In recent years, the publicly traded observability service New Relic started adding more machine learning-based tools to its platform for AI-assisted incident response when things don’t quite go as planned. Today, it is expanding this feature set with the launch of a number of new capabilities for what it calls its “New Relic Applied Intelligence Service.”

This expansion includes an anomaly detection service that is even available for free users, the ability to group alerts from multiple tools when the models think it’s a single issue that is triggering all of these alerts and new ML-based root cause analysis to help eliminate some of the guesswork when problems occur. Also new (and in public beta) is New Relic’s ability to detect patterns and outliers in log data that is stored in the company’s data platform.

The main idea here, New Relic’s director of product marketing Michael Olson told me, is to make it easier for companies of all sizes to reap the benefits of AI-enhanced ops.

Image Credits: New Relic

“It’s been about a year since we introduced our first set of AIops capabilities with New Relic Applied Intelligence to the market,” he said. “During that time, we’ve seen significant growth in adoption of AIops capabilities through New Relic. But one of the things that we’ve heard from organizations that have yet to foray into adopting AIops capabilities as part of their incident response practice is that they often find that things like steep learning curves and long implementation and training times — and sometimes lack of confidence, or knowledge of AI and machine learning — often stand in the way.”

The new platform should be able to detect emerging problems in real time — without the team having to pre-configure alerts. And when it does so, it’ll smartly group all of the alerts from New Relic and other tools together to cut down on the alert noise and let engineers focus on the incident.

“Instead of an alert storm when a problem occurs across multiple tools, engineers get one actionable issue with alerts automatically grouped based on things like time and frequency, based on the context that they can read in the alert messages. And then now with this launch, we’re also able to look at relationship data across your systems to intelligently group and correlate alerts,” Olson explained.

Image Credits: New Relic

Maybe the highlight for the ops teams that will use these new features, though, is New Relic’s ability to pinpoint the probable root cause of a problem. As Guy Fighel, the general manager of applied intelligence and vice president of product engineering at New Relic, told me, the idea here is not to replace humans but to augment teams.

“We provide a non-black-box experience for teams to craft the decisions and correlation and logic based on their own knowledge and infuse the system with their own knowledge,” Fighel noted. “So you can get very specific based on your environment and needs. And so because of that and because we see a lot of data coming from different tools — all going into New Relic One as the data platform — our probable root cause is very accurate. Having said that, it is still a probable root cause. So although we are opinionated about it, we will never tell you, ‘hey, go fix that, because we’re 100% sure that’s the case.’ You’re the human, you’re in control.”

The AI system also asks users for feedback, so that the model gets refined with every new incident, too.

Fighel tells me that New Relic’s tools rely on a variety of statistical analysis methods and machine learning models. Some of those are unique to individual users while others are used across the company’s user base. He also stressed that all of the engineers who worked on this project have a background in site reliability engineering — so they are intimately familiar with the problems in this space.

With today’s launch, New Relic is also adding a new integration with PagerDuty and other incident management tools so that the state of a given issue can be synchronized bi-directionally between them.

“We want to meet our customers where they are and really be data source agnostic and enable customers to pull in data from any source, where we can then enrich that data, reduce noise and ultimately help our customers solve problems faster,” said Olson.

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Amazon is apparently pleased with how its Amazon Care pilot in Seattle has gone, since it announced this morning that it will be expanding the offering across the U.S. this summer, and opening it up to companies of all sizes, in addition to its own employees. The Amazon Care model combines on-demand and in-person care, and is meant as a solution from the search giant to address shortfalls in current offerings for employer-sponsored healthcare.

In a blog post announcing the expansion, Amazon touted the speed of access to care made possible for its employees and their families via the remote, chat and video-based features of Amazon Care. These are facilitated via a dedicated Amazon Care app, which provides direct, live chats via a nurse or doctor. Issues that then require in-person care are then handled via a house call, so a medical professional is actually sent to your home to take care of things like administering blood tests or doing a chest exam, and prescriptions are delivered to your door as well.

