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Machine learning ops to lead AI in 2020

The increased usage of pre-trained models, machine learning ops coming to the fore and increased transparency are all poised to lead the new year in AI.

At the end of the decade, it's clear that AI has continued at its unrelenting pace of adoption, investment and growth in both the private and public sectors. It has been a banner year for investment across the board in AI, from investors and venture capitalists to governments across the globe. This year was also one of expansion for AI activity across a wide range of industries from finance and banking, accounting, fitness and wellness, legal and many back-office operations. This year also saw people become increasingly aware of their data footprint, and express concerns with how companies are using data. Since it's clear that AI shows no signs of slowing down as we approach the start of a new year, here are a few predictions about where AI will make waves in 2020.

2020 will be the year of model-as-a-service

Last year, we predicted that 2019 was going to be the year of the pre-trained model and improved third party datasets. For 2020 we're going to take this prediction one step further and say that these pre-trained models offered by third parties will become a larger percentage of the overall model usage. Known as model-as-a-service, we'll see entities set up specifically to offer models for usage on a per-consumption, subscription or license basis. This means we'll start to see more model marketplaces and model-as-a-service embedded in cloud offerings.

Currently machine learning is, for the most part, limited to companies that have large data sets and large teams. These tech companies, big enterprises and well-funded startups have all invested heavily in data science talent and machine learning skills to build and manage their own models. However, small businesses also want access to the same machine learning technology. These organizations will be more interested in consuming models built by others rather than developing the in-house talent and skills to build their own.

This will result in a shift in the market, as companies adopting AI in 2020 will not be those building their own proprietary machine learning models from scratch but rather using or extending third party models. This will increase the reach of AI to a much larger pool of companies, including smaller businesses such as regional banks, regional insurance companies and regional retailers.

Machine learning ops comes to the fore

Commensurate with the growth of third-party model usage is the shift to consumption-centric approaches to ML model usage. The market is already shifting in the way that organizations approach model development and usage. While up to this point the primary emphasis has been on model development and creation for use by a single organization, the shift to consumption-centric models will require tooling and environments that have the specific needs of model users vs. model developments.  In 2020, we'll see the growth of machine learning ops infrastructure that provides a range of functionality and capabilities for those looking to consume models.

Machine learning ops systems are meant to simplify the usage and consumption of various AI and machine learning models that were built in-house or by third-party vendors. The platforms will offer features including model governance for controlling, limiting or prioritizing access to models for different users, allowing for collaboration in model usage among various team members. Managing model operationalization, handling model versioning, model monitoring, model security, model transparency and other factors primarily relevant to model usage and consumption will also be features. Machine learning ops platforms ensure that the correct model versions are in use, that models are secure and not corrupted, and continual monitoring is done to prevent model decay.

In 2019, companies such as HPE and Booz Allen Hamilton released machine learning ops platforms, but we expect to see many more companies to enter the fray in 2020. The growth of machine learning ops usage is going to help both public and private sector users get a better overall understanding and management of all their machine learning models.

Responsible and ethical AI continues to be important

The General Data Protection Regulation (GDPR) entered full effect in 2019, making the European Union's data privacy regulation a de facto worldwide law. Part of GDPR is particularly relevant to AI in 2020 in that it requires companies to comply with the various data-centric regulations such as the lawful, fair and transparent processing of personal data or consumers' right to be forgotten. In 2019, the state of California also passed a data privacy law called the California Consumer Privacy Act (CCPA), which is set to officially take effect in January 2020.

The combination of these laws, as well as people becoming increasingly more aware of their data footprint and the use of their data, is going to have an impact on the use of consumer data in machine learning model development in 2020. As AI becomes even more intertwined in our everyday lives, users will increasingly demand the responsible and ethical use of AI.

To help address these issues, companies are taking an active stance by promoting efforts to help with transparent and ethical AI. For AI in 2020, we expect this trend to accelerate. As it is now common knowledge that the most popular machine learning algorithm approach in use today, deep learning, is widely considered to be an unexplainable "black box," people are increasingly demanding more transparency into how these systems arrive at decisions. Some organizations are tackling this problem by creating standards and transparency scores for users to better understand models. As model-as-a-service becomes more widespread, and AI is used to make increasingly important decisions at organizations, these efforts around transparency will become more important.

RPA market shows signs of maturity and possible consolidation

While RPA is often discussed in the same context as AI, many have become acutely aware of the fact that RPA systems are not intelligent and rely on third-party machine learning tools to add any aspect of cognitive intelligence. Despite the possibly mistaken association between RPA and AI, the market continues to be hot, and possibly overinflated, for the past year.

Two of the biggest vendors in the market each raised large sums from investors in 2019. UiPath raised $568 million in Series D funding with a $7 billion valuation, and Automation Anywhere raised an additional $290 million at a $6.8 billion valuation.. Given that intense interest by the market, Microsoft entered the fray with their own offering in late 2019 with UI Flows as part of the Power Automate platform. The company is already claiming 150,000 users, which indicates the latent interest for RPA offerings and the potency of Microsoft as a market disruptor.

Despite these growth indicators, there are signs of potential cooling and consolidation in the market in 2020. UiPath announced a very public round of layoffs right after their big event in Las Vegas in October 2019, letting go of over 400 employees. Similarly, we're hearing indications that the large enterprise software vendors including Oracle, SAP, IBM and others might be making more aggressive moves in this market. Within the next few years it's highly likely that the RPA market will consist of large enterprise software vendors who have grown either organically or through acquisition of fast-growing RPA vendors looking to provide their investors a return on their significant investments.

Voice assistant stagnation, at least in the enterprise

In 2019 we predicted that voice assistants were going to start seeing more adoption in the enterprise. We got this prediction wrong. With most voice assistants sitting on smartphones and inside user's homes, it's clear that voice assistant vendors are focusing primarily on personal and consumer use cases for the system and not enterprise-focused ones. While there are many efforts to push enterprise use of voice assistant technology, the recent announcement of Microsoft's pull back on use of Cortana and the lack of greater enterprise emphasis on the devices leads us to wonder if enterprise voice assistant use will continue to stagnate in 2020.

That said, media analytics firm ComScore says that by 2020, 50% of all searches will be voice searches. With such an increase in searches done through voice, rather than web or text-based searches, this is going to change the way that users engage with content and information. Users are beginning to demand instant, accurate results while also accepting a limited availability of choice. This will open opportunities for forward-thinking companies to be early adopters in embracing voice search and learning how to get to the top results. With the expected increase in voice-based searches in 2020, businesses of all sorts will grapple with the fact that the voice assistants will be mediating the opportunities they have to reach customers. This will pose challenges to companies who have so far used the power of web-based search to increase their visibility with customers.

Throughout the past few years AI adoption has continued, with just about every single industry finding use cases for AI. As such, we don't expect to see the momentum, excitement or adoption slow down any time soon. We're excited to see what 2020 has in store regarding AI.

Next Steps

Check out these IT ops predictions for 2020

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What do you believe is in store for AI in 2020?
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I have a question:
Organisations which have data are more inclined to develop these models or hire AI/ML resource on T&M basis to build their own model, which might not go on cloud for others to reuse.
Third party might not have well trained models in the absence of real data. However they can have models for small use cases.
What about the accuracy?

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