Lately it’s been difficult to find an organization that isn’t taking a hard look at artificial intelligence. AI is all over the news and recent advancements have unlocked capabilities that were unthinkable just five years ago. For instance, outsourcing customer support to AI in 2020 was a far-fetched idea, but now it’s becoming the default in many sectors.
However, moving from the ideation phase to actual production in machine learning (ML) projects is not as straightforward as in software engineering projects. AI solutions sit at the crossroads of engineering and data science, each bringing its own set of challenges. A machine learning system can carry out operations such as classification, prediction, and decision-making based on the data it processes, but it also faces practical challenges in real-world scenarios. Many organizations lack the expertise to handle these projects internally, and building out a mature data science practice is a considerable undertaking.
Developing AI solutions is a multidisciplinary effort that requires expertise in data science, software engineering, DevOps, legal considerations and product management. It’s essential to understand any knowledge gaps early in the project to avoid missteps later.
For instance, there may be a robust data science team at the company, but no specialists in machine learning operations (MLOps) or DevOps to move the data insights into production. Alternatively, the company could have software engineers who excel at building scalable systems but lack data science expertise. Another common issue is that marketing teams might be collecting quality data but don’t have the infrastructure to use it effectively.
Identifying and addressing these gaps is crucial for timely completion. Everyone on the team needs a base level of domain knowledge to ensure that they are aligned with the goals of the project. For example, if an AI solution is being developed for the restaurant industry, understanding key features like revenue drivers and customer preferences can help guide the development process.
In short, the phrase “we’ll handle it ourselves” should only be used if the company is confident that it has the expertise needed in-house.
When embarking on an ML project, it’s important to be realistic about timelines. Even with careful planning, unexpected issues will arise. A good rule of thumb is Hofstadter’s Law: “It always takes longer than you expect, even when you take into account Hofstadter’s Law.”
There are several reasons for unpredictability in the timeline of an ML project: scope creep, integration issues, or resource allocation problems are common challenges. Leaning on experts who have seen these problems before can help anticipate and mitigate delays.
A well-defined statement of work (SOW) can help keep the project on track. Real-time visibility using project management tools ensures that expectations remain aligned. The key here is to come up with an estimate that includes variability and is built on a deep understanding of the challenges that may arise.
In case of delays, regular communication should be established from the start. Clear channels for discussions, whether through Slack or during weekly calls, ensure that everyone is updated on the project’s progress.
Most companies do not have the resources to train their own AI models from scratch. At some point, businesses will rely on external solutions for cloud compute, AI models, or data lakes.
Throughout the course of the project, discussions around outsourcing technical solutions will come up. There will be tradeoffs between continuing to build in-house or outsourcing, which need to be addressed as they can significantly impact the project timeline.
Key questions to answer include:
These questions should guide decision-making as the project progresses, helping to avoid getting stuck or running over budget.
One of the key factors for success in an AI project is defining expectations from the very beginning. ML projects that succeed are often narrow in scope, focusing on a specific task rather than attempting to solve everything at once.
For instance, instead of asking an AI solution to manage all customer interactions, a more realistic expectation might be to automate just one part of the process, such as responding to simple, repetitive queries.
Setting clear, focused expectations helps keep the project on track and ensures that success can be measured. This is especially important for customer-facing applications, where any AI-generated output must be aligned with the brand’s values and standards.
Questions to consider at this stage include:
Data quality is the cornerstone of any successful AI project. A well-designed AI system will still perform poorly if the data it’s trained on is of low quality. Therefore, understanding the type of data you have, its accuracy, and how it will be processed is critical.
Data should be thoroughly vetted, with clear answers to the following questions:
One of the key issues that arise with data is the manual process of uploading and cleaning it. Anything manual should be avoided. Data pipelines must be automated and scalable from the start, with built-in quality checks to avoid issues such as empty fields, irrelevant columns, and incorrect values.
One of the misconceptions around AI is that the model chosen will make or break the entire project. While model selection is important, it’s not the most critical factor, especially in the early stages of development.
In the initial phases, the goal is to get a functional proof-of-concept with minimal cost. Pre-trained models like GPT-3.5 can provide excellent performance without breaking the bank. For testing purposes, speed and quick iteration matter more than selecting the “perfect” model.
