Accelerating Growth Through Outsourced Machine Learning Services

Consumers expect quality products and service delivery as standard today, with this market becoming ever more competitive. To mark their supremacy, businesses need to adapt fast to cutting-edge technologies and reap innovations that attract the target audience and lead to profitability. Artificial intelligence (AI) and its non-secular fields have recently experienced a rapid development in every aspect of our lives, from machine learning (ML) and natural language processing to computer vision. However, we observe a clear upward trend in the AI app space, currently valued at $1.8 billion in 2023 and projected to reach over $18.8 billion by 2028. According to the O’Reilly report, “AI Adoption in the Enterprise 2022,” nearly 70% of businesses have implemented or are experimenting with an AI solution.

However, the process of developing these ML features and integrating them into the application presents a unique set of expertise and experience challenges, which many organizations today encounter.

It was only logical to team up with machine learning outsourcing providers when over 94% of companies are already outsourcing IT services. You can start the process and choose the best business strategy. This arrangement will serve to explain the process of outsourced machine learning services.

How Does Machine Learning Outsourcing Work?

Machine learning outsourcing is a practice where companies outsource the development, implementation, and maintenance of machine learning (ML) solutions to specialists or third-party service providers. Rather than developing an org-internal space to manage ML projects, they work with firms or even freelancers in the space and deploy their tools/infrastructure and know-how.

Essential Aspects Of Machine Learning Outsourcing

A variety of services could be involved, such as, but not restricted to:

• Preprocessing and cleaning data
• Development and optimization of the model
• Algorithm implementation and customization
• Validation and testing
• Integration and deployment
• Maintenance and monitoring on a sustained basis
• Models of delivery

Machine Learning Outsourcing Can Be Structured In A Number Of Ways:

Project-based outsourcing: The provider owns an entire machine learning project end-to-end.

Dedicated teams: In this case, the provider sets up a dedicated team of ML experts who work only on your project.

Consulting: Advisory services provided by providers to aid internal teams in developing and implementing strategies.

Platforms and Technologies

Outsourced providers usually use advanced tools, frameworks, and platforms such as TensorFlow, PyTorch, and AWS SageMaker. They can integrate solutions seamlessly with an organization’s existing technology.

How Machine Learning Outsourcing Operates

Companies use machine learning outsourcing to increase productivity, set and achieve new business goals, increase safety, or provide actionable insights. Usually, it includes developing solutions tailored to the client by an outsourcing company. Which process utilizes the latest machine learning algorithms to analyze data and extract actionable insights? As a result, a more effective and efficient application of business operations through this process would lead to better decisions, ensuring profitability.

Key Differences Between AI Outsourcing And Machine Learning Outsourcing

While in general, Artificial Intelligence (AI) and machine learning may seem to be like twins, they do provide subtle differences when outsourcing functions. AI outsourcing is hiring AI specialists or an AI development company to work on more extensive AI projects. Which might include building AI systems internally, securing data from intrusions, or building deep learning models. However, data annotation, data labeling, and ML model construction are also areas of emphasis in machine learning outsourcing.

For companies lacking the funds or expertise to establish their own in-house AI team, outsourcing can be a cost-effective solution. On the other hand, outsourcing machine learning gives you access to a complete team capable of dedicating the necessary time and resources to design and deploy ML algorithms efficiently. Businesses can adopt AI and machine learning outsourcing based on project size and complexity to align with their unique needs and specific project delivery objectives.

What Is The Process Of Hiring ML Services?

The process of outsourcing machine learning services usually includes a couple of very high-level steps:

Identifying Business Requirements and Goals

Be aware of the accurate ML necessities for the company. Identify the concepts that will solve problems, achieve goals, and be part of a business strategy. As an example, organizations might look for ML answers to problems in areas like consumer behavior analysis, fraud detection, or provision chain optimization.

Selecting the Appropriate Service Provider

Choosing the appropriate ML service provider is huge. For organizations, it is important to evaluate potential partners based on their strengths, areas of proficiency, tech stack used, and client reviews. Niche providers generally have more experience across realms as a result, which can yield faster project cycles with all-around higher quality.

Collaborative Planning and Scoping

The two parties work together to define the scope of work after selecting a provider.

This consists of setting out datasets, algorithms, timelines, and deliverables. As part of this process, making sure that communication during this phase is clear to avoid misunderstanding between what the provider can do and what the client expects is very important.

