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A predictive AI model is a sophisticated algorithm designed to forecast future outcomes based on historical data. These models use various techniques from statistics and machine learning to identify patterns in data and make predictions about future events or behaviors.
The process of building a predictive AI model typically involves several steps. First, data scientists collect and preprocess relevant data, which often requires cleaning, normalizing, and transforming the information into a suitable format. Next, they select appropriate features or variables that are likely to influence the outcome being predicted. The team then chooses a suitable algorithm or combination of algorithms, such as decision trees, neural networks, or ensemble methods, depending on the nature of the problem and the available data.
After the initial model is built, it undergoes a training phase where it learns from the historical data. This is followed by a validation phase to assess its performance and make necessary adjustments. Finally, the model is tested on new, unseen data to evaluate its real-world effectiveness before being deployed in a production environment.
The build of an AI model as a whole is a highly complex tech and engineering process that needs to be productized from infrastructure through, code, interface design, business logic and definitions, up until maintenance and retrain of the model. Each of these phases entails a complexity of its own which requires expertise, know how, talent, operation efficiency and business knowledge. The process may take a dedicated engineering squad months and even years to master, the time, resource-heavy and complexity of management has led more and more tech companies to consider the buy approach when approaching the question of how to build proprietary AI models.
The article below weighs the different complexities from the perspective of the build vs buy software dilemma that many companies face in today’s software development environment. Each of the different complexities should be carefully considered when either embarking on the resource heavy path of building an AI model or choosing an AI modeling platform to purchase.
Building a predictive AI model requires a diverse team of skilled professionals. At the core, you need data scientists who are well-versed in statistical analysis, machine learning algorithms, and programming languages such as Python or R. These experts are responsible for designing and implementing the model itself.
However, data scientists alone are not sufficient. You'll also need data engineers to handle the collection, storage, and preprocessing of large datasets. Software engineers are crucial for integrating the model into existing systems and developing user interfaces. Additionally, domain experts who understand the specific business context are essential for interpreting results and ensuring the model addresses relevant business problems.
Finding and retaining this mix of talent can be challenging and expensive, especially given the high demand for AI and data skills in today's job market.
When considering the buying approach, one significant advantage is the reduced need for specialized in-house talent. Vendors typically provide their own expertise, allowing companies to implement predictive modeling without hiring a full team of data scientists, engineers, and domain experts.
However, relying on external expertise may limit the organization's ability to build internal AI capabilities for future projects.
Developing a predictive AI model is a time-intensive process. The initial data collection and preparation phase can take weeks or even months, depending on the complexity of the data and the state of existing data infrastructure. Feature engineering and selection, another crucial step, can be equally time-consuming as it requires deep analysis and often involves trial and error.
The model development phase, including algorithm selection and initial training, may take several weeks to months. However, the process doesn't end there. Extensive testing and validation are necessary to ensure the model's accuracy and reliability, which can add several more weeks to the timeline.
It's important to note that this timeline can extend significantly if the initial results are unsatisfactory, requiring the team to iterate on the model design or collect additional data.
Opting to buy a pre-built solution can dramatically reduce the time to implementation. Vendors offer model building and definition tools that are already developed, tested, and ready for deployment, potentially cutting the timeline from months to weeks.
This quick turnaround can be crucial for businesses needing to rapidly respond to market changes or competitive pressures. There is always the learning curve associated with understanding and effectively using the purchased system, which, while generally shorter than building from scratch, still requires time investment from the organization.
Maintaining a predictive AI model is an ongoing process that requires continuous attention and resources. Models need to be regularly monitored for performance degradation, which can occur due to changes in the underlying data patterns or shifts in the business environment.
Retraining is a critical aspect of model maintenance. For example, consider a predictive model used by a SaaS tech company to forecast marketing lead to sales qualified lead conversion rates. If the company introduces a new product category or enters a new market, the existing model may no longer accurately predict demand patterns. In this case, the model would need to be retrained with new data that includes these changes.
The frequency of retraining can vary widely depending on the specific use case and the rate of change in the relevant environment. Some models may need weekly or monthly updates, while others might be stable for longer periods.
Purchasing a Predictive AI Modeling Platform can significantly reduce the maintenance burden on an organization. Vendors typically handle updates, bug fixes, and regular retraining of the model, which can free up internal resources and ensure the model remains up-to-date with the latest advancements. This can be particularly advantageous for companies without the expertise or resources for ongoing AI maintenance. However, this convenience comes with less control over the retraining process.
