The global machine-learning market was valued at $19.20 billion in 2022.
In 2030, it is expected to be worth $225.91 billion.
This is a fabulous number, and the fact that we are using more AI tools than ever in our daily lives reminds us that this number can explode. However, hiring experienced machine learning professionals is expensive. According to BuiltIn, a machine learning engineer’s average salary in the US is between $150k and $160k. This is out of reach for many small businesses that could really use that expertise.
Building an in-house machine learning team isn’t an option for them. The best solution is to outsource the work. Outsourcing ML projects can offer several plus points, but it also requires a clear understanding of when and how to make that decision.
That, in itself, is a task.
We’re here to help. This article explores the reasons for outsourcing machine learning projects and guides you through the process.
What is AI Ops?
The term was coined by Gartner. The operational processes related to building, deploying, and maintaining AI models are called AI ops or Artificial Intelligence in IT Operations. The simplest way to define it is: using AI to automate and streamline IT operational workflows. This could include tasks like data collection, training machine learning models, performance monitoring, and/or ensuring the smooth integration of AI systems into business operations.
AI ops tech uses Machine Learning (ML) and Natural Language Processing (NLP) to improve IT operations. AI Ops is also responsible for ensuring that AI models work as expected and deliver value. Any performance issue is reported to DevOps.
How is AI Ops related to Machine Learning?
AI Ops (Artificial Intelligence for IT Operations) and machine learning projects are related but different. AI Ops uses AI and machine learning to automate and enhance IT operations. It applies AI to manage and monitor complex IT environments, helping teams detect anomalies, predict potential issues, and automate routine tasks.
Machine learning projects involve building models that can learn from data and make predictions or decisions without being explicitly programmed. These projects could be focused on a wide range of areas, such as image recognition, recommendation systems, or predictive analytics, across various industries.
In short, AI Ops uses machine learning to improve IT operations, but “machine learning projects” refer to a broader spectrum of applications beyond IT operations.
Why Should You Outsource Machine Learning Projects?
Outsourcing machine learning projects is a realistic and practical solution for companies lacking resources and expertise. This is why outsourcing makes sense:
1. Cost-Effective
Hiring and maintaining a full-time AI team is a significant financial commitment. With outsourcing, you can work with the same level of skilled professionals at a fraction of the cost, providing financial security. It lets you focus on your core business while leaving the technical work to experts.
2. Access to Global Talent
Outsourcing gives you access to a wonderful global pool of AI talent. You can find highly qualified professionals with the right skills for your machine learning projects without the need to hire locally. This can speed up project delivery and reduce the risk of failure.
3. Scalability
As your business grows, your AI needs will increase. You don’t need to hire and train staff continuously if your projects are being outsourced. You can handle ups and downs in your business better, and the increased flexibility is healthier for all ops in your processes.
4. Faster Time to Market
Outsourcing gives you faster results because your partners are usually well-versed in specific areas of AI (and implementation) and can deliver projects quickly. If you’re working with a hiring guide (like InCommon), you can work with teams tailormade to handle your specific needs. This helps you stay ahead of competitors.
When to Outsource Your Machine Learning Projects
Outsourcing isn’t always the correct answer.
What is your current AI budget? Can you work with a remote team, however skilled? What are your goals for the future? These are questions you have to ask yourself to make the best decision.
These are the situations in which outsourcing is always the best answer.
1. Limited In-House Expertise
Outsourcing can fill the gaps if your team needs to gain the necessary skills to handle complex AI projects. Machine learning requires specialized knowledge, including data science, model training, and deployment. Outsourcing infuses processes with the expertise needed when your internal team isn’t equipped to manage these tasks.
2. Budget Constraints
Businesses start with limited budgets.
Full-time AI professionals are expensive, especially if your company is starting with AI. Outsourcing removes the financial burden of hiring and training new employees, making it an excellent choice for smaller companies. Bonus: the professionals you work with are equally (if not more) skilled than their local counterparts
3. Project Complexity
Some machine learning projects are highly complex, even if you have an in-house AI team. You might need more expertise than is available to you immediately. Also, niche skills or tools may not be available in-house. In such cases, outsourcing to specialized firms can provide access to cutting-edge tools and techniques.
4. Time Sensitivity
All work is time-sensitive. Outsourcing can improve project quality overall when delivering on a comfortable deadline. It can also speed up the process if you have a tight deadline. External teams often have the infrastructure and experience to deliver projects quickly, reducing the conceptualization to implementation timeline.
5. Need for Flexibility
Outsourcing continuously improves flexibility. Outsourcing is a good choice if your AI needs are project-based or fluctuate over time. You can bring in experts temporarily without the long-term commitment of hiring full-time employees. You can now scale your AI ops up or down based on project demands.
