Machine learning (ML) outsourcing is hiring an external agency to delegate the development and maintenance of ML-enabled applications. ML gives advantages for companies when it comes to problem-solving independently, refining customer experience, gathering large sets of data and giving valuable business insights, and tweaking business operations that can put the company into a competitive advantage.
Why your company should outsource machine learning tasks
Machine learning (ML) used to be exclusively about productivity and cost. However, with the evolvement of technology, experts have predicted that the use of machine learning in business operations will be twice as much compared to the past years as businesses recognize that machine learning helps companies save greatly in terms of costs and labor.
According to the statistics, the global Machine Learning market is expected to increase ten times in the next seven years.
Here are some considerations with regard to the benefits and risks of machine learning outsourcing and tips to further assist you in choosing the right partner.
By hiring ML and AI outsourcing companies to develop forward-looking solutions, businesses can achieve a significant competitive advantage. ML-driven applications can process large amounts of information and provide data-driven answers. The most popular applications of machine learning are:
- text and image recognition
- data classification
- anomaly detection
ML-driven applications can turn large data sets into useful insights that help optimize business processes, boost sales, increase customer satisfaction, etc.
Benefits of Machine Learning Outsourcing
ML helps businesses increase their performance, achieve new business goals, improve safety, or get useful insights. Let's learn more about the top-four benefits of machine learning.
High-Level Data Scientists
Implementing machine learning solutions involves broad subject knowledge and extensive experience for its complex developments. Therefore, having high-level scientists work on ML projects is necessary. However, having in-house developers may be expensive, whilst teaming up with service providers that cater to your requirements, especially in data science projects, is beneficial and easier.
Time Saving and Cost-Effective Development
Neural networks perform a lot of complex tasks, such as categorizing information, decision-making, and having the ability to make future predictions based on data. These things can be quite challenging and time-consuming. Hence, it is beneficial to have an outsourced team of developers that can speed up the process of neural network development. Having outsourced partners allows for a quicker building of teams and scaling them up once needed. It is also a fact that experienced outsourcing companies have the necessary exemplary equipment and ML software, which lets you save from extra costs on it.
Better Resources to Provide ML Services
Having outsourced teams work hard is vital when it comes to creating predictive algorithms for decision-making. In-house team members may lack data science tools, especially when handling errors involving machine learning. Outsourced teams deem very helpful as they have access to a wide range of libraries and tools for collecting, exploring, preparing, and visualizing datasets.
Effective Data Management and Safety
ML and AI outsourcing developers for machine learning projects are helpful when it comes to securing sensitive information with regard to your company's data. Aside from that, implementing proper and systematic management, organization, and data storage on various platforms is well taken care of when handled by experienced professionals.
HOW TO OUTSOURCE MACHINE LEARNING
The machine learning outsourcing process implies four crucial stages.
1. Requirements Definition
Uncertain or vague requirements can lead to the unsatisfying quality of deliverables or increased budgets. Therefore, it's required to define your needs clearly. Analyze your internal processes that can be improved with the help of a new ML-driven solution.
Create a detailed list of the ML-driven software and features you need to share with machine learning outsourcing companies for further estimation. We recommend creating a list of business problems and prioritizing them. Try to discover how they can be solved with the help of machine learning.
2. Outsourcing Provider Research and Selection
Finding an experienced vendor to start an outsourced machine learning project is key to success.
In case you don't have a reliable tech partner yet, feel free to choose the three most popular options.
- Use a search engine. Feel free to specify the “machine learning outsourcing company” to find the most popular agencies on the Internet.
- Ask your connections. Reach out to your friends, partners, or colleagues, asking to share any references.
- Explore trusted B2B listings. Use the most popular websites like Clutch to find top-rated agencies.
- Check out freelance websites. Use popular sites like Upwork to explore the most successful agencies that help develop ML-enabled applications.
Create a list of top-rated agencies to review them in more detail. Feel free to check reviews, projects completed, and workflows to find the best vendor to hire. Also, get in touch with the representatives of selected firms and share all the requirements. It will help business analysts estimate your project.
3. Project Initiation and Progress Monitoring
Sign an agreement with a selected and initiate a project.