Data Sharing and Security

Organizations have to share data with the service provider in a secure manner. We all implement elaborate information safety measures and protection for regulations such as GDPR or HIPAA, which are specific to our industry.

Model Development and Testing

The ML service provider builds the model as per the agreed scope. By incorporating multiple updates and feedback loops throughout the development process, the resultant solution is always in alignment with what the organization needs. You must conduct extensive testing on your model in real-world scenarios before deploying it, proving that the caliper alone is insufficient.

Integration and Deployment

The organization’s systems integrate the developed and tested ML solution. Providers may also provide assistance with deployment and continuous operation.

Advantages To Gain While Outsourcing Machine Learning Services

Here are the strategic advantages of outsourcing ML services:

Access to Expertise

The knowledge and expertise that ML service providers tend to possess is much deeper than what an organization can create internally. This gives you access to new and innovative strategies.

Cost Efficiency

Investing in talent that is infrastructure and tools needed to build an internal ML team Outsourcing, on the other hand, eliminates such costs and helps companies to reinvest these resources in core areas.

Faster Time-to-Market

Sure, big ML service providers sell cookie-cutter processes and canned tools, which inevitably reduces the lead time for a project. This agility can be the determining factor for sectors where innovation velocity is a prerequisite for competitive success.

Scalability and Flexibility

Outsourcing allows scaling these ML projects up and down easily depending on the changing needs of the organization. Providers can respond more rapidly to changing needs without a lot of internal organizational adjustments being necessary.

Focus on Core Business

An outsourcing solution enables businesses to concentrate on their core business, leveraging some of the most advanced analytics and automation skills available, without the burden of managing the entire ML development process in-house.

What To Consider Before Outsourced Machine Learning Services

Despite the endless advantages, outsourcing comes with some major considerations that businesses should weigh to ensure it works:

Data Protection and Anonymity

Sharing sensitive data with a third party carries inherent risks. Thus, organizations must make sure that the service provider has proper security in place and is compliant with industry regulations.

Provider Reliability

The most crucial factor is the dependability of the provider where outsourcing partners are situated. We should thoroughly vet providers and ensure that contracts clearly outline expectations and penalties for failure.

We structured it in a manner that aligns with the expected business objectives for each product, taking into account the business’s contribution to the KPI.

ML solutions must be in line with the wider organizational goals. It is important that communication and updates remain consistent to ensure alignment during the project lifecycle.

Long-Term Support

Organizations must monitor and update ML models. Then organizations should ensure post-deployment support and maintenance from the provider to fix issues and enhance performance as required during its lifecycle.

Cost-Benefit Analysis

Outsourcing can be cost-effective, but fees for integrations or changes to scope may incur additional costs. You will also want to complete a full cost-benefit analysis.

What Are The Obstacles To Outsourcing Machine Learning?

Sharing sensitive information with outsourcing firms and locating experts with the necessary subject knowledge are two obstacles to outsourcing machine learning projects.

The exchange of data

Under data privacy regulations, like the GDPR or CCPA, sharing your company’s data with third parties can be very tricky and even problematic. If a data breach occurs, businesses could be laden with heavy penalties and also face reputational damage. Although there are currently solutions for this problem, these methods also carry some disadvantages. Check out our articles on federated learning, synthetic data, and data obfuscation for more.

Expertise in the field

If the business objective is too domain-specific, it will be difficult to find experienced people with the required experience. Domain knowledge plays a crucial role in sanitizing raw data from the real world before training ML models, as it can lead to poor performance. Besides the model and the dataset, this processing requires a thorough understanding of the sector and domain of a particular application.

For example, hundreds of developers built COVID-19 diagnosis applications without even realizing the biases in the dataset. Tomography scans of patients who are in recumbency position are usually more severe than those of individuals who have erect posture. This is because individuals who are so sick that they can hardly stand and exhibit signs of COVID-19 are likely to be infected with the virus. This could result in a model incorrectly diagnosing patients with COVID-19, even when they are actually lying down and do not have the virus. If a data scientist had merely known the rules of how hospitals operate, they could have avoided these types of errors.

Conclusion

Outsourcing machine learning services is a business decision that can significantly accelerate an organization. This enables the organization to access specialized providers who leverage their expertise to deliver advanced analytics, minimize expenses, and concentrate on their core competencies. Yet, success also requires strategic planning, due diligence on provider selection, and outsourcing relationship management. If done the right way, ML services can help turn data into an engine for growth and innovation.