Only roughly one in four companies moves past proof of concept and gets tangible value out of their solutions. Reasons vary from adjustment costs and organizational readiness to training data availability and quality – and, of course, limited AI expertise and skills.
Securing this expertise is easier said than done — unless you outsource an AI team, that is. A shortage of in-house expertise is the second most common roadblock for AI implementation, after all.
Here’s how to leverage project outsourcing to secure the AI expertise you need.
3 Reasons to Outsource an AI Team
If you remain somewhat unconvinced about the benefits of outsourcing your AI team, let’s make the case for it.
Expand Your Talent Pool
When you hire AI experts locally, you’re limited by the talent pool present in your country. Outsourcing, whether nearshore or offshore, gives you access to the global AI talent pool.
Besides the availability itself, hiring locally can get quite expensive. An in-house data scientist in the United States receives a median total pay of $164,447 per year. Multiplied by 1.25x to 1.4x to factor in extra costs like payroll taxes, it comes down to around $99-110 per hour. If you decide to outsource to Poland, however, you’ll have to pay $75-96 per hour on average.
Speed Up Time-to-Market
Filling a senior data scientist position takes 70.5 days on average, globally. This can make for a lengthy preparation stage for your project as you risk spending months assembling the team.
In contrast, outsourcing companies in Ukraine and other popular destinations already have a pool of vetted developers ready to work. So, they can put together a team in a matter of weeks instead of months.
Easily Scale Your Team
Adding a new hire to an in-house team can take a few months. Outsourcing, however, allows you to expand your team much faster as your partner has experts ready. You can also quickly decrease the team’s size when you don’t need extra developers.
Key Applications of Custom Software in Renewable Energy
When outsourcing AI tasks, you’ll need to put together a team with relevant AI expertise. Here are the six roles your AI team may need.
Machine Learning Engineers
Machine learning (ML) engineers are the ones building and training ML models. These models can be used to automate business processes or, in the case of predictive analytics models, make forecasts based on historical or real-time data.
Throughout the project, ML engineers usually:
Develop and fine-tune ML models
Configure and manage cloud environment resources
Select and implement data preprocessing techniques
Test, monitor, and continuously optimize deployed ML models
Required skills:
Proficiency in Python, Java, C++, R, and other programming languages
Proficiency in ML frameworks like TensorFlow and PyTorch
Experience with cloud platform configuration for ML model deployment
Understanding of core ML model metrics like accuracy and F1 score
Data Scientists
Data scientists work with raw data and figure out a way to turn it into actionable insights. To that end, they:
Extract and clean relevant data
Conduct statistical analysis to identify patterns in the available data
Use cleaned data to create variables (feature engineering)
Choose the best ML algorithms for the given business problem
Develop data models and algorithms
Select the most suitable way to communicate insights
Validate model outputs
Required skills:
Proficiency in programming languages like Python and R
Experience with data analysis libraries like NumPy and Pandas
Experience with ML libraries like scikit-learn, TensorFlow, and PyTorch
Proficiency in data manipulation and statistical analysis
Familiarity with SQL databases and big data
AI Researchers
If your solution fits the definition of cutting-edge, you may need an AI researcher when you outsource an AI team. AI researchers specialize in advancing AI theory and its application to real-world use cases.
As part of the team, AI researchers:
Conduct research in artificial intelligence
Design new AI algorithms, frameworks, and techniques or optimize existing ones
Work with engineers to apply the new research to real-world applications
Required skills:
Proficiency in mathematics, statistics, and AI algorithms
Proficiency in AI methodologies
Experience with AI programming languages like Python and R and frameworks like TensorFlow and PyTorch
Good grasp of the latest developments in artificial intelligence
Software Engineers with AI Expertise
These experts use their programming skills to build enterprise and consumer solutions with built-in AI capabilities. They’re the ones integrating AI features into the software application.
In their daily work, software engineers with AI expertise:
Implement software architecture that supports AI features
Deploy custom-built AI models as part of other applications (together with ML engineers)
Integrate off-the-shelf AI models into end products
Monitor, optimize, and scale AI-powered software solutions
Required skills:
Proficiency in software engineering and its best practices
Expertise in programming languages like Python, Java, C++, and JavaScript
Good grasp of AI and ML frameworks, libraries, and techniques
Experience with cloud computing platforms and API integrations
MLOps Engineers
MLOps applies DevOps principles to ML systems to facilitate their deployment. When you outsource an AI team, MLOps engineers are there to ensure the infrastructure is scalable and effective enough to enable the model’s high performance, stability, and longevity.
The day-to-day tasks of an MLOps engineer depend on the project. They tend to include:
Setting up CI/CD pipelines for ML solutions
Automating model monitoring, training, and version control
Establishing monitoring and alerts to catch issues
Regularly assessing monitoring metrics
Required skills:
Full grasp of the MLOps methodology and CI/CD practices
Proficiency in cloud platforms like AWS and containerization tools like Kubernetes and Docker
Experience with automation tools like Jenkins and GitLab
Experience with system monitoring and logging tools like Prometheus and Grafana
Data Engineers
Data engineers build and manage AI-ready data pipelines to collect, manage, and convert raw data into insights for analysts and other professionals. They work closely with data scientists to develop, test, and maintain data architectures.
