Personal blog of Yzmir Ramirez

“Yes.”

That was Google Cloud CEO Thomas Kurian’s simple answer when I asked if he thought he’d achieved what he set out to do in his first year.

A year ago, he took the helm of Google’s cloud operations — which includes G Suite — and set about giving the organization a sharpened focus by expanding on a strategy his predecessor Diane Greene first set during her tenure.

It’s no secret that Kurian, with his background at Oracle, immediately put the entire Google Cloud operation on a course to focus on enterprise customers, with an emphasis on a number of key verticals.

So it’s no surprise, then, that the first highlight Kurian cited is that Google Cloud expanded its feature lineup with important capabilities that were previously missing. “When we look at what we’ve done this last year, first is maturing our products,” he said. “We’ve opened up many markets for our products because we’ve matured the core capabilities in the product. We’ve added things like compliance requirements. We’ve added support for many enterprise things like SAP and VMware and Oracle and a number of enterprise solutions.” Thanks to this, he stressed, analyst firms like Gartner and Forrester now rank Google Cloud “neck-and-neck with the other two players that everybody compares us to.”

If Google Cloud’s previous record made anything clear, though, it’s that technical know-how and great features aren’t enough. One of the first actions Kurian took was to expand the company’s sales team to resemble an organization that looked a bit more like that of a traditional enterprise company. “We were able to specialize our sales teams by industry — added talent into the sales organization and scaled up the sales force very, very significantly — and I think you’re starting to see those results. Not only did we increase the number of people, but our productivity improved as well as the sales organization, so all of that was good.”

He also cited Google’s partner business as a reason for its overall growth. Partner influence revenue increased by about 200% in 2019, and its partners brought in 13 times more new customers in 2019 when compared to the previous year.

At its annual Ignite conference in Orlando, Fla., Microsoft today announced a major new Azure service for enterprises: Azure Synapse Analytics, which Microsoft describes as “the next evolution of Azure SQL Data Warehouse.” Like SQL Data Warehouse, it aims to bridge the gap between data warehouses and data lakes, which are often completely separate. Synapse also taps into a wide variety of other Microsoft services, including Power BI and Azure Machine Learning, as well as a partner ecosystem that includes Databricks, Informatica, Accenture, Talend, Attunity, Pragmatic Works and Adatis. It’s also integrated with Apache Spark.

The idea here is that Synapse allows anybody working with data in those disparate places to manage and analyze it from within a single service. It can be used to analyze relational and unstructured data, using standard SQL.

Microsoft also highlights Synapse’s integration with Power BI, its easy to use business intelligence and reporting tool, as well as Azure Machine Learning for building models.

With the Azure Synapse studio, the service provides data professionals with a single workspace for prepping and managing their data, as well as for their big data and AI tasks. There’s also a code-free environment for managing data pipelines.

As Microsoft stresses, businesses that want to adopt Synapse can continue to use their existing workloads in production with Synapse and automatically get all of the benefits of the service. “Businesses can put their data to work much more quickly, productively, and securely, pulling together insights from all data sources, data warehouses, and big data analytics systems,” writes Microsoft CVP of Azure Data, Rohan Kumar.

In a demo at Ignite, Kumar also benchmarked Synapse against Google’s BigQuery. Synapse ran the same query over a petabyte of data in 75% less time. He also noted that Synapse can handle thousands of concurrent users — unlike some of Microsoft’s competitors.

As Google Cloud Next opened today in San Francisco, Accenture announced its intent to acquire Cirruseo, a French cloud consulting firm that specializes in Google Cloud intelligence services. The companies did not share the terms of the deal.

Accenture says that Cirruseo’s strength and deep experience in Google’s cloud-based artificial intelligence solutions should help as Accenture expands its own AI practice. Google TensorFlow and other intelligence solutions are a popular approach to AI and machine learning, and the purchase should help give Accenture a leg up in this area, especially in the French market.

“The addition of Cirruseo would be a significant step forward in our growth strategy in France, bringing a strong team of Google Cloud specialists to Accenture,” Olivier Girard, Accenture’s geographic unit managing director for France and Benelux said in a statement.

With the acquisition, should it pass French regulatory muster, the company would add a team of 100 specialists trained in Google Cloud and G Suite to the an existing team of 2,600 Google specialists worldwide.

The company sees this as a way to enhance its artificial intelligence and machine learning expertise in general, while giving it a much stronger market placement in France in particular and the EU in general.

