You wouldn't conceive of setting up your own SMS messaging stack across 193 countries and god knows how many telecom carriers in a world where Twilio exists. Machine learning (ML) is in a similar scenario; why would you waste time putting together a whole infrastructure unless Machine Learning is key to your program -- which it probably isn't? Slai is claiming to have laid the foundation to a developer-first machine learning platform to address this specific challenge. It gives developers the tools they need to release machine-learning apps swiftly. The company's offering claims to focus on allowing developers to focus on the machine learning models rather than all of the other nonsense that wastes time but doesn't directly add to the application.

Oracle has announced availability of Oracle Cloud Infrastructure (OCI) AI services, a collection of services that make it easier for developers to apply AI services to their applications without requiring data science expertise. The new OCI AI services give developers the choice of leveraging out-of-the-box models that have been pretrained on business-oriented data or custom training the services based on their organization's own data. The six new services help developers with a range of complex tasks from language to computer vision, and time-series forecasts. Companies today need AI to accelerate innovation, assess business conditions, and deliver new customer experiences. However, they frequently run into implementation issues ranging from a scarcity of data science expertise, difficulties in training models on relevant business data to getting their platform to work in a live environment or breaking down data silos.

Robotics has become an essential part of many heavy sectors including manufacturing, automobile, and aerospace etc. Plus, we have seen in COVID-19 pandemic that robots are being used to take care of patients in hospitals or serve consumers at hotels/restaurants. These instances are indicating that existence of robots is not our future anymore; it is our present. To make robots a part of our everyday life, its developers require an accessible platform upon which they can design the robots. Misty Robotics is a company behind Misty, a robot platform purpose-built for developers. Misty Robotics was facing challenges to co-create an open-platform robot that will be able to perform human-like tasks with a personality as well as perception abilities.

In application-development circles, containers have gotten a lot of attention in recent years -- for many good reasons. To name a few advantages, containers simplify and accelerate the process of building and isolating applications. They are lightweight and come with low overhead. And they enable easier application sharing and reproducibility, because the container image includes both the application and its development environment. Especially when it comes to deep learning (DL) frameworks, containerization is now rising in importance.

Federighi announced new APIs that help coders building apps for Apple devices do things like recognize faces or animals in photos, or parse the meaning of text. The reasoning goes that if you can make your phones, operating system, or cloud the best place to build smart new software that leverages AI, more users and revenue will follow. For example, Federighi boasted that Apple's new tools help developers run machine learning on data without it having to leave a person's device, giving performance and privacy benefits. A company that needs to run image recognition inside apps on both Apple and Android devices might prefer to use Google's cloud machine learning APIs instead, for example.

Federighi announced new APIs that help coders building apps for Apple devices do things like recognize faces or animals in photos, or parse the meaning of text. The reasoning goes that if you can make your phones, operating system, or cloud the best place to build smart new software that leverages AI, more users and revenue will follow. For example, Federighi boasted that Apple's new tools help developers run machine learning on data without it having to leave a person's device, giving performance and privacy benefits. A company that needs to run image recognition inside apps on both Apple and Android devices might prefer to use Google's cloud machine learning APIs instead, for example.

Real and personal property is a basic delineation in English common law that corresponds roughly to the differences between immovable and movable objects. Interests in land and fixtures, such as permanent buildings, are classified as real property interests. The real estate operations industry consists of companies engaged in developing, renting, leasing, and managing residential and commercial property interests. The industry includes real estate brokerage and agent services, real estate appraisal services, and consulting services. The real estate operations industry excludes real estate investment trusts (REITs).

Dato, creator of the popular machine learning platform GraphLab Create, announced tools to give scientists, developers and users confidence in machine learning models and predictions. Dato is the first machine learning company to address the industry need for confidence in models and predictions. Demand for machine learning has spread to large enterprise organizations," said Carlos Guestrin, Dato CEO and Amazon Professor of Machine Learning at University of Washington. "We have more than 80 commercial customers. The need for trust in models and predictions is an indicator of market adoption among established companies." Dato introduced tools within GraphLab Create and Dato Predictive Services to build trust and confidence in machine learning by making it easy to evaluate, explore, and explain models and predictions. With Dato's machine learning platform, companies can gain trust and confidence in the models and predictions behind their core business applications. At Capital One, we exhaustively work on model robustness and validation," said Brendan Herger, Capital One Data Innovation Lab Data Scientist.

On a wet and windy day in October 2014, Team Seldon was sitting on Brighton Beach, discussing the bigger picture of what we could achieve. We had come a long way over the previous three years; every month we were serving content recommendations to over a hundreds million people. However, we believed that if we continued to ship a black box solution we would increasingly face obstacles to user adoption by enterprises. Machine learning technology was becoming increasingly commoditized, and new applications were, and continue to be, both widely developed and adopted. Through the mist that had settled over Brighton Beach the bigger picture of what we could achieve became clear -- democratising machine learning.

SEATTLE--(BUSINESS WIRE)--Dato, creator of the popular machine learning platform GraphLab Create (GLC), announced today tools to give scientists, developers and users confidence in machine learning models and predictions. Dato is the first machine learning company to address the industry need for confidence in models and predictions. "Demand for machine learning has spread to large enterprise organizations," said Carlos Guestrin, Dato CEO and Amazon Professor of Machine Learning at University of Washington. "We have more than 80 commercial customers. The need for trust in models and predictions is an indicator of market adoption among established companies."