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.

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.

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.

"What good is an ML model if it isn't fast? Having machine learning in a company's portfolio used to be an investor magnet. Now, the market is bullish on MLaaS, with a new breed of companies offering machine learning services (libraries/APIs/frameworks) to help other companies get their job done better and faster. According to PwC, AI's potential global economic impact will be worth $15.7 trillion by 2030. And, as interests slowly shift towards MLOps, it is possible that these companies, which promise to scale and accelerate ML deployment, might grab a bigger piece of the pie. Last week, OctoML raised $28 million. The Seattle-based startup offers a machine learning acceleration platform built on top of the open-source Apache TVM compiler framework project. The $28 million Series B funding brings the company's total funding to $47 million. For OctoML's CEO, Luis Ceze, there is still a significant gap between building a model and making it production-ready. Between rapidly evolving ML models, wrote Ceze in a blog post, ML frameworks and a Cambrian explosion of hardware backends makes ML deployment challenging. "It is not easy to make sure your model runs fast enough and to benchmark it across different deployment hardware.

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.

See some shoes you like on a frenemy's Instagram? Search will pull up all the matching images on the web, including from sites that will sell you the same pair. In order to do that, Google's computer vision algorithms had to be trained to extract identifying features like colors, textures, and shapes from a vast catalogue of images. Luis Ceze, a computer scientist at the University of Washington, wants to encode that same process directly in DNA, making the molecules themselves carry out that computer vision work. And he wants to do it using your photos.

Most of us rarely think about it, but when we turn on our smartphones and PCs, we're giving ourselves over to machines that reduce every single task to a series of 1s and 0s. But according to Doug Burger, a researcher with Microsoft's Extreme Computing Group, this may be coming to an end. Burger thinks we could be entering a new era where we don't need digital accuracy. To hear him tell it, the age of really big data may well be an age of slightly less-accurate computing. We could drop the digital straightjacket and write software that's comfortable working on hardware that sometimes makes errors.