Real-time Challenges of Machine Learning Projects

This article was published as a part of the Data Science Blogathon.

Introduction

Machine learning projects can be extremely challenging in the IT industry. Several factors can make them difficult, including the volume of data that needs to be processed, the complexity of the algorithms involved, and the need to ensure that the systems are accurate and reliable. In addition, machine learning projects can be time-consuming and expensive to develop and deploy. The challenges of machine learning projects in the IT industry can be daunting but also very rewarding.

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This blog will be divided into two sections; the first part, i.e., Challenges or pain points, and the second part, which is a Solution to overcome the desired set of challenges. It will give a super lens that could be applied to your current project and organization.

Disclaimer: The below-discussed points mainly focus on consultant organizations, not in-house ML companies.

Challenges of Machine learning Projects in the IT Industry

1. Data – Though we are in the era of data evolution. However, this is still one of the biggest pain- points for many ML and DS people in many organizations. The first challenge is often in finding the right data to train the machine learning algorithm. The data needs to represent the problem you are trying to solve, and it can be difficult to find enough data to cover all the possibilities. How many of you can relate to a situation where you have been asked to develop something without giving you any data? All the business stakeholders have fancy ideas and thoughts about machine learning and artificial intelligence. They ask ML developers to come up with some implementation without any clarity about the data or data elements. This is one of the real challenges, especially in new growing industries or startups.

2. Connecting bridge –  This is frequently another puzzle component that is overlooked. While ML experts have their own preferred method of working on a given use case, business folks have their notions about certain use cases and their outcomes. And frequently, it is rather challenging for people to communicate their views to one another. Additionally, the technical portion is tiresome and uninteresting to comprehend for businesspeople. How can businesses overcome the challenge of a lack of machine learning knowledge in their decision-makers? The challenge for businesses is the lack of knowledge in machine learning among business people. Without this knowledge, businesses will not be able to take advantage of the opportunities machine learning presents.

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3. Production -As per the Gartner report, 87% of ML projects like prediction modeling, forecasting, and recommendation system never goes into production, and there are different reasons and factors. Machine learning projects can be challenging to manage in a production environment. Several factors must be considered when deploying a machine learning model into a live system. One of the biggest challenges is ensuring that the model is accurate. The model may be used in a production environment to make critical decisions, so it must produce accurate results. Another challenge is ensuring that the model is stable. The model may be used frequently and under various conditions in a production environment. The model must perform consistently under these conditions. A third challenge is ensuring that the model is efficient. The model may need to handle a large volume of data in a production environment. The model must be able to process this data quickly and efficiently. Finally, it is important to ensure that the model is secure. The paucity of resources for these ML operations and the lack of a standardized process for ML model deployment are making the issue worse.

Solutions for the Machine Learning Application

1. Keep asking the relevant questions in client calls for the data required to build the particular defined use case.
2. Make the problem statement or use case very clearly and dig every outcome.
3. Practice narrating stories using simpler language. The company stakeholder’s interest will be maintained in this way. Always remember the fundamental rule that money comes from profits, not company ideas. Businesspeople are, therefore, always highly valued in any organization.
4. Always make your story around the points that business people are interested in. This will give weightage to your ML project. Even though it is difficult for them to absorb, they will be keenly interested in the results.
5. The right education and training for business people from time to time will help in overcoming the lack of ML knowledge in business people or decision-makers
6. Make a proper plan for deployment and add the resources needed to make the project live.
7. There is a wide variety of ML ops tools in the market. They provide long-lasting benefits like model versioning, lineage, and packaging, to name a few advantages. Make maximize of them. My favorite is ML flow.

Conclusion

In the rising field of AI, where Technologies and platforms are constantly evolving, it can be difficult to keep up with the latest trends. It is still challenging to make ML projects and deploy them into production. Constant efforts and setting up of organization in 360view by involving technical and business people as decision-makers could change the situation in the coming years. Educating and training about ML areas with tools like no code or low code platform for businesspeople can improve things a lot easier and better. There has been a lot of buzz lately about the benefits of advanced machine learning (ML) algorithms in the IT industry. By utilizing these algorithms, businesses can improve their operations and decision-making processes. Some of the benefits of advanced ML algorithms include the following:

1. Increased accuracy and efficiency in data analysis
2. Improved customer service through the use of predictive analytics
3. More effective fraud detection and prevention
4. Greater insight into market trends and customer behavior
5. Enhanced automation of business processes

Lastly, finding real-time use cases appealing to business and storytelling are the two important factors that would play a key role in finding a better solution in this industry.

I hope you enjoyed reading it. Thank you.

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