor. It enables you to build a deep learning environment that allows data scientists to focus on training, tuning and deploying models into production. Quickly get started working your data for deep learning, avoid highly manual and repetitive steps and bypass the need for specialized domain knowledge. The solution deploys with simple software downloads that give data scientists everything they need to build a d
Testing AI/ML systems requires domain knowledge. At Payatu, our AI/ML domain experts have orchestrated ways to help you secure your intelligent application against esoteric and potentially severe security and privacy threats. ML Security assessment coverage 1)Understanding the Application a)Use-case b)Product Capabilities c)Implementations 2)Attack Surface Identification a)Understanding the ML Pipeline b)Gather Test Cases If Any 3)Threat Modeling a)Actors and Entity Boundaries b