Machine learning tools help enterprises to understand the trends in customer behavior and business operational patterns, as well as support the development of new products. Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. Big Squid's Kraken AutoML is an automated machine learning platform for building and deploying machine learning models for business analytics – including within existing analytics stacks – without the need to write code. Kraken's no-code capabilities simplify the adoption of machine learning and AI, helping data analysts, data scientists, data engineers, and business users collaborate on machine learning and predictive analytics projects.

If you're spending months hand-tuning your machine learning model to run well on a particular type of processor, you might be interested in a startup called OctoML, which recently raised $28 million to bring its innovative "Octomizer" to market. Octomizer is the commercial version of Apache TVM, an open source compiler that was created in Professor Luiz Ceze's research project in the Computer Science Department at the University of Washington. Datanami recently caught up with the professor–who is also the CEO of OctoML–to learn about the state of machine learning model compilation in a rapidly changing hardware world. According to Ceze, there is big gap in the MLOps workflow between the completion of the machine learning model by the data scientist or machine learning engineer, and deployment of that model into the real world. Quite often, the services of a software engineer are required to convert the ML model, which is often written in Python using one of the popular frameworks like TensorFlow or PyTorch, into highly optimized C or C that can run on a particular processor.

If you're spending months hand-tuning your machine learning model to run well on a particular type of processor, you might be interested in a startup called OctoML, which recently raised $28 million to bring its innovative "Octomizer" to market. Octomizer is the commercial version of Apache TVM, an open source compiler that was created in Professor Luiz Ceze's research project in the Computer Science Department at the University of Washington. Datanami recently caught up with the professor–who is also the CEO of OctoML–to learn about the state of machine learning model compilation in a rapidly changing hardware world. According to Ceze, there is big gap in the MLOps workflow between the completion of the machine learning model by the data scientist or machine learning engineer, and deployment of that model into the real world. Quite often, the services of a software engineer are required to convert the ML model, which is often written in Python using one of the popular frameworks like TensorFlow or PyTorch, into highly optimized C or C that can run on a particular processor.

New funding: Seattle-based startup OctoML raised a $28 million Series B round. The University of Washington spinout aims to help companies deploy machine learning models on various hardware configurations. The technology: OctoML is led by the creators of Apache TVM, an open source "deep learning compiler stack" that started as a research project at the UW's computer science school. The idea is to reduce the amount of cost and time it takes companies to develop and deploy deep learning software for specific hardware such as phones, cars, health devices, etc. -- "using ML to optimize ML," as OctoML CEO Luis Ceze explains. Traction: OctoML is working with Qualcomm, Microsoft, AMD, Bosch, and many others.