Presented by Provectus
In today’s rapidly evolving business landscape, incorporating artificial intelligence (AI) has become a critical strategy for companies seeking a competitive advantage. But despite its value, developing and implementing effective AI solutions can pose significant challenges that demand substantial resources and expertise. That is where Managed AI comes in.
Managed AI is an end-to-end service provided by third-party managed service providers (MSPs). It empowers businesses to efficiently develop, deploy and manage AI/ML solutions that deliver ROI faster and at scale, without the need for internal expertise.
Managed AI is an essential service for businesses looking to drive growth and gain a competitive advantage by adopting AI solutions.
Managed AI offers many advantages
Managed AI stands apart from traditional Managed IT Services, landing somewhere between general MSP services and cloud AI services like ChatGPT. Despite its perceived simplicity, Managed AI differs from ordinary Managed IT Services in that it requires unique niche expertise in AI adoption and ML development. Managed AI offers an intricate combination of service and technology, enabling businesses to build enterprise- and cloud-grade-quality AI/ML solutions in-house, without capital investment into the organizational infrastructure.
Managed AI has enormous potential for saving businesses time and resources. By outsourcing AI workload management to a third party, businesses can focus on their core operations and leave complex AI/ML tasks to the experts, ensuring that AI/ML products and services are developed and deployed much faster and at a lower cost.
Managed AI gives businesses convenient access to top-tier AI talent. Rather than building an in-house AI team, companies can leverage the latest AI technologies and expertise without having to scale AI’s steep learning curve. On-call professionals, many with PhDs, are ready to help customers with their questions or concerns. Customers can easily tap into a pool of engineers with a variety of skills, including data and infrastructure engineering, and data science.
Managed AI gives businesses the flexibility to scale their AI operations up or down, based on demand. It is a viable alternative to investing in expensive IT infrastructure and hiring AI specialists. This provides companies with the resources they need, when they need them, without building and managing an in-house AI team from scratch.
Real-world applications of Managed AI
Managed ML models ensure a smooth user experience in applications and environments where data volumes are perpetually increasing.
Consider Earth.com’s EarthSnap, an application designed and built as a sophisticated image recognition system that enables users to quickly identify plants and animals using pictures from a mobile phone. The amount and variety of images processed by the application requires a sophisticated AI solution.
Earth.com lacked expertise in the AI/ML field, but they had a clear vision for the app. They hired third-party consultants to research and validate the concept, which was successfully achieved. To make the solution production-ready, and to scale and maintain it, Earth.com sought a Managed AI service to continuously improve its image classification ML model. Thanks to Managed AI, the solution progressed from proof-of-concept to production deployment in just three months. This rapid progression would have been impossible for a company lacking expertise in AI/ML development.
The core value of Managed AI for startup businesses is the enhancement of products with AI solutions, to ensure best-in-class customer experience and quickly gain a competitive edge without large capital investment.
Managed MLOps is another essential component of Managed AI, especially for enabling AI experimentation at scale. A large multinational food, snack and beverage corporation was looking to accelerate its AI transformation by introducing various AI use cases organization-wide. However, prior to using Managed AI services, the CPG giant had to deal with enormous amounts of data from various sources worldwide. The data was handled by separate data engineering and data science teams, which made it difficult to effectively generate business insights, while juggling different batch jobs and physical GPU boxes.
The Managed AI MLOps platform provided them with a collaborative environment for data scientists to conduct experiments with greater flexibility and convenience, which accelerated development and expanded applications for the team. They were able to access a team of professionals who continually evolved and improved Managed AI services as needed, dramatically streamlining ML workload management. This was accomplished without significant capital investment in MLOps, while providing the customer with a cost-effective solution.
Managed AI for Enterprise businesses accelerates the adoption of AI enterprise-wide, to achieve operational efficiency, discover new markets and create new data-driven products.
Managed AI vs in-house development
When it comes to developing and deploying AI/ML solutions, companies have two options: build or buy. The build scenario requires companies to develop and manage their AI solutions in-house. The buy scenario involves outsourcing AI workload management to a third-party Managed AI service provider. Both options have their pros and cons.
In the buy scenario, companies can save time and resources. They can easily outsource AI work to experts while focusing on their core business operations. Managed AI provides a shortcut to the features and benefits of mature organizational AI stages, such as responsible AI and embedded innovation culture.
However, one potential drawback of the buy scenario is that companies may have less control over their AI solutions. Outsourcing AI workload management means that a third-party will be responsible for the development and management of the AI solution. This can raise concerns about data security, data privacy and compliance with industry regulations.
On the other hand, in the build scenario, companies have more control over their AI solutions. By developing and managing AI solutions in-house, they can tailor solutions to their specific business needs and requirements. They maintain complete control over the security, privacy and compliance aspects of their operations.
However, the build scenario can be expensive and time-consuming. Companies may need to invest in expensive infrastructure, hire new employees with specialized skills and train current employees to use new technologies. The development and deployment process may take longer than outsourcing to a third-party provider, which can impact a company’s ability to deliver solutions to the market quickly.
There is no one-size-fits-all solution when it comes to AI adoption and ML development. Companies should carefully consider the pros and cons of buy vs build. While outsourcing to a third-party can save time and resources, and provide access to top-tier AI talent, the tradeoff is less control over AI solutions. Conversely, developing and managing AI in-house may provide greater control over solutions, but it can be expensive and challenging, both business- and technology-wise. Ultimately, the best option depends on the company’s specific business needs, resources and expertise.
Managed AI is the future of AI workload management. It provides companies with the necessary expertise, resources and flexibility to develop and deploy AI/ML solutions, faster and at scale. If you want to stay ahead of the curve in the AI space, Managed AI may be one of the best solutions to achieve your business goals.
Dmitrii Evstiukhin is Director of Managed Services at Provectus. He leads a decorated team of experts who deliver cloud-based solutions to Provectus’ clients and partners. Prior to joining the Managed Services team, he held a Senior Solutions Architect position and was responsible for designing, building and implementing advanced solutions in the cloud. Dmitrii is passionate about leveraging the latest technologies, encompassing cloud, data, AI/ML, and analytics to help businesses achieve their goals.
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