What are Managed Services in AI?

How AI Companies are Unlocking Value with Managed Services Providers

Deploying a well-performing AI model is a complicated, resource-intensive task, so it’s no surprise companies are turning to managed services for assistance. Generally speaking, managed services refers to any end-to-end white glove service. A company can partner with a managed service provider (MSP) for all or part of a given operation via managed services. This concept is now frequently being applied to AI, as organizations seek guidance and assistance from third-party vendors to launch quickly and successfully.

Managed Services in AI

The Growing Need for Managed Services in AI

Organizations are experiencing a fundamental shift in how they must operate to maintain a competitive edge, and much of that involves adopting the latest technologies like AI. However, the vast majority of AI projects fail to reach deployment due to challenges around shortage of skills and expertise, barriers to acquiring sufficient high-quality data, and a myriad of other factors. Even the largest of companies with the most resources often partner with third-party vendors to help manage parts of their AI workflows because the fact remains that AI development is very complex and difficult to succeed in. It requires significant cost, highly-specialized skills, efficient pipelines, and many other organizational resources to deliver on its ROI.
More than ever, managed services can be a welcome solution to organizations competing in the AI field, whether they’re struggling to launch at all or simply looking for greater efficiencies in operations. As AI moves from a fringe offering to a core component of the modern business, expect to see more diversified managed services offerings across the entire AI value chain.

What Services are Most Commonly Used in AI?

When it comes to AI specifically, managed services can be used for one or many of the components of the AI lifecycle. Here are a few examples:

Data Collection and Annotation

MSPs like Appen can handle the end-to-end data preparation pipeline, including the collection and annotation processes.

Model Development

An MSP, like our partners at Silo AI, Provectus or Pariveda, can offer support during the model development phase of the AI lifecycle. This includes managing key workflows during the build process.

Security

For security, MSPs can offer security tools for protecting data and ensuring compliance. Providers may also help identify key vulnerabilities in existing security protocols and identify fixes.

IT Support

Managed IT services can either serve as full IT support for a team, or take on the more routine tasks so the IT team can focus on more complex issues. This is especially helpful considering IT talent remains a critical gap in the technical space.

Analytics and Monitoring

AI models require continuous monitoring on key metrics even after deployment. Outsourcing this function to an MSP enables companies to focus less on day-to-day analytics and more on big-picture needs.

The above list is by no means exhaustive, but gives a sense of the wide range of managed services available within the AI space. Almost every component of AI development can be outsourced in this manner.

The Benefits of Managed Services

There are many benefits to using managed services in AI:

Efficiency

When implementing AI fully in-house, you take on the full burden of collecting data, annotating it, building a model, testing it, deploying it, and continuing to monitor it. A managed services provider can help you automate and create pipelines for these processes, meaning you don’t need to expend resources reinventing the wheel every time you need more data or want to launch a new AI initiative.

Speed

Managed services providers have ready-made tools and expertise to get AI models off the ground and running faster. Working with one helps companies remain competitive with quick turnarounds and ultimately scale much faster.

Filled Talent Gaps

According to Appen’s State of AI 2021 Report, the talent gap in technical skills persists, particularly in specialized skills needed for AI development. Doing the work in-house could be prohibitive due to this shortage; seeking expertise from a managed services provider can help fill the gap.

Improved Quality

Sourcing data on your own is challenging, time-consuming, and can lead to biased datasets (and ultimately biased models). A managed services provider can offer quality controls and data management tools for greater accuracy and consistency in your models.

Reduced Brand Risk

Building AI sometimes carries risk for the organization, especially if it publicly fails in production. Working with a managed services provider minimizes this risk, because they are more likely to know what pitfalls to look out for and have tried-and-true processes and tools for implementing AI to minimize risks in production.

Working with an MSP can help new entrants to the AI field launch their first products, or assist older entrants with maximizing operational efficiencies. In other words, many companies can benefit from this type of partnership.

How to Select the Right Managed Services Provider

With many MSPs to choose from, it’s useful to have a rubric of sorts to refer to in selecting the provider that’s right for your company. Here a few important factors to consider:

1. Identify gaps and match them to offerings.

Evaluate the needs of your organization, including any gaps or areas where greater efficiency may be needed. Find an MSP whose offerings match your requirements.

2. Look at industry experience.

A good MSP will be able to demonstrate their level of experience, including areas of expertise, previous clientele and case studies, and overall market presence. Request to review references from previous clients.

3. Ask about certifications.

Depending on what your requirements are, you may want an MSP who holds specific certifications. For instance, if you’re concerned about data security, look for a provider that possesses information security certifications such as ISO 27001.

4. Understand support levels.

Ask questions on what kind of support the provider will offer you: how often will they be available for routine or urgent matters? What is their response time when issues arise?

5. Agree on a flexible contract.

It’s very likely you’ll need to add or remove services as you get into the weeds on an AI project. Ensure the contract between you and the provider is flexible to these types of changes.

Expert Insight from An Appen Expert

Complete and accurate instructions, or guidelines, are key for quality data. Guidelines drive the understanding and application of accurate ranking/labeling to AI data. An MSP can help develop robust guidelines to ensure the crowd interprets correct intent, removes personal bias and applies judgments accurately. Strong, easy-to-apply guidelines can reduce noise in data, ensuring your AI model performs as expected.

Getting to high quality data can be a journey. MSPs can partner with your team to develop specific, measurable quality metrics that drive AI performance. These metrics can vary based on task type and desired outcome. In all cases, using multiple quality metrics and continually iterating on targets will drive refinement and accuracy of AI data.

Spend time getting the task UI right. The correct task design can help drive quality and also save cost. A well-designed UI should provide needed context, such as access to instructions or intent research, while also eliminating extraneous content that can cause confusion or slow down a rater’s thought process. AI can be applied to task designs to streamline steps in the process like data collection, randomization of results and pre-labeling.

Invest in the people working on your data and quality will follow. Having a crowd with the right background and competency is key to data quality. Language expertise, cultural knowledge, education and experience are all factors in data quality. Consistent engagement and feedback are critical to build expertise and drive continuous improvement of metrics and data quality.

What We Can Do For You

Appen provides the most diverse, scalable data labeling options to help you achieve the level of quality your AI team demands. With our advanced AI-assisted data annotation platform, we offer managed services for all of your data needs. Operating with over 25+ years of expertise, we’ll work with you to optimize your data pipeline efficiency to its maximum.

Working with us gives you instant access to our global crowd of over 1 million contributors who speak 180+ languages and dialects to support your efforts across markets. We’ll also connect you with our data scientists and machine learning experts who have real-world expertise to help you design and create world-class AI, and ultimately deploy with confidence.

To learn more about our managed services offerings, contact us.

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