Algorithmia’s 2021 Enterprise Trends in Machine Learning report features the views of 403 business leaders involved with machine learning initiatives.

Diego Oppenheimer, CEO of Algorithmia, says:

“COVID-19 has caused rapid change which has challenged our assumptions in many areas. In this rapidly changing environment, organisations are rethinking their investments and seeing the importance of AI/ML to drive revenue and efficiency during uncertain times.

Before the pandemic, the top concern for organisations pursuing AI/ML initiatives was a lack of skilled in-house talent. Today, organisations are worrying more about how to get ML models into production faster and how to ensure their performance over time.

While we don’t want to marginalise these issues, I am encouraged by the fact that the type of challenges have more to do with how to maximise the value of AI/ML investments as opposed to whether or not a company can pursue them at all.”

The main takeaway is that AI budgets are significantly increasing. 83 percent of respondents said they’ve increased their budgets compared to last year.

Despite a difficult year for many companies, business leaders are not being put off of AI investments—in fact, they’re doubling-down.

In Algorithmia’s summer survey, 50 percent of respondents said they plan to spend more on AI this year. Around one in five even said they “plan to spend a lot more.”

76 percent of businesses report they are now prioritising AI/ML over other IT initiatives. 64 percent say the priority of AI/ML has increased relative to other IT initiatives over the last 12 months.

With unemployment figures around the world at their highest for several years – even decades in some cases – it’s at least heartening to hear that 76 percent of respondents said they’ve not reduced the size of their AI/ML teams. 27 percent even report an increase.

43 percent say their AI/ML initiatives “matter way more than we thought” and close to one in four believe their AI/ML initiatives should have been their top priority sooner. Process automation and improving customer experiences are the two main areas for AI investments.

While it’s been all good news so far, there are AI deployment issues being faced by many companies which are yet to be addressed.

Governance is, by far, the biggest AI challenge being faced by companies. 56 percent of the businesses ranked governance, security, and auditability issues as a concern.

Regulatory compliance is vital but can be confusing, especially with different regulations between not just countries but even states. 67 percent of the organisations report having to comply with multiple regulations for their AI/ML deployments.

The next major challenge after governance is with basic deployment and organisational challenges. 

Basic integration issues were ranked by 49 percent of businesses as a problem. Furthermore, more job roles are being involved with AI deployment strategies than ever before—it’s no longer seen as just the domain of data scientists.

However, there’s perhaps some light at the end of the tunnel. Organisations are reporting improved outcomes when using dedicated, third-party MLOps solutions.

While keeping in mind Algorithmia is a third-party MLOps solution, the report claims organisations using such a platform spend an average of around 21 percent less on infrastructure costs. Furthermore, it also helps to free up their data scientists—who spend less time on model deployment.

You can find a full copy of Algorithmia’s report here (requires signup)

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Algorithmia specialises in artificial intelligence operations and management. The company is backed by Google LLC and focuses on simplifying AI projects for enterprises just To Get Started.

Diego Oppenheimer, CEO of Algorithmia, says:

“Organisations have specific needs when it comes to ML model monitoring and reporting.

For example, they are concerned with compliance as it pertains to external and internal regulations, model performance for improvement of business outcomes, and reducing the risk of model failure.

Algorithmia Insights helps users overcome these issues while making it easier to monitor model performance in the context of other operational metrics and variables.” 

Insights aims to help enterprises to monitor the performance of their machine learning models. Many organisations currently don’t have that ability, or use a complex variety of tools and/or manual processes.

Operational metrics like execution time and request identification are combined with user-defined metrics such as confidence and accuracy to identify data skews, negative feedback loops, and model drift.

Model drift, in layman’s terms, is the degradation of a model’s prediction power due to changes in the environment—which subsequently impacts the relationship between variables. A far more detailed explanation can be found here for those interested.

Algorithmia teamed up with monitoring service Datadog to allow customers to stream operational – as well as user-defined inference metrics – from Algorithmia, to Kafka, and then into Datadog.

Ilan Rabinovitch, Vice President of Product and Community at Datadog, comments:

“ML models are at the heart of today’s business. Understanding how they perform both statistically and operationally is key to success.

By combining the findings of Algorithmia Insights and Datadog’s deep visibility into code and integration, our mutual customers can drive more accurate and performant outcomes from their ML models.”

Through integration with Datadog and its Metrics API, customers can measure and monitor their ML models to immediately detect data drift, model drift, and model bias.

(Photo by Chris Liverani on Unsplash)

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