More than a trillion dollars are lost every year due to system failures. To resolve them, engineers must troubleshoot outages quickly. An important task in incident response involves analyzing observability metrics, or time series data that snapshot the health of software systems. For example, an engineer for a service may use Datadog to answer questions like "When did latency start increasing?" and "What metrics outside of latency are also behaving abnormally?" to localize the root cause of the anomalous behavior. These time series question-answering (TSQA) tasks are essential for engineers, and present challenging and necessary tasks for SRE models and agents to perform.

This technical report describes the Time Series Optimized Transformer for Observability (Toto), a new state of the art foundation model for time series forecasting developed by Datadog. In addition to advancing the state of the art on generalized time series benchmarks in domains such as electricity and weather, this model is the first general-purpose time series forecasting foundation model to be specifically tuned for observability metrics. Toto was trained on a dataset of one trillion time series data points, the largest among all currently published time series foundation models. Alongside publicly available time series datasets, 75% of the data used to train Toto consists of fully anonymous numerical metric data points from the Datadog platform. In our experiments, Toto outperforms existing time series foundation models on observability data. It does this while also excelling at general-purpose forecasting tasks, achieving state-of-the-art zero-shot performance on multiple open benchmark datasets.

We're on a mission to build the best platform in the world for engineers to understand and scale their systems, applications, and teams. We operate at high scale--trillions of data points per day--allowing for seamless collaboration and problem-solving among Dev, Ops and Security teams globally for tens of thousands of companies. Our engineering culture values pragmatism, honesty, and simplicity to solve hard problems the right way. The Revenue Data Engineering Teams create the data processing pipelines that measure our customers' usage across all Datadog products, providing vital insights to a broad variety of users. This group of teams is at the leading edge of any new product we release.

Every chief executive is figuring out the right balance for new hybrid business models. Regardless of the chosen approach, which will vary, technology executives understand they must accelerate digital and build resilience as well as optionality into their platforms. This is driving a dramatic shift in information technology investments at the macro level as we expect total spending to increase at 8% in 2021, a big turnaround from last year's contraction. Investments in cybersecurity, cloud, collaboration to enable hybrid work and data, including analytics, artificial intelligence and automation are the top spending priorities for CxOs. In this post we'll share some takeaways from ETR's latest survey and provide our commentary on what it means for markets, sellers and buyers. We'll also explain what we think Wall Street is missing about Amazon's latest earnings.

These days, artificial intelligence is showing up in all sorts of new services. There's a reason: The use of AI is growing fast, and businesses putting it to good use are unlocking efficiencies and delivering better experiences to their customers. According to tech researcher IDC, global spending on AI is expected to have increased more than 12% this year and to top $156 billion. That's impressive given the current state of world affairs, and AI is expected to continue its expansion for the foreseeable future. If investing in the artificial intelligence trend for the long-haul is your goal, Dynatrace (NYSE:DT), Marvell Technology Group (NASDAQ:MRVL), and Medallia (NYSE:MDLA) stocks are worth serious looks.

Artificial intelligence operations and management software provider Algorithmia Inc. is taking on the chore of machine learning model performance monitoring with a new tool announced today that it says provides greater visibility into algorithm inference metrics. Algorithmia is a Google LLC-backed company that sells software designed to make machine learning projects easier to get off the ground and manage. Its software manages every stage of the machine learning lifecycle, automating model deployment, optimizing collaboration between operations and development, and leveraging existing so-called continuous integration/continuous development processes. It also provides security and governance, and it operates a marketplace for researchers and developers to share ML models they create and get paid when others use them. Algorithmia Insights is the company's latest addition to that software set.

Datadog, a heavily funded cloud monitoring platform for applications and infrastructure, has acquired under-the-radar French startup Madumbo, which develops an AI-powered web app-testing service. Terms of the deal were not disclosed. Founded in 2010, New York-based Datadog offers a DevOps toolset that enables developers to monitor everything in their stack, aggregating metrics and events across all of their databases, servers, apps, and more under a single unified view. Founded out of Paris in 2017, Madumbo provides an automated platform that helps companies determine whether a web app is performing as it should. Madumbo's bot checks for errors using code run from a real browser, meaning it should be able to detect the same issues end users experience.

The tools and practices of IT Operations have to get better and easier. Every IT Ops engineer has the job of responding to alerts when a website is down or unresponsive. To restore service, the engineer follows a certain procedure to restart the web server and validate that the website is operational. Maybe it happens again a few days later and another engineer repeats the procedure to restore service. If it happens yet again, a proactive engineer hopefully takes the initiative to develop a simple script that automates this procedure.