As the company stated, there are some hurdles before the deal becomes official. “The acquisition requires prior consultation with the relevant works councils and would be subject to customary closing conditions,” Accenture indicated in a statement. Should all that come to pass, then Cirruseo will become part of Accenture.

Cloud Foundry, the open-source platform-as-a-service project that more than half of the Fortune 500 companies use to help them build, test and deploy their applications, launched well before Kubernetes existed. Because of this, the team ended up building Diego, its own container management service. Unsurprisingly, given the popularity of Kubernetes, which has become somewhat of the de facto standard for container orchestration, a number of companies in the Cloud Foundry ecosystem starting looking into how they could use Kubernetes to replace Diego.

The result of this is Project Eirini, which was first proposed by IBM. As the Cloud Foundry Foundation announced today, Project Eirini now passes the core functional tests the team runs to validate the software releases of its application runtime, the core Cloud Foundry service that deploys and manages applications (if that’s a bit confusing, don’t even think about the fact that there’s also a Cloud Foundry Container Runtime, which already uses Kubernetes, but which is mostly meant to give enterprise a single platform for running their own applications and pre-built containers from third-party vendors).

“That’s a pretty big milestone,” Cloud Foundry Foundation CTO Chip Childers told me. “The project team now gets to shift to a mode where they’re focused on hardening the solution and making it a bit more production-ready. But at this point, early adopters are also starting to deploy that [new] architecture.”

Childers stressed that while the project was incubated by IBM, which has been a long-time backer of the overall Cloud Foundry project, Google, Pivotal and others are now also contributing and have dedicated full-time engineers working on the project. In addition, SUSE, SAP and IBM are also active in developing Eirini.

Eirini started as an incubation project, and while few doubted that this would be a successful project, there was a bit of confusion around how Cloud Foundry would move forward now that it essentially had two container engines for running its core service. At the time, there was even some concern that the project could fork. “I pushed back at the time and said: no, this is the natural exploration process that open-source communities need to go through,” Childers said. “What we’re seeing now is that with Pivotal and Google stepping in, that’s a very clear sign that this is going to be the go-forward architecture for the future of the Cloud Foundry Application Runtime.”

A few months ago, by the way, Kubernetes was still missing a few crucial pieces the Cloud Foundry ecosystem needed to make this move. Childers specifically noted that Windows support — something the project’s enterprise users really need — was still problematic and lacked some important features. In recent releases, though, the Kubernetes team fixed most of these issues and improved its Windows support, rendering those issues moot.

What does all of this mean for Diego? Childers noted that the community isn’t at a point where it’ll hold developing that tool. At some point, though, it seems likely that the community will decide that it’s time to start the transition period and make the move to Kubernetes official.

It’s worth noting that IBM today announced its own preview of Eirini in its Cloud Foundry Enterprise Environment and that the latest version of SUSE’s Cloud Foundry-based Application Platform includes a similar preview as well.

In addition, the Cloud Foundry Foundation, which is hosting its semi-annual developer conference in Philadelphia this week, also announced that it has certified it first to systems integrators, Accenture and HCL as part of its recently launched certification program for companies that work in the Cloud Foundry ecosystem and have at least 10 certified developers on their teams.

At last year’s Microsoft Ignite conference, the CEOs of Microsoft, Adobe and SAP took the stage to announce the launch of the Open Data Initiative. The idea behind this effort was to make it easier for their customers to move data between each others’ services by standardizing on a common data format and helping them move their data out of their respective silos and into a single customer-chosen data lake. At this week’s Adobe Summit, the three companies today announced how they plan to expand this program as they look to bring in additional partners.

“The intent of the companies joining forces was really to solve a common customer problem that we hear time and time again, which is that there are high-value business data tends to be very siloed in a variety of different applications,” Alysa Taylor, Microsoft’s corporate vice president, Business Applications & Global Industry, told me. “Being able to extract that data, reason over that data, garner intelligence from that data, is very cost-prohibitive and it’s very manual and time-consuming.”

The core principle of the alliance is that the customers own their data and they should be able to get as much value out of it as they can. Ideally, having this common data schema means that the customer doesn’t have to figure out ways to transform the data from these vendors and can simply flow all of it into a single data lake that then in turn feeds the various analytics services, machine learning systems and other tools that these companies offer.

At the Adobe Summit today, the three companies showed their first customer use case based on how Unilever is making use of this common data standard. More importantly, though, they also stressed that the Open Data Initiative is indeed open to others. As a first step, the three companies today announced the formation of a partner advisory council.