The expansion is being handled differently across both in-person and remote variants of care; remote services will be available starting this summer to Amazon’s own employees, as well as other companies that sign on as customers, starting this summer. The in-person side will be rolling out more slowly, starting with availability in Washington, D.C., Baltimore, and “other cities in the coming months” according to the company.

As of today, Amazon Care is expanding in its home state of Washington to begin serving other companies. The idea is that others will sign on to make Amazon Care part of an overall benefits package for employees. Amazon is touting as a major strength of the service the speed advantages of testing services, including results delivery, for things including COVID-19.

The Amazon Care model has a surprisingly Amazon twist, too — when using the in-person care option, the app will provide an updated ETA for when to expect your physician or medical technician, which is eerily similar to how its primary app treats package delivery.

While the Amazon Care pilot in Washington only launched a year-and-a-half ago, the company has had its collective mind set on upending the corporate healthcare industry for some time now. It announced a partnership with Berkshire Hathaway and JPMorgan back at the beginning of 2018 to form a joint venture specifically to address the gaps they saw in the private corporate healthcare provider market.

That deep pocketed all-star team ended up officially disbanding at the outset of this year, after having done a whole lot of not very much in the three years in between. One of the stated reasons that Amazon and its partners gave for unpartnering was that each had made a lot of progress on its own in addressing the problems it had faced anyway. While Berkshire Hathaway and JPMorgan’s work in that regard might be less obvious, Amazon was clearly referring to Amazon Care.

It’s not unusual for large tech companies with lots of cash on the balance sheet and a need to attract and retain top-flight talent to spin up their own healthcare benefits for their workforces. Apple and Google both have their own on-campus wellness centers staffed by medical professionals, for instance. But Amazon’s ambitions have clearly exceeded those of its peers, and it looks intent on making a business line out of the work it did to improve its own employee care services — a strategy that isn’t too dissimilar from what happened with AWS, by the way.

SecurityScorecard has been helping companies understand the security risk of its vendors since 2014 by providing each one with a letter grade based on a number of dimensions. Today, the company announced a $180 million Series E.

The round includes new investors Silver Lake Waterman, T. Rowe Price, Kayne Anderson Rudnick, and Fitch Venture along with existing investors Evolution Equity Partners, Accomplice, Riverwood Capital, Intel Capital, NGP Capital, AXA Venture Partners, GV (Google Ventures) and Boldstart Ventures. The company reports it has now raised $290 million.

Co-founder and CEO Aleksandr Yampolskiy says the company’s mission has not changed since it launched. “The idea that we started the company was a realization that when I was CISO and CTO I had no metrics at my disposal. I invested in all kinds of solutions where I was completely in the dark about how I’m doing compared to the industry and how my vendors and suppliers were doing compared to me,” Yampolskiy told me.

He and his co-founder COO Sam Kassoumeh likened this to a banker looking at a mortgage application and having no credit score to check. The company changed that by starting a system of scoring the security posture of different companies and giving them a letter grade of A-F just like at school.

Today, it has ratings on more than 2 million companies worldwide, giving companies a way to understand how secure their vendors are. Yampolskiy says that his company’s solution can rate a new company not in the data set in just five minutes. Every company can see its own scorecard for free along with advice on how to improve that score.

He notes that in fact, the disastrous SolarWinds hack was entirely predictable based on SecurityScorecard’s rating system. “SolarWinds’ score has been lagging below the industry average for quite a long time, so we weren’t really particularly surprised about them,” he said.

The industry average is around 85 or a solid B in the letter grade system, whereas SolarWinds was sitting at 70 or a C for quite some time, indicating its security posture was suspect, he reports.

While Yampolskiy didn’t want to discuss valuation or revenue or even growth numbers, he did say the company has 17,000 customers worldwide including 7 of the 10 top pharmaceutical companies in the world.

The company has reached a point where this could be the last private fundraise it does before going public, but Yampolskiy kept his cards close on timing, saying it could happen some time in the next couple of years.

It was easy to wonder what would become of Docker after it sold its enterprise business in 2019, but it regrouped last year as a cloud native container company focused on developers, and the new approach appears to be bearing fruit. Today, the company announced a $23 million Series B investment.

