How to Outsource Machine Learning Development Successfully?

Advantages of Machine Learning Outsourcing

According to the World Economic Forum Insight Report, AI and ML specialist jobs will grow most within the next five years. The market will expand by around 40%, surpassing sustainability specialists and business intelligence analysts. The trends align with the job outlook by the US Bureau of Labor Statistics, which predicts a rise in ML and AI jobs and a decline in software engineer positions within five years.

Since timing is critical to business success, companies cannot afford to wait for colleges to catch up and address the growing demand for ML specialists. So, outsourcing seems like the most natural option, coming with many advantages other than finding a needed professional fast. 

1. Save up product development time

With the ability to outsource machine learning development, you can save some time on finding the in-house team. Although you will still need to find a team to outsource your development, you will put all the effort into finding the best one that understands your business needs. To add, if the team is on the same page as you are from the start, each iteration will be conducted saving up time here and there, which could have been used to resolve miscommunication issues.

2. Save a lot of product development costs

Having a project to outsource does not mean that you have an extensive budget. So, you need to find yourself an outsourced team that would not cost you all the money. Unfortunately, in-house developers might be the wrong choice. According to Indeed, the average yearly salary of US-based ML engineers is $161,471 ($69.13 per hour). Glassdoor shares similar numbers—$165,454 per year or $70.86 per hour. Since most complex projects require a team of ML specialists to work together, the development budget can cut into revenue and worsen yearly projections. 

In contrast, outsourcing ML specialists’ salaries are significantly lower, reducing the strain on your budget. Here are the average yearly rates for ML engineers across popular outsourcing destinations:

3. Prioritize valuable product development labor efforts

Via outsourcing ML development you can leave your in-house team to do some other and more valuable work. For instance, there are always tasks that require urgency. And, if you have successfully outsourced the development team, then your in-house colleagues might be getting other things done on a bigger scope project. Sounds rational.

4. Economize on product development resources

Among the benefits of outsourcing machine learning development is the possibility of the machine learning outsourcing provider making use of his/her resources on the project. As outsourcing developers is always done overseas, clients cannot always supply the team with hardware and software. So, the team will use their own resources. And the price for them will be quite low. You may even get to choose what is going to be used.

5. Mitigate Risks

ML algorithms handle large data sets, including sensitive customer and business details. It’s risky since data breaches, corruption, and loss can disrupt daily operations, cause legal repercussions, and lead to reputational losses. 

Trustworthy ML outsourcing partners can take over data security. Experienced teams know the latest compliance requirements, implement cybersecurity best practices and ensure your data is backed up. Their disaster recovery management plans guarantee your business can restore normal operations quickly and with minimal losses in case of natural or man-made disruptions.

Machine Learning Outsourcing Models

While going for ML development outsourcing, you can choose between three cooperation models:

• Staff augmentation means introducing individual machine learning specialists to your in-house development team to share relevant technical expertise and accelerate the project timeline.
• A dedicated team works alongside your in-house development team, typically under a separate project manager or team lead. It works exclusively on your project until it’s complete. 
• Product development implies hiring a team of ML engineers, quality assessment specialists, project managers, and a team lead to take over all the development tasks, from discovery to deployment. 

How do you pick the right model for your project? Use our checklist to make the right choice:

1. How big and complex is your project? Project-based outsourcing is best for well-defined projects with limited timelines and scope. Staff augmentation and dedicated teams can work on large-scale and complex projects for extended periods.
2. Do you have an in-house development team working on the project? If so, opt for staff augmentation or a dedicated team, depending on how much help you need. If not, go with project-based outsourcing.
3. Do you want complete control over the development process? If so, choose team augmentation. A dedicated team is a better option for controlling the process without micromanaging. If you’re ready to trust your outsourcing vendor, go with a project-based option.

Risks of Machine Learning Development Outsourcing

But besides advantages and benefits, there are a few risks you should also know about. For instance:

1. Communication

If you consider outsourcing machine learning, the time zone can be quite an issue. It’s ok when the time difference equals 2-3 hours. But, some countries have a 5 or 8-hour difference. What’s then? There is always a chance you will be unable to communicate with your team and schedule meetings not compromising their or your working hours. 

You can mitigate this risk by replacing real-time communication with effective real-time progress reports to assess ongoing changes. Once effective communication channels are established, you can limit your face-to-face calls to once or twice a week at a time suitable for both time zones to get the necessary updates yet not disrupt the workflow. 

