Thanks to an explosion of data, exponential increases in computing power and storage capacity, and better algorithms, artificial intelligence (AI) and machine learning (ML) capabilities are poised to revolutionize business processes.
“There is a lot of long-term momentum in the AI market,” says Nitish Mittal, Partner in the digital transformation practice at global research firm Everest Group, noting that AI/ML adoption is expected to accelerate through 2024.
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With cyberattacks and data breaches at all-time highs, consumers are increasingly skeptical about sharing their data with enterprises, creating a dilemma for artificial intelligence (AI) that needs massive data to thrive. The nascent technology of federated learning offers an ideal growing alternative for healthcare, life sciences, banking, finance, manufacturing, advertising, and other industries to unleash the full potential of AI without compromising the privacy of individuals. To learn how you can have all the data you need while protecting consumers, read on.
The infinite number of massive data breaches that have stripped individuals of their privacy has made the public more aware of the need to protect their data. In the absence of strong governance and guidelines, people are more skeptical than ever about sharing their personal data with enterprises.
This new data-conscious paradigm poses a problem for artificial intelligence (AI) that thrives on huge amounts of data. Unless we can figure out a way to train machines on significantly smaller data sets, protecting the privacy and data of users will remain key obstacles to intelligent automation.
Federated learning (FL) is emerging as a solution to overcome this problem. Broadly speaking, Federated learning is a method of training machine learning models in a way that the user data does not leave its location, keeping it safe and private. This differs from the traditional centralized machine learning methods that require the data to be aggregated in a centralized location.
Federated learning is a mechanism of machine learning, wherein the process of learning takes place in a decentralized manner across a network of nodes/edge devices, and the results are aggregated in a central server to create a unified model. It essentially comprises decoupling the activity of model training with centralized data storage.
By training the same model across an array of devices, each with its own set of data, we get multiple versions of the model, which, when combined, create a more powerful and accurate global version for deployment and use.
In addition to training algorithms in a private and secure manner, Federated learning provides an array of other benefits such as:
Based on an Everest Group framework, we have found Federated learning is most suitable and being adopted at higher rates in industrials where data is an extremely critical asset that is present across different locations in a distributed fashion and privacy is paramount.
Federated learning is especially beneficial for industries that have strict data residency requirements. This makes the healthcare and life-sciences industries perfect candidates for its adoption. Federated learning can help facilitate multi-institution collaborations between medical institutions by helping them overcome regulatory hurdles that prevent them from sharing patient data by pooling data in a common location.
The next industry ripe for the adoption of Federated learning is the banking and financial sectors. For instance, it can be used to develop a more comprehensive and accurate fraud analytics solution that is based on data from multiple financial entities.
Another industry where we see high applicability of Federated learning is the manufacturing industry. By ensuring collaboration between different entities across the supply chain, using Federated learning techniques, there is a case to build a more powerful model that can help increase the overall efficiency across the supply chain.
Federated learning also might find increased use in interest-based advertising. With the decision to disable third-party cookies by major internet browsers, marketers are at a loss for targeted advertising and engagement. With Federated Learning, marketers can replace individual identifiers with cohorts or group-based identifiers. These cohorts are created by identifying people with common interests based on individual user data such as browsing habits stored on local machines.
Since Google introduced the concept of Federated learning in 2016, there has been a flurry of activity. Given that this is a nascent technology, the ecosystem is currently dominated by big tech and open-source players. We see hyperscalers taking the lead with Microsoft and Amazon Web Services (AWS) making investments to activate Federated learning, followed by Nvidia and Lenovo who are looking at the market from a hardware perspective.
Another segment of players working in this arena are startups that are using Federated learning to build industry-specific solutions. AI companies such as Owkin and Sherpa.ai are pioneering this technology and have developed Federated learning frameworks that are currently operational at a few enterprises’ locations.
The adoption and need for Federated learning depend on the industry and vary with the use case. Everest Group has developed a comprehensive framework to help you assess and understand the suitability of Federated learning for your use-case in our Latest Primer for Federated Learning. The framework is built on four key parameters that include data criticality, privacy requirement, regulatory constraint, and data silo/ diversity.
Federated learning provides us with an alternative way to make AI work in a world without compromising the privacy of individuals.
If you are interested in understanding the suitability of federated learning for your enterprise, please share your thoughts with us at [email protected].
In this era of industrialization for Artificial Intelligence (AI), enterprises are scrambling to embed AI across a plethora of use cases in hopes of achieving higher productivity and enhanced experiences. However, as AI permeates through different functions of an enterprise, managing the entire charter gets tough. Working with multiple Machine Learning (ML) models in both pilot and production can lead to chaos, stretched timelines to market, and stale models. As a result, we see enterprises hamstrung to successfully scale AI enterprise-wide.
