Nixtla has partnered with Microsoft to bring time-series forecasting to Azure

We’re beyond thrilled to share that we have partnered with Microsoft to bring time-series forecasting to Models as a Service (MaaS) in Azure AI. 

We’re bringing our foundational time-series model TimeGPT, to Azure’s enterprise-grade AI solution, optimizing it for Azure infrastructure as TimeGEN-1. ‍

Forecasting is an essential part of planning, but it's often a hard task that requires many hours of development, maintenance, and testing, consuming valuable resources. Leveraging the latest technology on generative pre-trained models can help organizations easily improve or deploy new forecasting pipelines.

Nixtla is the first to do for time series models what OpenAI, Mistral and others have done for language models – making them accessible to anyone

Get started with TimeGEN-1 on Azure!

TimeGEN-1 is the first generative pre-trained model for time series data that can produce instant accurate predictions for new time series without training. It can be easily deployed across a plethora of tasks, without prior ML knowledge or specialized hardware. We’ve seen how TimeGEN-1 saves considerable human and computation resources, with pipelines that can be deployed in a few lines of code and run in seconds, producing comparable or better predictions than traditional forecasting. 

"We are incredibly excited to collaborate with Nixtla to integrate TimeGEN-1 into our Azure AI platform,” said Asha Sharma, Corporate Vice President, Azure AI Platform at Microsoft. “With TimeGEN-1, companies can deploy powerful time-series forecasting models effortlessly, enhancing operational efficiency and accuracy while significantly reducing costs. This is a prime example of how Azure AI continues to drive innovation, making advanced AI accessible to every enterprise and developer."

With Nixtla APIs and hosted fine-tuning on Models as a Service (MaaS) in Azure AI, you can forget the hassle of traditional time series analysis.

Accurate: Nixtla TimeGEN-1 produces state-of-the-art forecasts and anomaly detection, without the need of training and developing complex pipelines.

Self-hosted: Easily deployed through Azure AI Studio and Azure Machine Learning reducing the need to attain and manage GPUs, maintaining your sensitive data in your infrastructure. 

Cost-effective: The model is provided through pay-as-you-go inference APIs and it is more cost-effective than deploying dedicated instances for hosting forecasting pipelines.

See more on Microsoft's blog.

What our customers are saying

TimeGEN-1 is already revolutionizing the way businesses across various industries handle their predictive analytics. From enhancing demand forecasting in retail and manufacturing to optimizing financial predictions in investment research, TimeGEN-1 on Azure empowers organizations to achieve unparalleled accuracy and efficiency. MindsDB, a leading AI Startup, leverages TimeGEN-1 to enable their customers to perform rapid and precise forecasting across diverse applications such as anomaly detection and large scale predictions, drastically reducing complexity and time investment. Similarly, OpenBB Terminal Pro integrates TimeGEN-1 to allow financial analysts and quants to effortlessly generate forecasts from proprietary datasets, thus democratizing access to advanced forecasting technologies.

In the life sciences sector, RoadMap Technologies incorporates TimeGEN-1 within its TrailBlazer platform, providing users with robust and integrated forecasting solutions that quantify uncertainty and enhance decision-making. These diverse applications underscore the versatility and transformative potential of TimeGEN-1, making state-of-the-art forecasting accessible to companies of all sizes and across various sectors.

“As the leading predictive analytics models in the market, TimeGEN-1 offers advanced capabilities that provide a variety of unique features, making it a powerful asset for managing complex forecasting scenarios. Integrating TimeGEN-1 with MindsDB creates an impactful combination for predictive insights directly within business databases, so organizations can react swiftly to a rapidly evolving global market.”
‍-Jorge Torres, CEO, MindsDB

“At Bridgestone we value customers, and to make sure that we provide our customers tires at the right time we need to optimize the upstream. To do so we are working on state-of-the-art forecasting models. In this regards we value our partnership with Nixtla and Microsoft.”
-Onkar Ambekar, Director AI & Analytics (EMEA & Americas), Bridgestone.

"The OpenBB Terminal Pro allows analysts and quants to bring any dataset that they have access to (whether it’s a static file, an API, lives in a database or data warehouses). Combining this feature with the capability to leverage the first pre-trained foundation model optimized for time series forecasting, TimeGEN-1, on top of any financial time series is technology that hasn’t been available to the investment research world.
One of the most loved features of the OpenBB Terminal Pro, is the possibility to highlight data on a table and create a chart directly from it. With TimeGEN-1 from Nixtla, we allow users to go from table, to line chart, to an accurate forecasting. On any dataset and in a few clicks. This is truly democratizing access to state-of-the-art models - which previously wasn’t  accessible to most financial firms."
-‍Didier Lopes, CEO, OpenBB

"TimeGEN-1, the first pre-trained foundational model optimized for time-series forecasting, was an ideal solution for RoadMap Technologies. With TimeGEN-1 on Azure, RoadMap provides customers an integrated solution for their forecasting needs and improves quantifying uncertainty with TimeGEN-1's conformal prediction capabilities. The speed and accuracy of TimeGEN-1 within RoadMap TrailBlazer allows companies to build predictive models of key performance indicators for customers, demand, revenue, and sales."
-Dom Pizzano, Director of Technology Solutions, RoadMap Technologies

About TimeGEN-1 Generative AI: 

TimeGEN-1 is a generative pre-trained forecasting model for time series data that can produce accurate forecasts for new time series without training. It can be used across a plethora of tasks including demand forecasting, anomaly detection, financial forecasting and more. 

The TimeGEN-1 model “reads” time series data much like the way humans read a sentence – from left to right. It looks at windows of past data, which we can think of as “tokens”, and predicts what comes next. This prediction is based on patterns the model identifies in past data and extrapolates into the future. 

The API provides an interface to TimeGEN-1, allowing users to leverage its forecasting capabilities to predict future events; can also be used for other time series-related tasks, such as what-if scenarios and anomaly detection.

About Azure AI Models-as-a-Service (MaaS)

TimeGen-1 can be deployed as a service with pay-as-you-go through Azure AI, providing a way to consume the model as an API without hosting them on your subscription, while keeping the enterprise security and compliance organizations need. This deployment option doesn't require quota from your subscription.

Traditionally, virtual machines (VMs) with high-end GPUs meant for hosting large models are capable of generating thousands of tokens per second but can be prohibitively expensive for dev-test cycles. MaaS eliminates the need to host models in dedicated VMs, especially for developers who don’t need high throughput during the dev-test phase of their projects. With pay-as-you-go inference APIs that are billed based on input and output tokens used, MaaS makes getting started easy and pricing attractive for AI projects. Additionally, TimeGen-1 can be fine-tuned with your specific data through hosted fine-tuning to enhance prediction accuracy for tailored scenarios, allowing even smaller models to deliver superior performance for your needs at a fraction of the cost of the larger models.

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