A group of 20-somethings tried to convince us they weren't in the past few years, to varying degrees of success. And now a University of Washington professor wants us to believe that MLOps isn't real, either. What is the world coming to? "MLOps is NOT real," Luis Ceze, a professor in the UW computer science and engineering department, declared in a statement. Ceze is also the CEO and co-founder of OctoML, the Seattle, Washington company that is commercializing Apache TVM, the open source tool for automating the deployment of machine learning models to a variety of platforms, including those running atop GPUs, CPUs, FPGAs, and other types of processors.

Hosted for the global cloud computing community, Amazon Web Services' re:Invent 2021 brought together developers, engineers, IT executives and the technical decision-makers that are transforming how the world around us operates. The early stages of IT infrastructure were inflexible and expensive, but this year's conference brought to light the next shift in the digital journey that highlights the cloud's leading role as an enabler in the way that businesses function with machine learning (ML) and artificial intelligence (AI). In this on-the-show-floor video from the event, we looked at the three areas that are reshaping business processes and environments -- from the intelligent applications that embed AI/ML and take advantage of data, and the system of enablers that allow them to reach scale to the chips that power them. We spoke with Tom Trahan, vice president of business development at CircleCI, Matt McIlwain, managing director at Madrona Venture Group, and Luis Ceze, CEO at OctoML. TNS Publisher Alex Williams hosted these conversations.

In recent years, artificial intelligence programs have been prompting changes in computer chip designs, and novel computers have made new kinds of neural networks in AI possible. There is a powerful feedback loop going on. In the center of that loop sits software technology that converts neural net programs to run on novel hardware. And at the center of that sits a recent open-source project gaining momentum. Apache TVM is a compiler that operates differently from other compilers.

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.

OctoML Inc., a fresh-faced machine learning startup recently spun off from the University of Washington, today announced that it has raised $3.9 million in funding to tackle the complexity of deploying artificial intelligence software. Setting up an AI model on a hardware system is much different than the typical application install. To maximize an algorithm's performance and power-efficiency, engineers must painstakingly optimize their code for the specific chip powering the host system. OctoML is looking to make the task less resource-intensive. The startup's 10-person team, led by Chief Executive Officer and University of Washington professor Luis Ceze (pictured, second from left), has developed an open-source toolkit called Apache TVM that can automate the model deployment process.