To overcome the challenges enterprises face in their ML journeys and ensure successful industrialization of AI, enterprises need to shift from the current method of model management to a faster and more agile format. An ideal solution that is emerging is MLOps – a confluence of ML and information technology operations based on the concept of DevOps.
According to our recently published primer on MLOps, Successfully Scaling Artificial Intelligence – Machine Learning Operations (MLOps), these sets of practices are aimed at streamlining the ML lifecycle management with enhanced collaboration between data scientists and operations teams. This close partnering accelerates the pace of model development and deployment and helps in managing the entire ML lifecycle.
MLOps is modeled on the principles and practices of DevOps. While continuous integration (CI) and continuous delivery (CD) are common to both, MLOps introduces the following two unique concepts:
We are witnessing MLOps gaining momentum in the ecosystem, with hyperscalers developing dedicated solutions for comprehensive machine learning management to fast-track and simplify the entire process. Just recently, Google launched Vertex AI, a managed AI platform, which aims to solve these precise problems in the form of an end-to-end MLOps solution.
MLOps bolsters the scaling of ML models by using a centralized system that assists in logging and tracking the metrics required to maintain thousands of models. Additionally, it helps create repeatable workflows to easily deploy these models.
Below are a few additional benefits of employing MLOps within your enterprise:
Implementing MLOps is complex because it requires a multi-functional and cross-team effort across the key elements of people, process, tools/platforms, and strategy underpinned by rigorous change management.
As enterprises embark on their MLOps journey, here are a few key best practices to pave the way for a smooth transition:
When embarking on the MLOps journey, there is no one-size-fits-all approach. Enterprises need to assess their goals, examine their current ML tooling and talent, and also factor in the available time and resources to arrive at an MLOps strategy that best suits their needs.
For ML to keep pace with the agility of modern business, enterprises need to start experimenting with MLOps now.
Are you looking to scale AI within your enterprise with the help of MLOps? Please share your thoughts with us at [email protected].
Enterprises have identified Artificial Intelligence (AI) as a quintessential enabling technology in the success of digital transformation initiatives to further increase operational efficiency, improve employee productivity, and deliver enhanced stakeholder experience. According to our recent research, Artificial Intelligence (AI) Services – State of the Market Report 2021 | Scale the AI Summit Through Democratization, more than 72 percent of enterprises have embarked on their AI journey. This increased AI adoption is leading us into an era of industrialization of AI.
Enterprises face many challenges in their AI adoption journey, including rising concerns of privacy, lack of proven Return on Investment (RoI) for AI initiatives, increasing need for explainability, and an extreme crunch for skilled AI talent. According to Everest Group’s recent assessment of the AI services market, 43 percent of the enterprises identified limited availability of skilled, mature, and niche AI talent as one of the biggest challenges they face in scaling their AI initiatives.
Enterprises face this talent crunch in using both the open-source ecosystem and hyperscalers’ AI platforms for the reasons below:
As per our research in the talent readiness assessment, a large demand-supply gap exists for AI technologies (approximately 25 to 30 percent), hindering an enterprise’s ability to attract and retain talent.
In addition to this technical talent crunch, another aspect where enterprises struggle to find the right talent is the non-technical facet of AI that includes roles such as AI ethicists, behavioral scientists, and cognitive linguists.
As more and more enterprises adopt AI, this talent challenge becomes more exacerbated at the same time the demand for AI technologies is skyrocketing. There is an ongoing tussle for AI talent between academic, big tech, and enterprises, and, so far, the big techs are coming out on top. They have been able to successfully recruit huge amounts of AI talent, leaving a drying pool for the rest of the enterprises to fish in.
We see democratization as a potential solution to overcome this expanding talent gap. As we define it, democratization is primarily concerned with making AI accessible to a wider set of users targeted specifically at non-technical business users. The principle behind the concept of democratization is “AI for all.”
Democratization has to do with educating business users in the basic concepts of data and AI and giving them access to the data and tools that can help build a larger database of AI use-cases, develop insights, and find AI-based solutions to their problems.
Enterprises can leverage Everest Group’s four-step democratization framework to help address talent gaps within the enterprise and empower its employees. Here are the steps to guide a successful democratization initiative:
Following these steps, democratization can help reduce the barriers to entry for AI experimentation, accelerate enterprise-wide adoption, and speed up in-house innovation among its benefits.
The industry as a whole is now in the initial stages of AI democratization, which is heavily focused on data and AI literacy initiatives. Some of the more technologically advanced or well-versed enterprises have been early adopters. The exhibit below presents the current market adoption of the four key elements of democratization and a few industry examples:
As part of their democratization efforts, enterprises must also focus on contextualization, change management, and governance to ensure responsible and successful democratization.
By doing this, companies will not only help solve the persistent AI talent crunch but also ensure faster time to market, empower business users, and increase employee productivity. Hence, democratization is an essential step to ensuring the sustainable, inclusive, and responsible adoption of AI.
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