2. Insufficient domain knowledge

Some machine learning service provider companies can be relatively young. And, even if they say that they can carry out a project for you, you should check their domain knowledge. Here are a few steps to help you avoid inadequate ML expertise on your outsourcing team:

• Check if their case studies are relevant to your industry, use the needed technologies, and address your goals, team size, timeline, and budget.
• Look for customer testimonials from past projects on third-party platforms, like TopTal, Upwork, LinkedIn, etc.
• Study individual developers’ CVs and portfolios to assess their training, experience, and expertise.

Ideally, it’s better to set up technical interviews with outsourcing ML engineers—but that would involve getting specialist help, which would take extra time and add to your development budget. That’s why we recommend taking this step if you’re working on a large-scale, complex project with a big budget.

3. Safety issues

Do you trust your outsourcing partner? Is the company reliable? Are there any safety issues? Most of your project information is sensitive and confidential. So, you need to make sure the company with machine learning outsource service complies with all the safety regulations. Maybe, check whether they have such security and safety certificates as ISO 27001 or GDPR. Companies that promote safety always engage in all the certification and regulation measures.

4. Poor management of a project

Even if you found the right team, be cautious. There’s a chance your potential partner is a young inexperienced machine learning service provider that hasn’t refined its processes yet. This means, there might be poor management on your project. Experienced teams set your cooperation details and discuss them from A to Z. The ones that have no idea what to do only mess things up and try to put a process together. The latter may lead to money and time losses.

How to Find and Hire the Right Outsourcing ML Development Provider?

To obtain only ML outsourcing benefits, you should:

1. Understand your business needs

The first and the utmost rule is to be 100% aware of what you need to achieve via ml development. Based on these characteristics you can initiate your search for an ml development team. But if you lack certain knowledge and have no idea what the team has to carry out, then you might make a big mistake and outsource not the right team. A product you wish to produce has a lot to say about your potential team’s experience, skills, use of technology, and more.

2. Search for a trustworthy ML service provider

Second, always search for your potential partner via trustworthy resources. For example, Clutch, GoodFirms, LinkedIn, local media sources, or business partner recommendations.

3. Be on the same page with the team

Third, if your team understands the concept of your project 100% – this is the right team. Mutual understanding is of great value. If the team catches your project intentions from the start the whole process of development will be filled with easy communication, top quality features, and little to change.

4. Initiate testing

Forth, every project needs to be tested. Some initiate testing from the start, the others implement it after the design stage. What does this stage give you? It allows to be sure the application works fine and it won’t crash for no reason. So, your team has to have quality assurance engineers as well.

Consider Inoxoft Your Trusted Partner in ML Development

Inoxoft is a machine learning development company providing personalized and convenient solutions for our clients. We are delivering top AI and ML solutions in Education, Healthcare Fintech, Real Estate, and logistics industries. Our team’s expertise speaks louder than words. Get to know our tech stack and tools!

With ML and AI solutions you can scale your business and:

• Reduce unwanted human error
• Experience automation and better decision-making
• Get personalized software per your needs
• Obtain an innovative product

Here’s one example of how an AI-powered solution can bring business value to any industry.

AI-Powered Sales Assistant by Inoxoft

Inoxoft helped an Israeli-based startup leverage natural language processing (NLP) and machine learning to create a sales assistant for B2B interactions. Our team implemented an AI-based Chrome extension for Gmail and G Suite. 

Once launched, the extension started analyzing email threads and identifying their score (negative or positive), studying the keywords and phrases, and hypothesizing what caused the clients’ responses. Moreover, the sales assistant extension suggests tips for follow-up emails to prompt customers to purchase. Further development may incorporate real-time sales call analysis by adding a speech-to-text module.

With the assistant, sales managers can analyze email threads and write customized responses faster. Its efficiency helps to boost sales and increase revenue, regardless of the industry. 

Final Thoughts

Outsourcing ML development is the most viable option for businesses interested in implementing ML and AI solutions given the lack of Machine Learning (ML) engineers. It helps companies reduce costs, accelerate development, and focus on generating business value instead of developing in-house expertise. Thanks to multiple cooperation models, effective project management, and communication practices, you can mitigate potential risks and achieve your business KPIs.

In 10 years, the Inoxoft team has completed over 230 projects, including several successful AI and ML implementation cases. Our engineers are skilled in building AI-powered career mappers, art data platforms, and predictive models (for AdTech companies). We employ AI/ML consultants, data engineers, and AI model developers. Whether you work in healthcare, finance, education, retail, or any other industry, we can help with natural language processing, predictive modeling, AI-powered customer support, and more!

Contact us today to schedule a consultation with our analysts and ML experts.