Many who are new to Machine Learning (ML) are struggling with the upfront challenges of getting started with their first project. Here are some tips you can follow to succeed in a Machine Learning project.
Tools that use Machine Learning (ML) are enabling companies of all sizes to automate their processes and make better decisions. But many who are new to ML are struggling with the upfront challenges of getting started with their first project.
The risk of failure can seem too large to bear, especially if you are a small organization with limited technical resources. But as the transformative potential of Machine Learning becomes more apparent and as it becomes more accessible to smaller enterprises, it is a force that is hard to ignore.
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If you are about to embark upon building an Machine Learning project for your organization, here are a few clues as to what can help you succeed.
1. Know your data
Some projects fail because of expectations that Machine Learning will be able to handle the shortcomings of bad data sets. Poor-quality data will yield insights that your company can’t trust, which of course makes for a lot of wasted time, effort, and money.
What does bad data look like? It all depends on what you are using the data for but in general, these are some data set issues that companies often experience:
- Bad PII data. Personally Identifiable Information (PII) is notoriously difficult to clean up, with duplicates, inaccuracies, and omissions prevalent throughout many data sets.
- Incorrect e-commerce data. In a Deloitte survey, far fewer than half of respondents said their online purchase activity was correct. Even worse, fewer than a quarter said the data about their purchase categories was even close to being correct.
- Poorly structured and badly formatted data.
- Inaccessible data. Data that is not available to the team can be a major downfall to any ML project. Very often, companies we work with have constraints on who can access company data and how it can be used. The team that partners with us is not fully aware of these constraints and as a result, end up overestimating the size and/or quality of the data sets that will be used in the project.
One thing to note here is that it is commonly believed that a small data set is an obstacle to implementing a Machine Learning project. While it is usually the case that the more data you have, a good model can still be built with a small data set if the data is prepared the right way.
2. Bring in the right people to your machine learning project
In addition to starting with a team that has the right skills, you will also need to assemble a team that includes people with business knowledge. One chief indicator of an Machine Learning project that is going to fail is that it is driven by a fascination with technology rather than by business needs.
That is where having the right people on the team will really come into play. A business analyst, for example, will help define the goals within a context that is business-appropriate. The danger here is when the project takes on a “gee-whiz” factor you end up with business insights that do not matter.
With better business outcomes as the driving force behind your ML project, you are less likely to fail. Exploring the possibilities of Machine Learning is one thing, but it is important to focus on the goals that your business has right now and to keep the project simple, with clearly-defined goals that will lead to a better bottom line.
3. Define the metrics for success
Defining your business goals not only helps set a solid foundation for a successful project but it also helps the team define success. Using your business goals as a roadmap, clearly set out what metrics you will need to measure.
The ML engineers on your team will have another set of metrics to pay attention to. They will need to ensure their results are reproducible and that their algorithms are transparent, credible, fair, and impactful.
4. Match the data sets to the business problem
Chances are, even the smallest companies have impressive data sets at this point, thanks to increased digitization in all sectors. But when it comes to building a Machine Learning project, the team will need to identify exactly which types of data is required for the project’s specific, stated goal.
A data strategist can match the business problem with the right kind of data, which is a step that should take place before the team chooses and builds the solution.
5. Let the data engineer do their job
With good data, the right data, clearly-defined business goals, and proper success metrics in place, it is time to let the Data Engineer do their part. They will work to find the right solution and begin envisioning the infrastructure of the project – that is, the architecture that will enable the team to stream the required data into the right platform that best suits the goals of the project.
6. Build the best model, not the most complex model
There are lots of solutions with different algorithms that will give solutions but it is best to keep things simple. Even if you are “sacrificing” precision, a simple model that fulfills the set goals of the project can mean easier and faster success. It also means it will be easier to deploy and maintain as you move forward and expand the project in the future.
A final word on succeeding in your Machine Learning project
Having access to good business intelligence through ML is transforming industries across the board. From personalization to voice recognition to chatbots and customer service integrations, ML is helping organizations achieve better business outcomes.
And it is not just the big, resource-heavy brands that are leveraging the technology. With the help of technological partners, smaller companies can collaborate with teams who are experienced in helping companies like theirs make the leap to their first Machine Learning project.
By answering your ML questions, assessing your unique business needs, and taking a look at your data assets, we will help you see that there is no limit to what you can dream… let’s work together to build your next custom Machine Learning project, one careful step at a time.
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