“What this basically means is that we’ve extended it out to key participants in the ecosystem to come and join us as part of this ODI effort,” Adobe’s VP of Ecosystem Development Amit Ahuja told me. “What we’re starting with is really a focus around two big groups of partners. Number one is, who are the other really interesting ISVs who have a lot of this core data that we want to make sure we can bring into this kind of single unified view. And the second piece is who are the major players out there that are trying to help these customers around their enterprise architecture.”

The first 12 partners that are joining this new council include Accenture, Amadeus, Capgemini, Change Healthcare, Cognizant, EY, Finastra, Genesys, Hootsuite, Inmobi, Sprinklr and WPP . This is very much a first step, though. Over time, the group expects to expand far beyond this first set of partners and include a much larger group of stakeholders.

“We really want to make this really broad in a way that we can quickly make progress and demonstrate that what we’re talking about from a conceptual process has really hard customer benefits attached to it,” Abhay Kumar, SAP’s global vice president, Global Business Development & Ecosystem, noted. The use cases the alliance has identified focus on market intelligence, sales intelligence and services intelligence, he added.

Today, as enterprises often pull in data from dozens of disparate systems, making sense of all that information is hard enough, but to even get to this point, enterprises first have to transform it and make it usable. To do so, they then have to deploy another set of applications that massages the data. “I don’t want to go and buy another 15 or 20 applications to make that work,” Ahuja said. “I want to realize the investment and the ROI of the applications that I’ve already bought.”

All three stressed that this is very much a collaborative effort that spans the engineering, sales and product marketing groups.

IBM has launched a software service that scans AI systems as they work in order to detect bias and provide explanations for the automated decisions being made — a degree of transparency that may be necessary for compliance purposes not just a company’s own due diligence.

The new trust and transparency system runs on the IBM cloud and works with models built from what IBM bills as a wide variety of popular machine learning frameworks and AI-build environments — including its own Watson tech, as well as Tensorflow, SparkML, AWS SageMaker, and AzureML.

It says the service can be customized to specific organizational needs via programming to take account of the “unique decision factors of any business workflow”.

The fully automated SaaS explains decision-making and detects bias in AI models at runtime — so as decisions are being made — which means it’s capturing “potentially unfair outcomes as they occur”, as IBM puts it.

It will also automatically recommend data to add to the model to help mitigate any bias that has been detected.

Explanations of AI decisions include showing which factors weighted the decision in one direction vs another; the confidence in the recommendation; and the factors behind that confidence.

IBM also says the software keeps records of the AI model’s accuracy, performance and fairness, along with the lineage of the AI systems — meaning they can be “easily traced and recalled for customer service, regulatory or compliance reasons”.

For one example on the compliance front, the EU’s GDPR privacy framework references automated decision making, and includes a right for people to be given detailed explanations of how algorithms work in certain scenarios — meaning businesses may need to be able to audit their AIs.

The IBM AI scanner tool provides a breakdown of automated decisions via visual dashboards — an approach it bills as reducing dependency on “specialized AI skills”.

However it is also intending its own professional services staff to work with businesses to use the new software service. So it will be both selling AI, ‘a fix’ for AI’s imperfections, and experts to help smooth any wrinkles when enterprises are trying to fix their AIs… Which suggests that while AI will indeed remove some jobs, automation will be busy creating other types of work.

Nor is IBM the first professional services firm to spot a business opportunity around AI bias. A few months ago Accenture outed a fairness tool for identifying and fixing unfair AIs.

So with a major push towards automation across multiple industries there also looks to be a pretty sizeable scramble to set up and sell services to patch any problems that arise as a result of increasing use of AI.

And, indeed, to encourage more businesses to feel confident about jumping in and automating more. (On that front IBM cites research it conducted which found that while 82% of enterprises are considering AI deployments, 60% fear liability issues and 63% lack the in-house talent to confidently manage the technology.)

In additional to launching its own (paid for) AI auditing tool, IBM says its research division will be open sourcing an AI bias detection and mitigation toolkit — with the aim of encouraging “global collaboration around addressing bias in AI”.

“IBM led the industry in establishing trust and transparency principles for the development of new AI technologies. It’s time to translate principles into practice,” said David Kenny, SVP of cognitive solutions at IBM, commenting in a statement. “We are giving new transparency and control to the businesses who use AI and face the most potential risk from any flawed decision making.”

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