With the recent boom in Big Data and data scientists, ML is being used to provide business decision-making insights by analysing large amounts of data. Algorithms and statistical methods are combined to train systems to make predictions and provide key insights.
This is the era for digital transformation and companies are becoming more digitally advanced leading to a better world with technology. But these fast paced technological implementations come at a cost. There are various aspects that has to be taken into consideration before adapting these.
Here are some of the challenges of ML:
- The complexity of data will be a major challenge in ML adoption. Currently, there is a growing need for fast-paced solutions and with this, data sets will also grow 10 fold.
- If there is bias in training data, there can be a bias in predictions. And ML models can augment existing biases and amplify them while presenting the data. This is not intentional. This will then lead to outcomes that are unfair and prejudiced. It can happen in applications including hiring, lending, and criminal justice.
- There is a huge dependence on high-quality, relevant, and diverse datasets for ML models. If the data is inadequate or biased, it can lead to inaccurate models. However, it is difficult to get huge, representative datasets for certain types of tasks.
- In order to be effective, ML Models have to be re-calibrated time and again. Keeping ML Models up-to-date with the rate of data distribution and evolution is a difficult task. In order to adapt to the changing environments, continuous learning mechanisms are key.
- Also, since ML models requires high computation, memory & also high speed storage, the implementation of data becomes sluggish, and data analysation will also require more time.
- Lack of transparency can be a significant hurdle in gaining trust and understanding the model’s behaviour. Complex ML models like deep neural networks, are considered “black boxes”, as it is challenging to interpret how they arrive at specific decisions.
- It is crucial to achieve a model that generalizes new and unseen data well. When it comes to complex tasks, ML models might struggle to generalize well to diverse and unseen situations. Achieving models that can perform reliably in various scenarios is an ongoing challenge. When it comes to ML, it is a challenge to balance between overfitting (capturing noise in the training data) and under fitting (failing to capture the underlying patterns).
- Smaller organizations or researchers have very little access to powerful hardware. This can be an issue as training sophisticated ML models, particularly deep learning models, require substantial computational resources.
- It is challenging to compare and reproduce results across different studies and implementations as there is a lack of standardized frameworks and evaluation metrics. Standardization efforts are ongoing but are not yet available globally.
- Security of ML systems in critical applications is becoming a growing concern. Intentionally crafted inputs can mislead the ML models making them more prone to attacks.
- In areas like privacy, surveillance, and decision-making, ML applications raise some ethical concerns. It has been an ongoing challenge to ensure that ML systems are deployed ethically and with consideration for societal impact.
- In real world settings, it can be challenging to transition from a successful prototype to a deployed and integrated ML system as it comes with the specific requirements of a careful mix of system integration, scalability, and aligning the model with business needs.
Conclusion
We are living in the era of cutting edge technology of AI and ML. As ML is still growing, there’s a lot to be explored and to be rebuilt in this area. As this happens, there will be a lot more complex challenges and solutions that will be figured out only with the passage on time.
However, ML has been a bonus to organisations. With big data analytics and deep learning, companies are today able to get maximum benefits from ML. Finding and implementing the right solution is the key to the successful use of ML in organisations.
Need help with business decision-making insights? At CRG Solutions, as a Business Consulting Firm, we provide leadership, knowledge and expertise in the areas of enterprise resource planning, corporate performance management, business intelligence, financial and costing principles, shared services, value-based management, leadership & board assessments, and human capital management.
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