As business people, we ought not to chase the latest architectures and shiny new ultra-deep, unexplainable models; we must look for something completely different – simplicity, reliability, and results that can be replicated easily.
Tried and tested machine learning model and implementation practices
Companies trying to harness AI don’t just need powerful machine learning algorithms; we’ve seen time and again that division and random forest, primitive as they might seem, can produce just as high an AOC score (and help the organization achieve as high an ROI) as a deep, novel neural network without the pain of a lengthy setup and without you having to go through labeling colossal amounts of training data yourself.
What it comes down to really is scaling – we must work out a plan to quickly turn an excellent Juniper notebook into a real-world deployment.
How to make the most out of an AI initiative and machine learning algorithms
In the rapidly evolving landscape of AI and ML, the collaboration between business leaders and data scientists is more critical than ever. This piece highlights the importance of shared vision and understanding between these two groups, their challenges, and the best practices for effective collaboration.
The importance of shared vision and understanding
Shared vision and understanding are crucial. It’s common for business people and PMs to make all decisions regarding the overall direction of an AI project and for data scientists to oversee architecture selection, experimentation, and model deployment. This is logical.
The former group has insight into creating business value, optimizing operational decisions in corporate settings, and implementing far-reaching organizational changes, which AI integration requires. The latter group possesses computer vision and a deep understanding of available and obtainable data. It can estimate the feasibility of engineering a machine learning method or algorithm in a reasonable time—details from which business people are typically detached.
Bridging the communication gap between business and data science teams
Group communication occurs through joint discussions, presentations, and flip-chart sharing. Usually, the business side of things is handled first; senior management green-lights the idea at a high level, and only after that are the data scientists engaged.
However, since the groups use different jargon terminology and have profoundly different backgrounds, they might still understand the critical aspects of the project differently. Getting on the same page early often leads to loops in re-defining the assignment or, if left unchecked, to release products that seemingly fulfill the plan but don’t meet the project’s and customers’ requirements.
Understanding project aspects: A collaborative approach
In this manner, the whole, diverse project team’s expertise is captured, and all project aspects, from trivial to crucial, are considered. Technical details such as irrelevant features such as prediction targets are not just discussed between the data science team; they are explained clearly to the stakeholders, and the technology’s possible impact on organizational structures and decision optimization is discussed upfront.
Business perspective: Creating value and defining success
From this point, specific questions about the business view of the opportunity are explored:
- How does the technology create value for the organization? For example, which specific problem does it solve? Is the best use case a substantial improvement of an existing offering or a launch of a new one?
- How is profound learning success defined? The data science team might be tempted to concentrate on metrics assessing the machine learning model’s predictions. However, these predictions usually have little to do with the quality of the AI’s operational decisions, which are typically assessed not by technical metrics but with the help of KPIs.
Ignoring technical metrics is not suggested. Instead, it’s essential to understand that several layers of noisy data usually separate the prediction of a machine-learning model and the operational decision stemming from it.
Organizational changes and employee training
After carefully exploring the new business opportunity, what steps are taken to make all associated decisions explicit and specify every objective? Changes the organization will have to undergo to accommodate the switch in operational decision management are also considered: If machines fully or partially conduct some of the operations, how are employees trained so that their efforts and AI complement each other? Is additional training required?
Diving into technical specifics
At this stage, the technical part is delved into:
- What predictions should the AI machine learning system output to fulfill the objectives?
- What feature variables should be considered for the machine to output the most accurate predictions, and based on the potential features, which data sources should be considered first?
Data considerations: quality and sources
The AI will also need adequate processing capabilities to handle the input data and make predictions. Is there sufficient storage and computing capacity? Where will the model operate (private data center, cloud)?
Addressing constraints and security concerns
How are case-specific constraints dealt with? Will the model have to make predictions within certain time frames? Can specific requirements in terms of data security and privacy be met?
Monitoring success and handling deviations
Finally, once success criteria have been defined, how is success monitored? Are there genuinely relevant metrics? Should there be a concrete plan for handling deviations from the allowed range of data points and incidents should they occur?
The success of any AI initiative hinges on the seamless collaboration between business and technical teams. A shared vision, open communication, and a clear understanding of both business and technical objectives are essential for navigating the complexities of AI projects. By acknowledging and addressing the challenges outlined in this piece, organizations can pave the way for more effective and impactful AI implementations.
To sum up
Although ML has transformed entire sectors and industries, the disruption could happen faster due to fundamental limitations and the community’s obsession with novel methods rather than real-world applications.
As many have pointed out, the latest research papers exhibit troubling patterns such as misuse of language, excessive mathiness labeled incomplete data that obfuscates the message, and failure to identify sources of gains and distinguish between explanation accurate prediction and speculations. Their value could be better even for the scientific community and those trying to apply ML to real business problems.
If you want to learn more about applying AI to real-world business tasks, contact our experts for a free consultation.