Rapidly transforming several sectors, including healthcare, banking, retail, and entertainment, machine learning (ML) has transformed others as well. ML is a true game-changer with its capabilities to provide predictive analysis, tailored experiences, and automation.
With such tremendous potential, however, comes a specific set of obstacles in deploying machine learning solutions. If these obstacles remain unaddressed, an ML project may experience delays or even derailment. Whether your work involves an in-house team or outsourcing to a machine learning consultancy, awareness about such obstacles is the key to a successful ML journey.
1. Data Quality and Quantity
Data is among the most crucial components of any machine-learning effort. Effective operation of machine learning algorithms depends on plenty of high-quality data. Most companies have unorganized, inconsistent, and incomplete data, not in a form for analysis.
Challenge: ML algorithms rely a lot on large datasets representing the problem domain effectively. In case one doesn’t have access to high-quality, relevant, and clean data, even a high-tech model can generate no useful output. Besides, factors including data bias, outdating, and noise can generate unbalanced output and poor model performance.
Solution: To mitigate this challenge, companies have to invest in sound processes for collecting, cleaning, and preprocessing in a manner that sets the stage for use with machine learning algorithms. It is also a good practice to have a service provider for machine learning and/or a consultant assess the quality of the data and make recommendations for its improvement.
For businesses looking for machine learning consulting firms, experienced consultants can evaluate gaps in information and make recommendations for its collection, cleaning, and enrichment.
2. Choosing an Appropriate Algorithm
With a variety of algorithms at one’s disposal, selecting an appropriate one for a problem can become daunting. The algorithm should be compatible with the problem type—classification, regression, clustering, or forecasting a time series, for instance.
Challenge: For most companies, algorithm selection complexity can cause unnecessary testing and trial and error, wasting valuable resources and time. Besides, companies lack in-house expertise in knowing which algorithm will work best for a specific use case.
Solution: Companies can derive value from working with companies offering machine learning services with in-depth expertise and acquaintance with numerous algorithms. An experienced ML consultant will select an algorithm best suited for a project’s objectives, availability of information, and the issue at hand. In addition, an experienced consultant will have a consideration of accuracy, scalability, and interpretability when proposing an algorithm.
3. Model Transparency and Explainability
Sometimes, machine learning and deep learning algorithms can become opaque and difficult to understand. That is, even when such an algorithm generates high accuracy, at times it can become difficult to comprehend both how and why a model reached a specific conclusion.
Challenge: For industries including healthcare, finance, and law, interpretability and transparency become critical. Stakeholders have to trust model-made decisions, most importantly in regulated sectors. Transparency can instill hesitation in deploying machine learning technology and even generate legal and ethical concerns.
Solution: To address this, companies can go for options in machine learning with model interpretability as their first preference. There are alternatives, such as XAI, which can deliver transparency regarding how a model reaches its conclusion. It is very important for fields that require clear explanations for rules or ethics to have a model that is easy to understand and explain. This can be achieved by working with a machine learning consulting firm.
4. Overfitting and Underfitting
A model that learns a training set too precisely—including randomness and noise not generalizable to new, unseen examples—is said to be overfit. Conversely, underfitting results from a model lacking sufficient sophistication to detect broad trends in the data.
Challenge: Most data engineers and data scientists often struggle to balance between variation and bias. Both overfitting and underfitting provide poor generalization and reduce the efficacy of an ML model in practical settings.
Solution: Additionally useful for model parameter tweaking, feature selection, and model complexity determination is consulting a machine learning business with experience optimizing models.
5. Scalability and Deployment Challenges
A successful model not only must function effectively with training data but also must also have the ability to scale for use in real life. As a company grows, its machine-learning model must expand with it. Having a model with a high capacity for dealing with a lot of information and providing real-time forecasts is imperative for operational success.
Challenge: Deploying a model at scale can present a challenge in terms of hardware, pipeline, and integration with existing infrastructure and systems. For instance, real-time inference over large datasets can require high computational demand or cloud infrastructure.
Solution: Choosing a scalable machine learning platform is important. Having a consultancy develop our infrastructure and integrate a model with the existing infrastructure of a business can be an important part of that. A cloud platform for machine learning, such as Google Cloud, Amazon Web Services, or Microsoft Azure, can provide options for high-scale infrastructure deployment at a high level.
6. Model Retraining and Model Maintenance
Machine learning algorithms are not a technology that can be set and forgotten. Over a period of time, with new information emerging, they can become outdated and require constant maintenance and retraining in a continuous quest for accuracy and effectiveness.
Challenge: As environments become updated, models can become less predictive in case they’re not retrained with new information periodically. Organizations face a challenge in developing an effective feedback loop for model accuracy over a period of time.
Solution: To address such a problem, companies will have to have a robust model retraining and monitoring pipeline in place. Model performance testing over new information must become a part of the development pipeline. By working with providers of machine learning services, companies can have processes for model drift tracking and retrain models when necessary.
7. Cost and Resource Planning
Implementing a machine learning project can become expensive. With hiring the proper talent and investing in infrastructure and computational powers, expenses can soon run out of proportion. SMEs and even companies in general, in fact, face an issue with funding and manpower for a successful ML project.
Challenge: The development cost for machine learning can be high, and in case of poor planning, companies can face budget overruns. There is a risk of not having in-house expertise to manage an ML project effectively and thus encountering delays and mismatches in expectations.
Solution: Partnering with a consulting service with expertise in machine learning can alleviate cost and resource strain. Experts in consulting can provide expertise, and resources can be effectively utilized with expenses kept under budget. On top of that, cloud platforms for model development and hosting can save infrastructure costs.
Conclusion
While machine learning is full of many positive aspects, it is not problem-free. By being aware of and overcoming common stumbling blocks such as data quality, algorithm selection, interpretability, scalability, and dealing with resources, companies can make their ML projects a success.
Consulting with an experienced machine learning service provider or consultancy can yield the expertise to navigate such stumbling blocks and produce meaningful, fact-based insights. By overcoming such stumbling blocks, companies can access the full potential of machine learning and utilize it to drive innovation, make wiser choices, and deliver sustained growth.
sidhu mane 
Shirlley Williams