Outsourcing machine learning development: Yes or no?

By the end of 2018, the number of businesses using machine learning is expected to double as companies become aware of the actual value of smart technology. Machine learning is capable of solving issues independently without the need for manual instructions. This adaptive nature enables machines to steadily increase their knowledge and avoid system errors. As far as organizations are concerned, machine learning gathers large data sets to glean important insights as the companies refine their operations and tweak the customer experience to achieve a competitive edge. However, machine learning is complex and demands precision and expertise — kind of like making the perfect pumpkin pie, but let’s get back on track here! Thus, outsourcing machine learning development is better than assigning the job to in-house staff who lack the contextual know-how and resources. Check out the list of the reasons why outsourcing machine learning beats hiring in-house staff:

1. Big Data and feedback loop

Outsourcing Machine Learning

As the amount of data increases, so does the need for outsourcing machine learning development. The truth is, your company will never have as much voice data as one of the industry bigwigs like Amazon, nor will you annotate as much information as Google. If you’re working on an app that is particularly data-driven and hardcore, the max you will consider is SaaS, such as facial recognition, natural language processing, and sentiment analysis.

In situations like this where two companies have varying amounts of data, a feedback loop is a practical solution. It allows your customer to avail a suitable solution while you get to develop the best product in the industry. But this is a time-consuming process as it involves testing the SaaS services and gauging the fit of training as well as your data.

In case there is no fit, accuracy will not be off as well (sort of like some wild pitcher in a baseball game — we have all seen in Ricky Vaughn in “Major League”), and there will be no point investing precious resources and time on tweaking a personalized solution.

2. Development expense and timeframe

Outsourcing machine learning

Machine language professionals are not readily available, which means hiring one full time can prove to be expensive for many businesses.

Even at half the cost of development, the game isn’t going to change much. And if you’re considering a machine language library, know that you will still be required to update, scale, and orchestrate the solution as per your needs. This is where SaaS services step into the picture; not only do they offer solutions that can be quickly implemented, but they are reasonably priced as well.

3. Improves the customer experience

Big data and feedback loop

Machine learning can be used to predict future customer behavior on the basis of previous habits. Intelligent machines process large sets of data, and teach the software how to offer suggestions and tend to individual characteristics. If a business hires in-house employees for ML development, it will require rigorous testing to ensure that the device is processing the information being fed into the network.

The two most commonly used techniques for machine learning technology include unit testing as well as real-time testing as a front-end user. Irrespective of the preferred testing techniques employed by a business, a lot of effort is necessary to perform these examinations, which only increases the time needed for the items to enter the market.

Outsourcing machine learning development is a better idea so businesses can prevent time limitations leading to testing problems.

4. DevOps and infrastructure

From the start, scaling is a concern for businesses. The execution time of machine learning is greater than a regular database query, which means building solid architecture with a continuous feedback loop requires time and effort.

Normally, a deployed machine learning model has a stack including TensorFlow, Docker, Kubernetes, as well as a cloud provider, such as Amazon Web Services. The process involves developing a cloud-based scaling cluster, complete with a GPU-powered annotation and training tool on demand. On the contrary, using your personal hardware is never a wise choice.

5. Streamline decision trees

Machine learning uses Classification and Regression Trees (CART), simple algorithmic systems, at the time of decision-making. Known as decision trees, these trees use two main techniques for sorting through the available information.

The trees can either determine future values predictively, known as Regression, and organize and classify details (Classification). Starting off at the root level, decision trees branch out until they come to the final decision at the lower section.

The condition of the internal node determines how many branches are there on the tree and whether it needs trimming for attaining logical decisions fast. By outsourcing machine learning, the external team can limit branch splits and determine accuracy following every alteration. However, it might be a salient idea to establish a maximum depth for the tree so that you can put a stop to the decision process after a certain point

Once the decision tree comes close to the conclusion, the pruning process starts. When there is additional staff available, it provides extra time for both cost-complexity pruning and reduced error pruning. Upon completion of both processes, the machines in the workplace become a lot more reliable since they now focus on the key details and choose the best options.

6. Encourage intuitive analytics

DevOps and infrastructure

Contracted employees can set the machine to record behavior analytics by processing the available user details, search queries, the duration of time spent on a site, web page views, and so on. Since the machine algorithms are capable of picking up habitual behavior (yes, they can find out if you smoke or not and what type of shoes you enjoy purchasing), the systems can spot problematic behavior faster.

As soon as the development team gets alerts for unusual activity, they shut off the source to avoid any fraudulent behavior. So, the frontend experience tends to be a lot more intuitive, protecting vital customer information and making the organizations less vulnerable to hacking.

Machine learning nowadays forecasts upcoming trends and drives business. So, when by outsourcing machine learning development, you are technically gauging the data cleansing and scaling of these devices, which implies the way technology processes big data and enables machines with powerful algorithms to operate more intuitively.

Now if you could just get your cat to behave better that would be a real treat, right!?

Reasoning for outsourcing machine learning development

The smart thing to do would be to assign your in-house employees to focus more on primary objectives while your outsourced teams teach machines. By outsourcing machine learning development, the ROI for intelligent device input exceeds customer satisfaction as the machines will improve security, prevent operational problems, and create an innovative environment to make things easier for the development teams.

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