OctoML says it has ended its business relationship with Civitai days after an investigation by 404 Media revealed the text-to-image platform was being used to generate images that "could be categorized as child pornography." While OctoML initially indicated it would continue working with Civitai and introduced new measures to curb the creation of harmful images, 404 Media reported on Saturday that it has now decided to cut ties with the platform altogether. According to 404 Media's December 5 report, internal communications showed that OctoML was aware some Civitai users were creating sexually explicit material that included nonconsensual images of real people and pornographic depictions of children. In a followup report this weekend, the publication noted that OctoML rolled out a filter to block the generation of all NSFW content on Civitai before announcing its decision to pull out. Civitai also added new moderation methods in response to the investigation earlier this week, including a mandatory embedding called Civitai Safe Helper (Minor) that bars the model from generating images of children if "a mature theme or keyword is detected," according to 404.

Follow along with our new YOLOv5 deployment tutorial to power your next object detection application. Or, watch this tutorial video by Smitha Kolan on how to deploy YOLOV5 in under 15 minutes using the OctoML CLI. Today, we are excited to announce the results of our collaboration with Ultralytics to deploy the YOLOv5 models to over 100 CPU and GPU hardware targets in AWS, Azure and GCP. Our engineering work with Ultralytics unlocks the ability to deploy YOLOv5 models on hardware from Intel, NVIDIA, Arm and AWS, with minimal effort and cost. In this blog, I'll show you how simple it is to achieve hardware independence and cost savings across multiple clouds.

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.

OctoML Inc. today introduced a new release of its artificial intelligence platform that includes a collection of highly efficient neural networks. The neural networks are optimized versions of popular open-source AI models that OctoML has fine-tuned. According to the startup, the optimized versions cost less to run than the original AI models and require less power as well. The new platform release also introduces other improvements, including support for more machine learning development tools. Seattle-based OctoML launched in 2019 and is backed by more than $130 million in funding.

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.

Artificial intelligence and machine learning have set the record of receiving funding and investment worth millions of dollars in 2021. Investors are eyeing multiple start-ups for providing machine learning funding as well as machine learning investment for lucrative ML models for the betterment of society. It has been observed that these ML funding and ML investments have started transforming the tech-driven market across the world. Let's explore some of the top machine learning funding and investment in Q3 and Q4 in 2021. Landing AI rose US$57 million from Series A funding in November 2021 as one of the top machine learning start-ups in 2021.

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.