Machine learning is gaining traction today with more companies jumping on this bandwagon to automate internal workflows and generally improve business intelligence. The article explains why it makes sence for particular companies to leverage machine learning outsourcing and how to get the best value from such an engagement.
Technologies are evolving at a breathtaking pace, with artificial intelligence (AI) and machine learning (ML) revolutionizing the world. These technologies keep drastically changing the way we study, work, consume, as well as shaping our everyday lives.
Machine learning is about to be implemented in almost every company’s processes to automate workflows and improve business results. AI and ML are crucial for the successful operation of marketing, finance, sales, and especially R&D departments. Here are some statistics that perfectly demonstrate the current state of artificial intelligence and machine learning, and their extreme popularity for different businesses.
- By the year 2025, the AI market will become an industry worth $190 billion;
- AI will increase business profitability by 38% and boost economies by generating $14 trillion of additional revenue by 2035;
- 71% of executives consider AI and ML as the major game-changers that will disrupt both exiting business models and the way we interact with techology;
- 20% of C-level executives admit using machine learning or AI as an integral part of their businesses.
As AI and ML are the most advanced technologies known nowadays, more and more companies strive for their implementation. However, not every business has all the resources to hire and maintain an in-house team of qualified experts in this field.
Moreover, ML development requires extensive experience, contextual knowledge, and expertise. Therefore, more and more business owners, startups, and SMBs turn to outsourcing companies. These companies can offer them cost-effective yet quality services, as well as a great pool of accomplished developers and machine learning specialists.
AI outsourcing is a sound solution if you want to implement machine learning technology fast and efficiently. Besides getting effective results, you will receive smooth collaboration, save on recruitment, and cut operational costs.
Below, you can find even more benefits of outsourcing machine learning projects. These facts prove the positives of outsourcing and why it is worth opting for ML development outsourcing over hiring an in-house team.
As you can see from the statistics above, the demand for machine learning technology will keep growing continuously. For successful integration of AI for startups and SMBs, outsourcing is a perfect approach. Rather than hiring a costly in-house team, opt for ML development outsourcing for the following reasons.
High-Level Expertise
Machine learning implementation requires extensive experience and subject knowledge, as this kind of development is extremely complex. Often, an in-house team significantly lacks resources and expertise to tackle this challenging task.
Outsourcing companies offer their clients a larger talent pool. Thus, you can hire the best-dedicated team of professionals to work on an AI/ML project based on your particular requirements.
Smooth and Effective Neural Network Development
Developing neural networks can be time-consuming and challenging. A team of accomplished outsourced experts can make this process much faster and effective.
While developers will work on ML-related tasks, you will not just save time and money but will be able to concentrate on core business functions. Also, an outsourcing team usually has all the needed equipment and software, so you won’t have to spend extra costs on it.
Better Resources To Ensure Accuracy of ML Algorithms
Some projects require complete accuracy. However, machine learning still has some room for errors that outsourced experts, unlike in-house team, can handle effectively. An outsourced team can work thoroughly on creating predictive algorithms to make the most appropriate decisions.
Also, specialist ML outsourcing providers have better knowledge of ML development tools and libraries, which can help you significantly reduce a learning curve for your in-house staff and avoid mistakes that can be pretty costly at post-release stage.
Also, in-house team members usually lack time to input all the data to train ML-based software. Outsourced specialists have all the resources to provide the best services and ensure the desired results.
Enhanced Customer Experience
Machine learning technology is usually used for creating predictive tools that can generate results based on user behavior and habits. Respectively, users receive a better, more personalized experience.
Testing machine learning is essential to ensure the desired results and functionality. However, performing unit and real-time in-house testing will significantly prolong the time of the release of the new product. In this case, outsourcing can noticeably reduce the time spent on testing before the product launch and minimize potential errors.
More Intuitive Analytics and Safety Options
By analyzing all user information, website views, time spent on a particular page, and search requests, outsourced specialists can teach ML-based software to seize behavior analytics. As a machine learns the common behavior, it will be able to spot unusual activity.
In case of abnormal behavior, outsourcing developers will get an alert to block the source of this activity, so the machine can do the same in the future and block such sources accordingly. As a result, a more intuitive user experience is granted, as well as decreased risks of customer data leaks.
Here is a great example of entrusting an outsourcing team with machine learning technology implementation. A renowned UK content media agency has requested the development of an ML-based recommendation engine from a trusted outsourcing provider 8allocate. The agency wanted to penetrate the global market, grow audience, and increase user engagement with the help of an ML-driven recommendation engine.
The software was intended to analyze user preferences and provide them with more accurate recommendations on discovering new content. For creating a top-level recommendation engine, three accomplished data scientists were assigned to work on the project. The team has offered two effective solutions, based on content specifics and user preferences.
As a result, the outsourcing team came up with effective solutions that helped the content media agency to increase product monetization, as well as grow the audience by 40%.
If you decided to implement machine learning for your company or startup, check out these important tips on how to manage an outsourced project. By keeping these recommendations in mind, you will be able to achieve the desired results and ensure the smoothest collaboration during the whole process.
1) Prepare legal documents
Before you start your collaboration, take care of all the legal documents that cover terms and arrangements between you and your outsourcing partner. Sign Non-Disclosure Agreement to protect sensitive data and claim ownership to the ideas and solutions.
2) Start small
It’s highly recommended to start with testing small ideas first. You will still see the results and value for your business that this cooperation delivers. At the same time, it will be much easier for you to manage all the processes and calculate the reduced expenses. This way, you will be able to keep an eye on your dedicated development team and make sure that all the requirements are met.
3) Help the outsourcing partner to understand your idea
To establish successful collaboration and achieve excellent final results, you have to make sure that the outsourcing team fully understands your ideas and business goals. Successful communication, in this case, is a key. Set up constant communication, schedule video chats, clarify all the requirements from the start, and stay in touch in case any additional information is needed.
4) Put safety at the front end
Sharing confidential data with an outsourcing partner is a serious matter. While collaborating with remote workers, make sure you protect all the information. The best way to do it is to use hash values instead of sending unencrypted data.
Provide the outsourced development team with the only information that is needed to get the job done. Instead of sharing live data, you can generate test values that will follow the structure of real datasets.
5) Use proper project management tools
Trello, Taiga, Asana, and Jira are only a few examples of great project management tools that you can use to keep your development team on the right path. Thus, you will be able to assign tasks, specify details, set deadlines, monitor the delivery time, and arrange meetings with your team.
More and more companies want to implement machine learning and artificial intelligence technologies into their business processes. Therefore, extra attention should be paid to the latest approaches in project management, and the most prominent one is outsourcing ML development.
It can be a challenge to recruit and manage an in-house team of ML developers and AI specialists. At the same time, the results that outsourcing partners provide usually appear much more cost-effective, of higher quality, and even the most complex tasks are delivered in time.
Outsourcing is definitely the best solution for machine technologies implementation for AI companies and startups. By outsourcing ML software development, businesses get access to a larger talent pool. Thus, companies can choose the best-dedicated professionals with extensive experience and knowledge in this field.
And what do you think about ML project outsourcing? Please share your thoughts in the comments section below!
Credit: BecomingHuman By: Vik Bogdanov