One of the biggest challenges in building predictive AI models is ensuring they remain effective in dynamic business environments. Business conditions, customer behaviors, and market trends can change rapidly, potentially rendering a model obsolete if it's not designed to adapt.
To address this, models need to be built with flexibility in mind. This might involve implementing online learning algorithms that can update in real-time or designing modular systems where components can be easily replaced or modified as conditions change.
Pre-built solutions can offer advantages in dynamic environments, as a deeply knowledged and experienced software vendor will already consider dynamic models and updates as part of the product offering. They may also provide regular updates and new features based on broader market insights. This can be beneficial for companies operating in rapidly evolving industries.
Customizability is often cited as an advantage of building in-house models, achieving the right level of customization is a complex task. It requires a deep understanding of both the technical possibilities and the specific business needs.
Customization might involve tailoring the model's inputs to include unique data sources, adjusting the algorithm to prioritize certain factors over others, or designing custom metrics to evaluate the model's performance in the context of specific business goals.
However, over-customization can lead to problems such as overfitting, where the model performs well on the training data but fails to generalize to new situations. Striking the right balance requires considerable expertise and often involves a process of trial and error.
When buying a predictive AI model, customizability can be more limited compared to building in-house. However, many vendors now offer high degrees of customization in their products. This includes options to adjust and handpick parameters and fields which the model is built on, integrate specific data sources, modify output formats and choose output workflows. The advantage here is that these customization options are often designed to be user-friendly, allowing for adjustments without deep technical expertise.
A critical factor often overlooked in the technical discussion of model building is the importance of deep business knowledge. Effective predictive models don't just require technical expertise; they need to be grounded in a thorough understanding of the business context, industry trends, and strategic goals.
This knowledge is crucial at every stage of the model development process, from defining the problem and selecting relevant data sources to interpreting results and making decisions based on the model's predictions. Ensuring that this business knowledge is effectively integrated into the model development process can be challenging, especially if there's a communication gap between the data science team and business stakeholders.
Buying a solution can provide access to industry-wide knowledge and best practices that the vendor has accumulated from working with multiple clients. This broad perspective can be valuable, especially for companies new to predictive modeling or looking to benchmark against industry standards. Vendors often have experience in translating business needs into technical requirements, which can bridge the gap between data science and business operations. However, no vendor can match the depth of company-specific knowledge that an in-house team would possess. While vendors make efforts to understand each client's business, their insights may remain somewhat generalized. This can result in models that, while competent, may miss nuances or unique aspects of a company's operations that could be crucial for truly impactful predictions.
Handling data privacy is a major consideration when building predictive AI models, particularly when dealing with Personally Identifiable Information (PII). PII includes any data that could potentially identify a specific individual, such as names, social security numbers, or even combinations of non-identifying information that could be used to determine an individual's identity.
When building models that access PII, organizations need to implement robust data protection measures. This might include data anonymization techniques, encryption, and strict access controls. Moreover, they need to ensure compliance with data protection regulations such as GDPR in Europe or CCPA in California, which can add significant complexity to the data handling and model development process.
For example, if a healthcare provider is building a model to predict patient readmission risks, they would need to carefully anonymize patient data, ensure secure data storage and transmission, and potentially limit the types of data used in the model to comply with healthcare privacy laws like HIPAA.
When evaluating potential vendors for Predictive AI Modeling understanding if the vendor is compliant with the most up to date privacy regulations and that user privacy protections are handled correctly is essential.
In the table below we created a table weighting conclusions on the build vs buy dilemma while contemplating which route to take while implementing AI modeling for your organization.
The table provides a weight (1 low, 5 high) for each criteria. Please take into account that this is based on general yet educated assumptions that were made while witnessing and working with hundreds of companies who decided one way or the other while making the build vs buy decision.
It would be recommended that organizations and companies start their search by considering the factors below based on their specific business scenario and needs.
Ultimately buying a Predictive Modeling Platform, when done correctly, will outweigh and bring superior business efficiency in comparison to the build in house approach.
While building a predictive AI model in-house offers the potential for highly customized solutions, it comes with numerous complexities. From assembling the right team and allocating sufficient time, to ensuring ongoing maintenance and navigating privacy concerns, the process requires significant resources and expertise.
Organizations must carefully weigh these challenges against their specific needs and capabilities when deciding whether to build their own predictive models or opt for pre-built solutions.
At Forwrd.AI we have built a Predictive AI Modeling Platform which takes into consideration the above factors and supports all the edge use-cases described in the article.
