Pluto7 And Pythian Leveraging Google Cloud ML Specializations

‘When we know a customer is pursuing a specific solution, we’re going to bring in the partners that have those specializations,’ says Google Cloud channel chief Kevin Ichhpurani, corporate vice president of global ecosystem and channels.


Pluto7 used its machine learning expertise to help a global textile manufacturer better predict and react to customer demand. Pythian tapped its machine learning skills to help a regional U.S. grocery store chain predict shoppers’ buying habits.

Both companies, who are Google Cloud Premier Partners, today announced they’ve renewed their Google Cloud Machine Learning (ML) Specializations.

Milpitas, Calif.-based Pluto7 focuses on solving supply chain problems for customers across industries with AI/ML-powered solutions built on Google Cloud Platform (GCP).

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“They know that ML is a core component, and they want to make sure that we really know ML and Google has endorsed it,” said Manju Devadas, Pluto7’s founder and CEO. “And the way for them to verify Google has endorsed us is through this service specialization.”

The ML Specialization is one of 15 offered by Google Cloud, whose partners currently hold more than 450 specializations in total. A specialization is the highest technical designation that a Google Cloud partner can earn based on proven customer reference cases and technical capabilities vetted by the cloud provider and a third-party assessor. The specialization program ensures that partners have a minimum number of architects for the subject area and a robust business plan on how they’re growing around that area with Google Cloud.

The bar for the level of expertise required by customers is going up every day, making all Google Cloud certifications relevant for partners, according to Google Cloud channel chief Kevin Ichhpurani.

“More and more as they’re on their digital transformation journey, (customers) want more than just the IaaS stack,” Ichhpurani, corporate vice president of global ecosystem and channels, told CRN this week. “They’re leveraging analytics, they’re leveraging ML, customers are running SAP on GCP. They’re leveraging IoT applications. They’re developing new apps. Just having a knowledge of porting something over to the IaaS stack is not enough.”

The ML Specialization is a key one for Google Cloud, which sees ML as a differentiator among its rival cloud providers.

“We see ML and analytics often going hand in hand,” Ichhpurani said. “Customers want to take an application like a business process and make it fundamentally more intelligent, and so having that specialization on using our ML toolset together with our analytics is a combination that we see more and more.”

Beyond customer credibility, greater promotion from Google Cloud is a benefit of earning a specialization.

“When we know a customer is pursuing a specific solution, we’re going to bring in the partners that have those specializations,” Ichhpurani said.

Pluto7’s AI/ML Solutions

Pluto7’s solutions help customers with demand forecasting, inventory planning and positioning, logistics, track and trace, and marketing spending and sales.

“The heart of the problems we solve is supply chain and demand sensing, demand forecasting and inventory positioning,” Devadas said. “What we provide to the customer is a decision intelligence layer, which is a new layer that doesn’t exist in most enterprises today.”

Customers who come to Pluto7 typically have gaps in the decision-making layers of their supply chains that leave room for error. Humans often have to step in and make decisions beyond those addressed by their current systems, including enterprise resource planning systems from the likes of SAP and Oracle, according to Devadas.

“Customers realize that there is a layer of decision intelligence automation missing,” he said. “They come to us and say, ‘Hey, we need a better way to make decisions. We need ML recommendations, BI (business intelligence), we need AI insights, we need to be able to improve our forecast accuracy.’”

Pluto7’s solutions, which are available through Google Cloud Marketplace, include Demand ML, Inventory ML and Planning in a Box, a digital twin offering.

Pluto7 is working with a multibillion-dollar textile manufacturing company that wants to better predict and react to demand amid changes brought on by the coronavirus pandemic, including country borders that have shut down and reopened, and a shift in customer buying patterns in the omni-channel retail space.

“The demand for a given product goes up or down, and they want to be able to get closer to near real-time planning,” Devadas said.

Pluto7’s solutions allow all levels of the company – from stores to factories, warehouses and distribution centers – to receive the same data in close to real time so they can recalibrate their inventory.

“All of this is done by connecting internal and external data and putting machine learning models for demand forecasting on top of it,” Devadas said. “This drives profitability in the form of reducing inventory carrying costs. This is the same problem that we have solved for many other customers like California Design Den — which is another omni-channel retailer — over the last few years. And they have saved about 30 percent in inventory carrying costs by adopting these solutions.”

Pythian’s ML Specialization

Google Cloud’s ML Specialization is a particularly important one for Pythian, because it rounds out its data cloud and analytics story, according to Vanessa Simmons, senior vice president of business development for the New York-based IT services company.

“As cloud becomes more prevalent, and organizations are trying to digitally transform and get more value out of their data — whether they’re trying to monetize that data, trying to be more competitive with that data, trying to get maybe faster time to market with products with that data – it requires that extra mile or specialized skills in machine learning to do the more fun, cool, innovative type stuff,” Simmons said.

That “fun stuff” includes predicting what’s going to happen next, segmenting customers, predicting churn and determining what products to launch.

“Once you do all of the background work or heavy lifting work of ensuring that you can get all your data in one place, ensuring it’s clean, ensuring you can do some analysis on that, then you really want to get that data working for you,” Simmons said. “And it’s the magic of machine learning that helps to do that.”

The rigorous process to renew its Google Cloud ML Specialization included demos for three problem datasets that each took hundreds of hours to complete and extensive white papers on what was done with those datasets, Simmons said.

“These (datasets) were provided by Google, and you had to take them and actually build demos and projects out of it,” she said. “These are not things that you could fake. These take data scientists, data engineers — smart people on those teams who actually have the knowledge to step back and solve a particular problem.”

The third-party auditor, meanwhile, reviewed three Pythian customer case studies, including one focused on St. Louis-based Schnuck Markets, a family-owned grocery retailer with 111 stores. Pythian helped Schnuck develop an ML model, launched on GCP, to predict shoppers’ likelihood of buying certain products and to recommend complementary ones through personalized messaging. The chain was trying to leverage its loyalty program to build demand for its own house brand.

Earning the ML designation from Google Cloud is well worth that recertification effort, according to Simmons.

“Customers are always looking for partners who differentiate themselves,” she said. “They are looking for partners who align strongly with vendors like Google Cloud. They are looking for that credibility, those badges of honor, that technical capability. Over and above that, you also have to have the business acumen that goes with it and the project experience applied in customer environments. It has become much more sophisticated. The bar is higher for partners.”