How to Outsource Machine Learning Projects
You’ve decided to outsource your AI ops. Now what?
How can you make sure that the outsourcing process goes smoothly? Here’s how:
1. Have Crystal Clear Objectives
Before anything else, clearly understand what you want to achieve. Define your project goals, timelines, and expected outcomes. This will help you communicate your needs clearly to the outsourcing partner and ensure that both parties are aligned. It will avoid unnecessary misunderstandings and help keep professional relationships healthy.
2. Choose the Right Outsourcing Guide
Use a legit outsourcing guide. It’s possible to do it independently, but it is stressful and time-consuming. Most outsourcing guides have done this multiple times and will intuitively know what kind of AI/ML team will be best for you. They will seek partners with experience in AI ops and machine learning projects. Check their track record, client testimonials, and past work to ensure they have the expertise to handle your project.
3. Start with a Pilot Project
Before committing to a large-scale project, consider starting with a smaller pilot project. This allows you to assess the outsourcing partner’s performance, work quality, and communication style. A pilot project can help build trust and confidence before you proceed with larger projects. Your guide will be the best one to advise you on this.
4. Establish Clear Communication Channels
This will be the responsibility of your outsourcing guide, but you must also be proactive about it. Good communication is vital to successful outsourcing. Set up clear communication channels and agree on regular check-ins to monitor progress. Use the best project management tools and collaboration platforms to stay updated on the project’s development.
5. Ensure Data Security
Machine learning projects use sensitive data. Before outsourcing, make sure your data is protected. Sign non-disclosure agreements (NDAs); ensure the outsourcing partner has security measures. Data protection should be a top priority to avoid potential risks.
The best outsourcing guides will communicate these measures before outsourcing begins.
6. Be Realistic About Deadlines
Don’t rush the project: quality AI models require time for training, testing, and fine-tuning. Work with the outsourcing partner to set achievable deadlines that have room for thorough development processes.
Outsourcing speeds up delivery times, but speed must not come at the cost of work quality/durability.
7. Measure Performance and Results
Once the project is complete, measure its performance against the initial objectives. Evaluate the AI model’s quality, accuracy, and impact on your business. This will help you assess whether the outsourcing partnership was successful and whether to continue outsourcing in the future.
Work with your outsourcing guide on this. You must be on the same page as your guides for results that satisfy you, your clients, and your partner.
What Are The Benefits of Outsourcing Machine Learning Projects?
Outsourcing AI ops can significantly boost your business when done correctly. Here are some of the key advantages:
1. Reduced Costs
Outsourcing reduces the need for hiring, training, and maintaining an in-house AI team. This significantly cuts down operational costs, especially for small and medium-sized businesses. You free up your resources, and you can reinvest them in your company again.
2. Faster AI Integration
Outsourcing allows you to integrate AI into your business processes quickly. External teams often have the tools and expertise to deliver machine learning models faster, allowing your company to implement AI-driven solutions in less time.
3. Access to Better Technology
Outsourcing partners often have access to the latest AI tools and technologies. By working with them, you can leverage state-of-the-art machine learning techniques that might be too costly or complex to implement in-house.
4. Focus on Core Business
By outsourcing AI ops, you can focus on your core business functions. This allows your team to concentrate on what they do best while the external team handles the technical aspects of your machine learning projects.
How can an Outsourcing Company Help with Outsourcing Machine Learning Projects?
The right outsourcing guide and vendor can transform how the entire project is handled.
They will source skilled professionals with expertise in data science, AI, and machine learning from global markets. Additionally, they can facilitate communication between the client and the offshore team and align project goals, timelines, and expectations.
They provide comprehensive infrastructure support, handle payroll and legalities, and alleviate the client’s administrative burdens. The management and delivery of machine learning projects will be smooth sailing, so the client can focus on strategic objectives while the outsourced team manages the technical aspects.
Conclusion: Scaling AI Ops with Outsourcing
To begin with, choose the correct outsourcing expert and discuss your objectives with them. In most cases, this is all you need to do. The best guides will be able to give you options, strategies, and solutions that suit your specific machine-learning needs.
By understanding when and how to outsource, businesses can make smarter decisions about their AI strategy and leverage the power of machine learning to drive growth and innovation.
For everyone needing help deciding where to go after this, InCommon is here to help.
Our hiring experts are happy to discuss your needs and work with you to create solutions that benefit your business. AI/ML is a significant investment, and outsourcing helps take the burden off your pocket without adding any worries to the “quality of delivered work” plate.
The correct path is right here.
