Artificial intelligence and machine learning holds immense power to transform businesses. These two technologies have become a necessity for every modern business to function. However, the path from identifying the potential of these technologies to practical applications hinder some challenges. From ensuring data quality, to integrating these models into existing systems, organizations face several challenges.
Through this blog, we will be exploring some of the key challenges organizations face while implementing artificial intelligence and machine learning models.
Interconnection of Artificial Intelligence and Machine Learning
Artificial intelligence is a broader term that defines creating machines that perform tasks with human intelligence. On the other hand, machine learning is a subset of artificial intelligence that helps in achieving artificial intelligence capabilities by training data. Machine learning models are built on the data; hence data quality is an important aspect of machine learning.
There are three important parts for machine learning; they are,
Data
Algorithms
Training and evaluation
Data is the foundation and key for Machine learning models, as this is where the models learn from. This includes data collection, pre-processing and feature engineering. Algorithms means how the model is learning, i.e., supervised learning, unsupervised learning, and reinforcement learning. The final stage is to verify whether the model is working well or not. This includes model training, evaluation, and deployment.
Challenges in Integrating Artificial intelligence and Machine Learning
Artificial intelligence and machine learning is constantly evolving. Earlier ML models were easy to understand and manage compared to deep learning models available today like large language models (LLM). One of the key areas, where model advancement has happened is in the models that are trained on text data.
The rise of transformer, (an AI model that understand texts) has led to the rising shift towards LLMs. Even though these LLM models has brought massive advancement in the tech space, but this comes with its own challenge. Let us look into them.
1. Increasing model complexities
The shift from small ML models to large language models has led to higher complexities. Traditionally ML models were easy to maintain, deploy and interpret, whereas now LLM models comes with millions of parameters which becomes difficult to manage. Moreover, it requires huge computational resources, and specialized infrastructure for maintaining these models.
To maintain Machine learning models lifecycle, MLOps is a must. ML models go through continuous monitoring, re-training, i.e., updating the model with new data, and keeping a track of each model version. These tasks can be automated by integrating MLOps pipeline.
However, maintaining MLOps becomes a huge challenge as number of models deployed in a system goes beyond one hundred or so because of complexity of the system, scalability, and continuous lifecycle management. If businesses are planning to integrate or adopt artificial intelligence and machine learning they need to invest in MLOps, scalable AI, and better AI governance.
Quick POC – any model which they want, looking for performance
2. Feature Engineering
Feature engineering is a process of converting texts into meaningful insights for improving Machine learning models performance. Smaller machine learning models understand numeric columns called vectors rather than texts. Hence, for ML models to interpret better, converting raw texts to meaningful numerical insights is important.
However, this process is very time consuming also includes high computational resources and needs domain expertise. For instance, a patient’s medical record contains text like data, these texts need to be converted to structured numerical features for AI to diagnose. To train the AI model, will take months leading to delay in AI deployment.
Few potential solutions that businesses can consider making it easy are, look for pre-trained embeddings like use natural language processors (NLP) models to convert texts to vector representations.
3. Transparency in output
Traditional machine learning models such as linear regression and decision trees were easy to understand. Whereas today LLM’s holds complexities with billions of parameters making it impossible to understand how it reached to a specific output. This scenario creates a black box problem(transparency) — where we see the result but how it reached that decision it difficult to understand.
If a prediction from AI model is wrong, it is hard to debug from which internal data the error occurred. For instance, industries like healthcare, finance, law needs explainability in their decisions. A financial institution denying a loan based on AI prediction, must be able to justify why is it denying.
As a solution, businesses can use explainable AI tools to understand AI decisions clearer. Furthermore, hybrid models, which is a mix of both simple and complex AI can be considered by businesses. This involves combining decision tree models with powerful deep learning models.
4. Balancing cost and performance
Implementing ML models today, like LLMs has huge inference cost, training cost and level of accuracy expectation is high. LLM models are capable of handling large documents, texts etc, but their high inference and training cost must be managed carefully.
Inference cost means the price you pay for per predictions. In simple terms, every time your AI makes a prediction or gives an answer inference cost is paid. These costs are higher when compared to traditional ML models like linear regression and decision trees.
Businesses can implement artificial intelligence and machine learning models based on their specific tasks. If high accuracy is expected with complex tasks and price is acceptable then choosing LLMs, would be a great option otherwise traditional machine learning models are cost-effective.
However, in both the cases expecting 100% accuracy is probabilistic. While implementing these models, businesses should balance AI models with human expertise and not completely rely on them. Moreover, setting clear limits where AI provides the most value by combining it with traditional methods provides a more balanced approach.
How Prescience Helps with Artificial Intelligence & Machine learning Solutions
A global electric company struggled with sales forecasting, as they were relying solely on historical data, overlooking at opportunities, targets, orders, etc, as none of them had direct connecting links.
Prescience Decision Solution, a data analytics and AI company delivered a cost-effective machine learning solution using traditional models and a dual approach: a Sales-Only Forecast for short-term accuracy and a Combined Forecast integrating multiple data sources. They integrated times series and statistical models like ARIMA, SARIMAX, PROPHET, LSTM, Random Forest, SCBoost, and SVR.
This, in turn improved the forecasting accuracy to 85-95%. This multi-model ML solution provided granular insights, improving the resource allocation and better decision making.
Conclusion
As today businesses are looking forward for implementing artificial intelligence and machine learning models to their businesses, the road for adoption comes with its own challenges.
Businesses should think strategically from managing complex LLM models to balancing transparency, accuracy, and costs before implementation. It is not just about implementing AI but also using it wisely for smarter decision making. With the evolving nature of AI and ML models it is important for organizations to blend these advancements with human expertise for better outcomes.
At Prescience Decision Solutions, we navigate the complexities related to artificial intelligence and machine learning across various industries like sales, finance, e-commerce, marketing etc. by delivering custom solutions that integrate intelligent models to overcome challenges while ensuring data quality, transparency, and scalability. We provide comprehensive AI and machine learning solutions across analytics, business intelligence, and data engineering, delivering measurable business value and ROI.
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Prescience Team