Data and feature engineering play a far more critical role than model selection in the initial stages. Once the proof-of-concept is solid, there may be room to fine-tune the model further or switch to a more specialized model.
Testing is an area where delays often occur in AI projects. Unlike traditional software, where outputs are predictable, AI solutions are inherently statistical, meaning there will always be some variability in the results.
Establishing a clear evaluation suite from the start can prevent confusion. It’s essential to agree on how changes will be tested, what metrics will be used to evaluate the system’s performance, and what defines an acceptable outcome. Regular feedback from stakeholders who understand the process being replaced by AI is critical at this stage.
It’s important to define when the project is “done” and revisit that definition regularly. Machine learning improvements often hit a point of diminishing returns. Moving from 90% to 95% accuracy can be achieved relatively quickly, but the effort to improve beyond that can take much longer and may not be worth it for most applications.
The definition of “done” also depends on whether the solution is for internal use or customer-facing. Internal tools might not need to be perfect, whereas customer-facing tools will require higher standards.
AI projects must also consider data security and legal liabilities. Sensitive data, especially in fields like healthcare or finance, must be handled with care. There should be strict governance, access controls, and clear guidelines on how to protect data.
AI projects can become costly if not properly managed. Each stage—data preparation, model selection, testing, and deployment—adds to the overall cost. To keep expenses under control, it’s crucial to have open conversations about the scope and priorities of the project.
By following the framework outlined above—understanding the knowledge gaps, setting realistic expectations, agreeing on what to build or buy, and focusing on data quality—companies can ensure timely, cost-effective completion of their machine learning projects.
Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable machines to learn from data, make decisions, and improve their performance over time. This technology has revolutionized various fields, including natural language processing, where it powers applications like chatbots, language translation, and sentiment analysis. By leveraging machine learning, businesses can automate complex tasks, gain insights from large datasets, and enhance their decision-making processes. In this section, we will introduce the basics of machine learning and its wide-ranging applications.
Machine learning is a type of artificial intelligence that enables computers to learn from data without being explicitly programmed. It involves the use of algorithms and statistical models to analyze and interpret data, making predictions and decisions based on patterns and relationships. Machine learning can be broadly classified into three types: supervised learning, unsupervised learning, and reinforcement learning.
Understanding these types of machine learning is crucial for selecting the right approach for your project and ensuring its success.
Effective project planning and management are crucial steps in the machine learning process. It begins with defining the problem clearly and identifying the goals and objectives of the project. Selecting the appropriate machine learning algorithm and tools is also a key part of this phase.
Project planning involves several stages: data collection, data preprocessing, model training, model evaluation, and model deployment. Each stage requires careful planning and resource allocation to ensure smooth progress. Utilizing project management tools and methodologies can help keep the project on track and within budget. By setting clear milestones and regularly reviewing progress, teams can address issues promptly and adapt to any changes in scope or requirements.
Data preparation and preprocessing are critical steps in the machine learning process. This phase involves collecting, cleaning, and transforming the data into a format that can be used by the machine learning algorithm. Key activities include:
Effective data preparation and preprocessing can significantly improve the accuracy and performance of the machine learning model, making it a crucial step in the project lifecycle.
Building and training machine learning models involves selecting the appropriate machine learning algorithm and training the model on the prepared data. Machine learning algorithms can be broadly classified into two types: supervised learning algorithms and unsupervised learning algorithms.
The choice of machine learning algorithm depends on the type of problem, the size and complexity of the data, and the desired outcome. Once the algorithm is selected, the model is trained on the data, iteratively improving its performance through techniques like cross-validation and hyperparameter tuning.
Evaluating and deploying machine learning models involves assessing the performance of the trained model and deploying it in a production environment. Model evaluation includes metrics such as accuracy, precision, recall, and F1 score, which help determine how well the model performs on new, unseen data.
Once the model meets the desired performance criteria, it can be deployed. This may involve integrating the model with other systems, deploying it on a cloud platform, or even on mobile devices. Effective model evaluation and deployment are essential for ensuring the success of a machine learning project, as they ensure the model performs well in real-world scenarios and delivers the expected value.
By following these steps and focusing on key elements like data quality, model selection, and project management, organizations can successfully complete their machine learning projects on time and within budget.
