Any organisation involved with deploying machine learning models to production knows it comes with its share of business and technical challenges and will typically look to solve'some' of those challenges by using a Machine Learning Platform complemented with some MLOps processes to increase maturity and governance in your team. For organisations running multiple models in production and looking to adopt an ML platform they'll typically either build an end-to-end ML platform in-house (Uber, Airbnb, Facebook Learner, Google TFX etc), or buy. In this article I am going to compare some ML Platforms which you can buy. You should always answer another question first. "What problems are you trying to solve?".
New York, New York--(Newsfile Corp. - March 31, 2020) - Dell EMC, a leading provider of full-stack solutions for data science teams, and Comet, the industry-leading meta machine learning experimentation platform, announced a collaboration with a reference architecture for data science teams looking to harness the power of the Dell EMC infrastructure in tandem with Comet's meta machine learning platform. With Dell EMC PowerEdge reference architectures, organizations can deploy artificial intelligence workload-optimized rack systems approximately 6-12 months faster than it would have taken to design the correct configurations and deploy the solution. Organizations can now rely on architectures that are tested and validated by our Dell engineers and know that services are available when and where you need them. "Orchestrating and managing the stack for enterprise data science teams is a huge pain point for many of our customers," said Gideon Mendels, Co-founder/CEO, Comet. "Dell EMC's Kubeflow and Kubernetes solutions are best-in-class and an excellent choice for any data science team looking to build a robust and scalable ML platform."
The way we work has changed and it's continuing to change. People are working remotely while being part of their team irrespective of the location. With this change, traditional training methods being restrictive and costly have become less relevant. One of the challenges faced by teachers is to provide customized learning catering to the needs of every student. As different students have different requirements, even teaching one student is an arduous task as the teacher is challenged to find the right curriculum to meet their requirements.
This Brisbane AI startup claims to be able to predict ad creative success on various platforms with 82.4% certainty. Junction AI, artificial intelligence start-up coming out of Brisbane and Austin, Texas claims to have developed a proprietary machine learning platform that takes the guesswork out of selecting creative and copy for Google Ads, social media ads and web promotions. Ostensibly, marketers will be able to drag and drop their images and text onto the Junction AI dashboard and receive a percentage score of the probability that their promotion will convert a certain target audience. The startup says marketers, agencies and business owners can expect to predict the success of digital advertising with a confidence interval of 82.4%. The platform is currently in pre-release testing and proof trials with 12 agencies and brands in the US and Canada, with tests in the Australian market to begin soon.