As part of an outsourced AI team, data engineers:
Build, test, and maintain databases and large-scale processing systems
Ensure the architecture’s alignment with the business needs shared by the client
Develop processes for data modeling, mining, and production
Integrate systems to ensure a seamless flow of data
Required skills:
Experience with big data technologies like Spark and Hadoop
Programming skills (e.g., Python, Scala, Java)
Experience with data warehousing solutions like Snowflake and Redshift
Expertise in ETL/ELT processes
Proficiency in data pipelines and workflow orchestration tools like Apache Airflow
What AI Projects Can Be Outsourced?
You can outsource virtually any AI project, provided you find a development partner with relevant experience and expertise.
Here are three examples of the most commonly outsourced AI projects:
Predictive analytics and forecasting: ML solutions that identify trends and make predictions based on historical and real-time data. Examples: Stock price forecasting for portfolio optimization and customer churn forecasting.
Computer vision: AI solutions that use ML and neural networks to extract data from images and videos. Examples: image classification for automated content filtering, object detection for defect identification on an assembly line.
AI for process automation: Solutions that combine robotic process automation (RPA) with AI to increase operational efficiency. Examples: intelligent document processing with ML and NLP.
Outsourcing AI tasks isn’t limited to model development and training. You can also turn to a partner for AI model optimization, which can involve:
Retraining the model on better data
Modifying deployment configurations
Improving the source code
Using model pruning and regularization techniques
Finding the Right AI Partner: 4 Factors to Consider
Ready to outsource an AI team? Pay attention to these four selection criteria when selecting a service vendor:
Technical expertise: Check out the programming languages, frameworks, and AI subfields (NLP, computer vision, etc.) the company works with.
Track record: Browse the vendor’s portfolio and reviews on platforms like Clutch. Ensure the company has experience with similar AI solutions deployed to production.
Security and compliance: Verify the company’s experience in complying with applicable privacy, AI, and other regulations. Check their security certifications.
Available collaboration models: Compare your preferred collaboration model (e.g., team augmentation, dedicated team) with the company’s offerings.
When outsourcing to other countries, you should also analyze your regulatory obligations. For example, outsourcing to Canada will require you to comply with the Investment Canada Act.
The location of your AI partner defines compliance obligations, potential cultural and language differences, and the tech talent available in the market. Check out our guide on the best countries for outsourcing for an in-depth comparison.
How We Tackle AI Outsourcing at Integrio Systems
To show how we approach outsourcing AI tasks at Integrio, let’s take a quick look at one of our projects. Here’s how we optimized pathology testing with AI-driven optimizations for Synnovis, a pathology testing service provider for the UK’s NHS.
Synnovis was facing an increase in inappropriate test ordering, duplicated requests, and missed diagnoses. However, it also lacked medical data to train the AI features to solve these issues. That’s when the company sought our expertise to outsource an AI team.
We helped our client secure medical datasets by collaborating with King’s College London (KCL). Our data scientist became a KCL visiting fellow to procure and prepare the data. Thanks to our involvement, the AI model’s accuracy and efficiency substantially improved.
Our team developed and integrated an AI model into the client’s computerized physician order entry (CPOE) systems. The AI features automatically pinpointed unnecessary and duplicated tests, predicted patient pathology test results, and forecasted disease risks.
As a result of our collaboration, Synnovis received an AI-driven solution that improved operational efficiency and enhanced the accuracy of disease risk and test need assessments.
Final Thoughts
Outsourcing an AI team can help you speed up AI adoption, all while allowing you to continuously right-size your team and optimize costs.
That said, finding the right AI partner is no easy feat. You have to ensure the vendor has the right tech expertise and experience with solutions similar to yours. You can’t overlook your security and compliance requirements, either.
Integrio Systems is an AI development partner with 200+ projects under our belt. Fill out the form, and we’ll tell you what custom solutions our team of data scientists and ML engineers can deliver.
FAQ
Assess the vendor’s expertise in AI frameworks and technologies and verify the company has a proven track record in deploying AI systems to production. On top of that, consider the available collaboration models and evaluate the vendor’s ability to ensure the solution’s security and regulatory compliance.
Common risks include:
Subpar solution quality. To mitigate: Establish a quality assurance framework; verify the vendor’s track record.
Misalignment with business needs. To mitigate: Verify the vendor’s communication and business analysis skills.
Security and compliance risks. To mitigate: Check the company’s security standards and approach to compliance.
To create a detailed project brief:
Describe the business problems you aim to solve and provide context
Identify objectives and define KPIs for the project
Communicate your technical expertise and service needs
Set a clear timeline and milestones with deadlines
Provide communication guidelines
Specify your budget and expected collaboration model
Local outsourcing tends to be more expensive and limits the available pool, but you work with a vendor operating in the same regulatory framework. Browsing vendors worldwide allows you to find a better price-quality ratio and gives you access to more talent, but requires navigating time zone differences and legal and regulatory challenges.
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