A company culture of growth

Learn how your company's culture can either advance or totally sabotage your company goals.

During its GTC 2021 virtual keynote, Nvidia introduced a new product designed to help enterprises choose, adapt, and deploy machine learning models. Called TAO and available starting today in early access, it enables transfer learning as well as other machine learning techniques from a single, enterprise-focused pane of glass.

Transfer learning’s ability to store knowledge gained while solving a problem and apply it to a related problem has attracted considerable attention in the enterprise. Using it, a data scientist can take an open source model like BERT, for example, which is designed to understand generic language, and refine it at the margins to comprehend the jargon employees use to describe IT issues.

TAO integrates Nvidia’s Transfer Learning Toolkit to leverage small datasets, giving models a custom fit without the cost, time, and massive corpora required to build and train models from scratch. TAO also incorporates federated learning, which lets different machines securely collaborate to refine a model for the highest accuracy. Users can share components of models while ensuring datasets remain inside each company’s datacenter.

In machine learning, federated learning entails training algorithms across client devices that hold data samples without exchanging those samples. A centralized server might be used to orchestrate rounds of training for the algorithm and act as a reference clock, or the arrangement might be peer-to-peer. Regardless, local algorithms are trained on local data samples and the weights — the learnable parameters of the algorithms — are exchanged between the algorithms at some frequency to generate a global model.

TAO also incorporates Nvidia TensorRT, which dials a model’s mathematical coordinates to a balance of the smallest model size with the highest accuracy for the system it’ll run on. Nvidia claims that TensorRT-based apps perform up to 40 times faster than CPU-only platforms during inference.

Elements of TAO are already in use in warehouses, in retail, in hospitals, and on the factory floor, Nvidia claims. Users include companies like Accenture, BMW and Siemens Industrial.

“AI is the most powerful new technology of our time, but it’s been a force that’s hard to harness for many enterprises — until now. Many companies lack the specialized skills, access to large datasets or accelerated computing that deep learning requires. Others are realizing the benefits of AI and want to spread them quickly across more products and services,” Adel El Hallak, director of product management for NGC at Nvidia, wrote in a blog post. “TAO … can quickly tailor and deploy an application using multiple AI models.”

The benefits of AI and machine learning can feel intangible at times, but surveys show this hasn’t deterred enterprises from adopting the technology in droves. Business use of AI grew a whopping 270% from 2015 to 2019, according to Gartner, while Deloitte says 62% of respondents for its corporate October 2018 report deployed some form of AI, up from 53% a year ago. Bolstered by this growth, Grand View Research predicts that the global AI market size will reach $733.7 billion by 2027.

A company culture of growth

Learn how your company's culture can either advance or totally sabotage your company goals.

This article is part of a Gaming Insights series paid for by Facebook.

We recently spoke with Katerina Dudinskaya, VP of Performance Marketing at Gismart, to discuss how the award-winning European publisher is adapting its approach to face new challenges brought about by upcoming iOS 14 changes, and how the team is closing the gap between acquisition and monetization with campaign-level Return On Ad Spend (ROAS).

Facebook: Let’s start off with a macro view of the industry: The pandemic is in full swing, and iOS 14 changes are coming up. How are these massive shifts impacting your business?

Dudinskaya: The pandemic has driven more traffic to almost all of our entertainment products and much of the 2020 uplift has continued through 2021, although we are seeing more competition.

And IDFA deprecation will force us to adapt our approach to marketing and business. For us, the best strategy is just to accept this new reality and try to be creative about how to adapt.

Facebook: As limitations on targeting are becoming top-of-mind for publishers and developers, we’re seeing an evolution in the ads ecosystem. Can you talk about some of the new challenges you’re facing this year?

Dudinskaya: At a high-level, our current challenges are firstly, how to deliver personalized ads and reach our most valuable customers and, secondly, how to provide our monetization partners with reliable user data to predict user-level value more accurately. Latency data and fraudulent data can affect our predictions, and using incorrect data can lead us to the wrong decisions.

We’re also developing updates for our user acquisition structure, analytics, and creative production to help us deal with these challenges.

Facebook: Let’s dive into what it means to build resilience in 2021. What approach are you taking when it comes to app monetization? 

Dudinskaya: Like most other publishers, we will be using a combination of SKAdNetwork and our own analytics systems to track all possible marketing funnel events (downloads, click-throughs, etc). This data will become the base for the decisions and predictions we make.

We’re also working more closely with all our partners: publishers, traffic vendors, and monetization networks. In this new reality, more than ever, you need to work as a team.

Facebook: User acquisition has been a key challenge for publishers and developers. How will you ensure that Gismart will continue to reach and acquire high-value users?

Dudinskaya: We are using all open solutions that are currently available, including AppsFlyer’s proprietary user attribution solution and Unity’s machine learning solution. We also work closely with Facebook Audience Network and Facebook Instant Games teams who help us with user acquisition and monetization. We also continuously improve non-technical ways of personalization, such as growth activities on landing pages, creative production, and UA channel diversification.

Facebook: Many publishers are concerned with revenue loss and the impact of ad targeting limitations and fluctuating CPMs. How do you plan to sustain revenue in this environment? 

Dudinskaya: We plan to apply a holistic approach, working on UA strategies and targeting creative production. We also use some CPM/bidding add-ons produced internally to be able to perform fast testing and to get a snapshot of user funnel metrics from the first impression to the deepest product metric. For games, this could be clickthrough rate, cost per install, cost per app event/acquisition, or average revenue per user.

Facebook: How are you currently measuring return on ad spend (ROAS) at a campaign level? How do you maintain oversight of what’s working on which channels?

Dudinskaya: Facebook Audience Network’s new campaign-level IAA ROAS has helped us to better understand our ROAS, offering accurate insights to make more profitable decisions.

It enables us to acquire quality users, understand the ideal user experience, and ensure long-term engagement and revenue. The synergy of data we receive from different analytics systems, mediation, and other tools give us deeper insights into the channel performance. The more data we can bring into day-to-day operations, the better results we see in our marketing channels.

Facebook: What advice would you share with other publishers and developers who are also preparing to face the headwinds of the upcoming industry changes?

Dudinskaya: It’s worth noting that each advertising platform is quite unique; some creative approaches that work well on one platform, might not perform that well on another. So publishers need to have individual marketing strategies for each platform, taking into consideration their audience and content consumption habits, and the platforms’ technical specifics.

Also, we’re always open to new solutions and we are constantly testing new Facebook features and tools available in an alpha/beta version. Testing new solutions is not only a great way to adapt your marketing strategy but also helps to identify errors and gaps in your current solution set.

Anastasia Petrova is Strategic Partner Manager at Facebook Audience Network.

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