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How startups can leverage elastic services for cost optimization

Due to COVID-19, business continuity has been put to the test for many companies in the manufacturing, agriculture, transport, hospitality, energy and retail sectors. Cost reduction is the primary focus of companies in these sectors due to massive losses in revenue caused by this pandemic. The other side of the crisis is, however, significantly different.

Companies in industries such as medical, government and financial services, as well as cloud-native tech startups that are providing essential services, have experienced a considerable increase in their operational demands — leading to rising operational costs. Irrespective of the industry your company belongs to, and whether your company is experiencing reduced or increased operations, cost optimization is a reality for all companies to ensure a sustained existence.

One of the most reliable measures for cost optimization at this stage is to leverage elastic services designed to grow or shrink according to demand, such as cloud and managed services. A modern product with a cloud-native architecture can auto-scale cloud consumption to mitigate lost operational demand. What may not have been obvious to startup leaders is a strategy often employed by incumbent, mature enterprises — achieving cost optimization by leveraging managed services providers (MSPs). MSPs enable organizations to repurpose full-time staff members from impacted operations to more strategic product lines or initiatives.

Why companies need cost optimization in the long run

Mirantis releases its first major update to Docker Enterprise

In a surprise move, Mirantis acquired Docker’s Enterprise platform business at the end of last year and while Docker itself is refocusing on developers, Mirantis kept the Docker Enterprise name and product. Today, Mirantis is rolling out its first major update to Docker Enterprise with the release of version 3.1.

For the most part, these updates are in line with what’s been happening in the container ecosystem in recent months. There’s support for Kubernetes 1.17 and improved support for Kubernetes on Windows (something the Kubernetes community has worked on quite a bit in the last year or so). Also new is Nvidia GPU integration in Docker Enterprise through a pre-installed device plugin, as well as support for Istio Ingress for Kubernetes and a new command-line tool for deploying clusters with the Docker Engine.

In addition to the product updates, Mirantis is also launching three new support options for its customers that now give them the option to get 24×7 support for all support cases, for example, as well as enhanced SLAs for remote managed operations, designated customer success managers and proactive monitoring and alerting. With this, Mirantis is clearly building on its experience as a managed service provider.

What’s maybe more interesting, though, is how this acquisition is playing out at Mirantis itself. Mirantis, after all, went through its fair share of ups and downs in recent years, from high-flying OpenStack platform to layoffs and everything in between.

“Why we do this in the first place and why at some point I absolutely felt that I wanted to do this is because I felt that this would be a more compelling and interesting company to build, despite maybe some of the short-term challenges along the way, and that very much turned out to be true. It’s been fantastic,” Mirantis CEO and co-founder Adrian Ionel told me. “What we’ve seen since the acquisition, first of all, is that the customer base has been dramatically more loyal than people had thought, including ourselves.”

Ionel admitted that he thought some users would defect because this is obviously a major change, at least from the customer’s point of view. “Of course we have done everything possible to have something for them that’s really compelling and we put out the new roadmap right away in December after the acquisition — and people bought into it at very large scale,” he said. With that, Mirantis retained more than 90 percent of the customer base and the vast majority of all of Docker Enterprise’s largest users.

Ionel, who almost seemed a bit surprised by this, noted that this helped the company to turn in two “fantastic” quarters and was profitable in the last quarter, despite the COVID-19.

“We wanted to go into this acquisition with a sober assessment of risks because we wanted to make it work, we wanted to make it successful because we were well aware that a lot of acquisitions fail,” he explained. “We didn’t want to go into it with a hyper-optimistic approach in any way — and we didn’t — and maybe that’s one of the reasons why we are positively surprised.”

He argues that the reason for the current success is that enterprises are doubling down on their container journeys and because they actually love the Docker Enterprise platform, like infrastructure independence, its developer focus, security features and ease of use. One thing many large customers asked for was better support for multi-cluster management at scale, which today’s update delivers.

“Where we stand today, we have one product development team. We have one product roadmap. We are shipping a very big new release of Docker Enterprise. […] The field has been completely unified and operates as one salesforce, with record results. So things have been extremely busy, but good and exciting.”

Microsoft launches new tools for building fairer machine learning models

At its Build developer conference, Microsoft today put a strong emphasis on machine learning but in addition to plenty of new tools and features, the company also highlighted its work on building more responsible and fairer AI systems — both in the Azure cloud and Microsoft’s open-source toolkits.

These include new tools for differential privacy and a system for ensuring that models work well across different groups of people, as well as new tools that enable businesses to make the best use of their data while still meeting strict regulatory requirements.

As developers are increasingly tasked to learn how to build AI models, they regularly have to ask themselves whether the systems are “easy to explain” and that they “comply with non-discrimination and privacy regulations,” Microsoft notes in today’s announcement. But to do that, they need tools that help them better interpret their models’ results. One of those is interpretML, which Microsoft launched a while ago, but also the Fairlearn toolkit, which can be used to assess the fairness of ML models, and which is currently available as an open-source tool and which will be built into Azure Machine Learning next month.

As for differential privacy, which makes it possible to get insights from private data while still protecting private information, Microsoft today announced WhiteNoise, a new open-source toolkit that’s available both on GitHub and through Azure Machine Learning. WhiteNoise is the result of a partnership between Microsoft and Harvard’s Institute for Quantitative Social Science.