Share all the documents with an agency and discuss all the details. We recommend sharing the following:
- product vision
- technical requirements
- training and testing data sets
A hired agency will initiate a project, onboard the required experts, and manage them.
In order to monitor the progress, we recommend scheduling regular one-a-one sessions with the representative of a company. Alternatively, a company that outsources artificial intelligence services can prepare progress reports and share them daily or weekly.
4. Testing and Maintenance
Quality assurance (QA) specialists involved by an outsourced machine learning development company. They can help ensure that all the features developed by software engineers work properly. However, it's also advisable to conduct acceptance testing. Double-check the functionality delivered to be ensured that it works as designed.
Besides, it's vital to support your product. Clarify is a vendor that offers post-development support to keep your ML app up and running. Otherwise, don't hesitate to find an external agency with the required expertise to troubleshoot unexpected problems and improve your solution.
MACHINE LEARNING OUTSOURCING VS IN-HOUSE TEAM
Hiring a team of in-house ML experts is the main alternative to machine learning outsourcing. Let's compare the two options by reviewing the key factors that impact the decision.
||Machine Learning Outsourcing||In-House ML Developers|
|Cost||High hourly rate. ML outsourcing is a more affordable solution in the long run.||Lower hourly rate. In addition, businesses need to establish a workspace, cover scouting & hiring expenses, employee referral bonuses, etc.|
|Access To Tech Talents||Instant access to a large pool of skilled developers with hands-on experience.||Challenging talent scouting because of the significant shortage of ML specialists. Businesses are required to conduct interviews, access the skills of candidates, and onboard selected specialists|
|Scalability||A hired vendor can rapidly allocate a different number of tech talents to a project.||A new hiring campaign should be launched to hire new in-house ML experts.|
|Upskilling||Machine learning outsourcing agencies spend resources to upskill ML software engineers.||Additional resources should be invested to train in-house team members so that they can grasp new skills.|
|Flexibility||Outstanding flexibility. External vendors can involve tech experts with different expertise to solve new tasks.||External tech-strong experts should be involved when the in-house team members don't have the required expertise.|
|Management||No management hassle. The hired agency is responsible for managing all the processes.||A dedicated project manager should be hired to run a team of in-house software engineers.|
|Workflow||An agency has a tried-and-tested ML-enabled app development workflow.||An in-house team of ML developers should create a new product development workflow from scratch, coping with many bottlenecks and unforeseen issues.|
|Involvement||Business owners can have scheduled meetings with the representatives of a machine learning development company and receive reports regularly.||Complete involvement in all the processes. An in-house team can be supervised meticulously.|
|Maintenance||A business can request an outsourcing agency to maintain and update an ML-driven app upon request.||A business should keep in-house team members to maintain an app.|
|Security||Internal data should be shared with a third-party organization.||All sensitive information is processed by in-house team members.|
|Domain Expertise||An outsourcing agency has access to a large number of tech experts with the required domain expertise.||A business should assess the skills of applicants to discover if they have industry-specific skills and knowledge.|
|Data Analysis||A hired vendor can analyze and prepare data sets to train and test ML models when outsourcing data science activities.||Dedicated data analysis experts should be hired to prepare data sets.|
|Tools Knowledge||An agency has access to many software engineers with hands-on experience in using a wide variety of ML tools.||Tools knowledge should be assessed in advance. Also, additional training may be required.|
It's required to involve various experts in different stages when developing an ML-driven digital solution. For instance, data analysis experts, software engineers, and QA experts should be involved in different phases of the ML app development process.
An ML and AI outsourcing agency can allocate the required resources for every stage of ML-enabled software development to spend resources wisely. By comparison, in-house team members should be engaged in all the stages, even when minimum involvement is required.
OUTSOURCED MACHINE LEARNING STATISTICS
Let's explore the global tendencies to understand if it is worth outsourcing the development of ML-driven solutions.
Machine learning adoption is growing steadily at a high pace. The ML market is estimated to grow roughly 10x times from $21 billion in 2022 to $210 billion in 2029.
According to the report prepared by Statista, the increased demand for ML experts leads to a severe shortage of machine learning specialists. 82% of organizations cannot satisfy their demand for machine learning experts.
