Machine learning is the best method of data analysis. It also automates the creation of analytical business models. This is the reason why machine learning plays an important role in the growth of a business. Hence, your business will probably need new and highly inspired ideas to deploy machine learning solutions into your business. However, the implementation of machine learning can bring several challenges.
To identify the deployment challenges of machine learning applications, businesses require to have a complete understanding of the current ML technologies and the current advancements.
Here, I will be listing the challenges of machine learning implementation for startups and how to overcome them.
1. Challenges With Model Deployment
To identify the model deployment challenges in the implementation of ML applications, businesses require industry experts with a deep understanding of the current AI / ML technologies. Or, hire machine learning developers to deal with the issue of lack of compatibility between the machine learning model and business workflow that impacts the deployment of ML applications.
Machine learning models are commonly built using languages such as Java, C, Python, R, and SQL. For example, there have been lots of advances in the Facebook ability to detect faces or Amazon’s Alexa ability to recognize specific voice command. Specifically, business firms must ensure that they should get answers to such questions:
What machine learning model needs to be updated to your business profile?
What data usage pattern does your business have?
What are the right development algorithms for your machine learning model?
For the successful deployment of machine learning, businesses need to have a proper understanding of the data flows, algorithms, and how they can be applied to different processes. For companies with machinery & equipment, machine learning provides a platform to predict proactive measures and possible failures in the production department. The specific algorithm needs to be observed to characterize the normal operation state.
2. Ethical Challenges in Machine Learning
The ethical challenges in machine learning involve the issues related to how the data is used. There have been certain instances of racial biases in the machine learning programs that are also affecting the implementation of ML technology unintentionally.
Here is the example:
Similarly, while talking to a customer care representative it is becoming hard for people to understand whether they are talking to a human or a machine. This simply makes the ML solution a tough technology to implement.
A famous case of racism happened two years ago in which the mislabelling of two African-American young guys by Google facial recognition software, which had classified the two teenagers as Gorillas.
Google has faced a lot of criticism, and people starting to wonder whether a machine intelligence could be trained to be racist on purpose. The actual reason for the mislabeling is not because of racism, but the actual cause of the error lies in the application training set.
Developers need to make more transparent ML decisions with respect to ethics and user behavior. Your technical team needs to collect enough of the data with regards to ethical measures to appropriately train ML applications. It is important because different situations require different ethical approaches and system should be designed according to their goals and behaviors so that ML development technologies should benefit and empower as many people as possible.
3. Challenges Related to Data Collection and Storage
Data acquisition and storage is a real challenge in the ML implementation. Just look at the example, a healthcare project was aimed to cut their costs for the treatment of patients with pneumonia. So, they have implemented machine learning to sort out the patient records to see who’s at high risk of infection and should be there in the hospital, and who is at the low infection risk from pneumonia.
Still, they can’t make sense of the data records, hence, the machine learning will be of no use or perhaps even useless for them. So, what’s the right way to handle such challenges?
It’s true that establishing the right data collection mechanism is perhaps the hardest part for you to manage. Users go for ML technology for predictive analytics and the first thing to do is to combat data fragmentation.
For instance, in Travel tech data fragmentation is one of the main issues. Wherein hotel knows the guests' credit card details, home addresses, contact number, and other details. This data get transferred to different departments within the hotel. So, it’s not always possible for the hotel team to converge all data streams.
Well, that can be harmful to the guests. A large amount of data and missing values in data can reduce prediction accuracy and that’s wrong-headed. Thanks to machine learning, the approximated & predicted values it provides are considered as “more correct” for a software algorithm.
The points described here are basic and straightforward. However, you still need machine learning expert if you need to solve the method of data acquisition & storage mechanisms, set the complete infrastructure, and look for complex machine learning tasks.
4. Cost-Related Challenges
Smart businesses understand the possibility to rely on data-driven decisions in their business activities. And, lots of data mean lots of storage. So, how can this stuff turn useful and how much does it cost? In many cases, the cost analysis of the options is required to make an informed decision.
Let’s see the steps involved in developing a custom machine learning model and how the cost may vary for each step.
Requirements: The requirement phase is all about understanding what your business needs from the model. We recommend you to have clear ideas on what you want for your business because a too vague idea will make the cost explode.
Data: You can consider data as the experience for your business model. If you train the business model with a lot of good quality experience, it will learn better how to solve our purpose. The cost for this process can be zero if your data is model ready, otherwise, it should take some investment. And, if we talk about big datasets to be managed with a cluster the cost can go on the thousands.
Model: It’s always important to evaluate your business model to benchmark the cost effectively. It also takes several days to evaluate & choose the algorithm, train the business model, test it, and implement. This is the core part of your business process and it’s really not easy to decide on its cost.
Production: Once your algorithm is tested and is ready for production, there are two main activities to manage:
- Integration with your existing business setup.
- Updating the business model that may need to learn from newly available data or to include new features.
Both are not mandatory for your business, so also here the cost may go from zero to some thousand dollars.
So, the ML algorithm and deep learning development work are the main factors to look for. It’s true that performance rate depends on the client’s business objective and the cost of data predictions. Machine learning projects need time to achieve better results. Even if you are lucky and your ML algorithm matches up with the benchmarks immediately, chances are your program will work efficiently or will get completely failed.
Hence, continuous monitoring can only protect your ML model from degradation, but things will improve with time. Also defining your business metrics, planning technical architecture at the earliest stage decides the success or failure of your ML venture.
Hopefully, it was a really deep dive for you into the world of ML implementation for business startups. The key lies in minimizing the challenges and creating more benefits through the adoption of ML development that give your business the core capabilities of machine learning.