Tribe Capital led the round with participation from existing investors Benchmark and Insight Partners. Docker has now raised a total of $58 million including the $35 million investment it landed the same day it announced the deal with Mirantis.

To be sure, the company had a tempestuous 2019 when they changed CEOs twice, sold the enterprise division and looked to reestablish itself with a new strategy. While the pandemic made 2020 a trying time for everyone, Docker CEO Scott Johnston says that in spite of that, the strategy has begun to take shape.

“The results we think speak volumes. Not only was the strategy strong, but the execution of that strategy was strong as well,” Johnston told me. He indicated that the company added 1.7 million new developer registrations for the free version of the product for a total of more than 7.3 million registered users on the community edition.

As with any open-source project, the goal is to popularize the community project and turn a small percentage of those users into paying customers, but Docker’s problem prior to 2019 had been finding ways to do that. While he didn’t share specific numbers, Johnston indicated that annual recurring revenue (ARR) grew 170% last year, suggesting that they are beginning to convert more successfully.

Johnston says that’s because they have found a way to turn a certain class of developer in spite of a free version being available. “Yes, there’s a lot of upstream open-source technologies, and there are users that want to hammer together their own solutions. But we are also seeing these eight to 10 person ‘two-pizza teams’ who want to focus on building applications, and so they’re willing to pay for a service,” he said.

That open-source model tends to get the attention of investors because it comes with that built-in action at the top of the sales funnel. Tribe’s Arjun Sethi, whose firm led the investment, says his company actually was a Docker customer before investing in the company and sees a lot more growth potential.

“Tribe focuses on identifying N-of-1 companies — top-decile private tech firms that are exhibiting inflection points in their growth, with the potential to scale toward outsized outcomes with long-term venture capital. Docker fits squarely into this investment thesis [ … ],” Sethi said in a statement.

Johnston says as they look ahead post-pandemic, he’s learned a lot since his team moved out of the office last year. After surveying employees, they were surprised to learn that most have been happier working at home, having more time to spend with family, while taking away a grueling commute. As a result, he sees going virtual first, even after it’s safe to reopen offices.

That said, he is planning to offer a way to get teams together for in-person gatherings and a full company get-together once a year.

“We’ll be virtual first, but then with the savings of the real estate that we’re no longer paying for, we’re going to bring people together and make sure we have that social glue,” he said.

Early Stage is the premier “how-to” event for startup entrepreneurs and investors. You’ll hear firsthand how some of the most successful founders and VCs build their businesses, raise money and manage their portfolios. We’ll cover every aspect of company building: Fundraising, recruiting, sales, product-market fit, PR, marketing and brand building. Each session also has audience participation built in — there’s ample time included for audience questions and discussion. Use code “TCARTICLE at checkout to get 20% off tickets right here.

Data is a gold mine for a company.

If managed well, it provides the clarity and insights that lead to better decision-making at scale, in addition to an important tool to hold everyone accountable.

However, most companies are stuck in Data 1.0, which means they are leveraging data as a manual and reactive service. Some have started moving to Data 2.0, which employs simple automation to improve team productivity. The complexity of crypto data has opened up new opportunities in data, namely to move to the new frontier of Data 3.0, where you can scale value creation through systematic intelligence and automation. This is our journey to Data 3.0.

Coinbase is neither a finance company nor a tech company — it’s a crypto company. This distinction has big implications for how we work with data. As a crypto company, we work with three major types of data (instead of the usual one or two types of data), each of which is complex and varied:

1. Blockchain: decentralized and publicly available.
2. Product: large and real-time.
3. Financial: high-precision and subject to many financial/legal/compliance regulations.

Image Credits: Michael Li/Coinbase

Our focus has been on how we can scale value creation by making this varied data work together, eliminating data silos, solving issues before they start and creating opportunities for Coinbase that wouldn’t exist otherwise.

Having worked at tech companies like LinkedIn and eBay, and also those in the finance sector, including Capital One, I’ve observed firsthand the evolution from Data 1.0 to Data 3.0. In Data 1.0, data is seen as a reactive function providing ad-hoc manual services or firefighting in urgent situations.