ML-driven digital solutions can help businesses achieve different goals. The top-five use cases of machine learning applications are:
- improved customer experience
- customer insights generation
- enhanced communication with customers
- fraud detection
- improved long-term customer engagement
According to Glassdoor, the yearly salary of an ML specialist in the US ranges from $103,000 to $168,000. The average annual salary is $131,041 in 2023 and is expected to grow due to the talent shortage.
Risks of Machine Learning Outsourcing
It's vital to consider the top risks when adopting an ML-driven technology to avoid bottlenecks and budget losses.
One of the most common risks is a communication gap. Broken lines of communication, especially in case outsourced staff is off-site, might be a probability. However, once communication standards have been established among teams in the company, conflicts may be prevented.
Exposing sensitive organization data is one of the risks all companies need to be prepared for. In particular, it is the case when outsourcing or delegating tasks to contain private materials. Thus, it is very important to make sure the provider of services you'll choose for handling sensitive data is trustworthy and reliable. Also, make sure to lay out security measures that each party agrees on for the safety of data.
Project Management Failure
Outsourced teams or machine learning consulting companies have a tendency to take on many different projects for different businesses, which can often lead to errors and delays in delivering requirements. It is important to establish a detailed timeline and agree on a reasonable deadline to avoid such conflicts. It is also crucial that you plan meetings regularly to be updated on the project's progress.
Lack of Domain Knowledge
Good quality models in any project rely heavily on domain knowledge. If there's no adequate domain knowledge, feature engineering, as the key component of ML implementation, is at high risk of becoming a failure. It is important to make sure that specialists in the business niche field are present to avoid conflicts in the project.
How to avoid risks of ML outsourcing
The majority of machine learning outsourcing issues can be tackled with the help of recommendations listed below in the post.
Choose the Right Provider
It's key to understand whether the company you are choosing for its services has an ample amount of experience in delivering data science projects that match your industry niche and if it works well with the data storage systems that you currently have.
Sign an NDA
Having legal documents that clearly state the terms and arrangements between your company and your Ml and AI outsourcing partner is important. It is vital to sign a non-disclosure agreement so that sensitive data is protected and make sure to claim ownership over the ideas and solutions to avoid any legal problems in the future.
Explain Main Project Goals and Ideas
To ensure the success of your company's collaboration with your chosen outsourced team, it is important that you are both on the same page when it comes to ideas and business goals. Constant communication is a key player, and it is best to have a regular schedule for meetings and an open line in case you need clarifications during the project.
Safety is the Most Important
Even though it is a collaboration, you should always be careful with the data and information you're sharing with your outsourced development team. Provide the information essential to the project to avoid safety conflicts in the future.
Keep Abreast of a Project
Management tools are very important in the success of your project. This also plays an important role in making sure that conflicts are prevented. By using project management tools, you can assign tasks, specify details, set deadlines, monitor work progress, and set up a meeting with each other.
Machine learning development is one of the most prominent and effective approaches to business processes. That's why companies opt to implement ML and AI technologies in their businesses. However, there are challenges in terms of recruiting and managing an in-house team of ML developers and AI specialists, as it is more expensive and time-consuming.
Outsourcing partners for machine learning consulting appear to be more cost-effective while delivering a higher quality of work compared to in-house teams.
With that said, outsourcing machine learning is a smarter choice for machine technologies implementation, given that outsourced teams have more tools, larger talent pools, and professionals with extensive experience and knowledge in the field of machine learning development.
In order to outsource an ML-driven application development, you should take the following steps:
- Define requirements. Analyze problems that can be solved with the help of an ML-enabled solution. Define business goals to achieve.
- Find an outsourcing company. Explore the Internet, trusted B2B listings, and other sites to find top-rated agencies. Assess selected companies to find the best vendor.
- Initiate a project. Sign an agreement, define the scope of work, and share all the required information to begin an ML-driven app development.
- Test and maintain. Create a communication schedule with a project manager to stay updated about the progress. Thoroughly check all the deliverables.
The primary benefits of Machine Learning outsourcing are:
- Access to top-tier outsource data scientists
- Time-saving and cost-effective development
- Better access to tools and resources
- Effective data management and safety
The market grows at a high pace. According to statistics, roughly 82% of businesses experience a shortage of Machine Learning specialists. Hence, delegating the development of ML-driven solutions to an external company is the most popular solution.