I work for a small regional shipping company, mostly building CRUD apps and doing EDI integrations. I'd like to find a practical side project using machine learning and/or data science that could add value at work, but for the life of me I can't come up with any problems that I couldn't solve with a relational database (postgres) and a data transformation step. I've spent some time learning pytorch, numpy, and pandas but I know that if I don't use it, especially at work, I'll just forget everything I've learned. My boss is a dev and is generally supportive of learning new things and finding ways to innovate independently, so if I can come up with a good idea I'm sure he'll let me pursue it in my spare time. Has anyone tried to do this before? Any suggestions would be great.
1) You could use ML to assess the on-time reliability of each of your delivery agents (contractors or individuals). Providing more accurate estimates of delivery time to customers (maybe via text message) might be very desirable. Or you could notify only when expected delivery time has changed (arriving earlier or later). For Just In Time-based shops, this could be a big win.<p>2) You could assess different delivery routes/ regions to determine if they are more/less on-time than other routes/ regions. Are the number of delivery vehicles adequate? When should you adjust the number of vehicles or change the routes themselves (like moving some peripheral regions to another route, or adjusting the cost charged when delivery is delayed).<p>3) When do external factors (like weather, esp rain or snow) introduce delays? Can you predict these delays, and ideally, compensate by changing routes or adding more delivery vehicles?<p>4) Should you more dynamically adjust your shipping fees to reflect faster/slower delivery time targets? This way you can tune your routes and manpower to save money for those who aren't as time sensitive, and improve the response time for those who are.<p>A lot of this is basic operations research. But you can call it AI, or use AI techniques just as well as traditional OR methods. Nobody will care what math/methods you use if you can add value.
This is a common problem in tech:<p>"I've just learned a neat new tool but I never apply it because I can solve all the problems in front of me with the tools I have."<p>In effect, you've found a local maximum where every direction seems like a step backwards, or an investment of time without any reasonable payoff.<p>Here are two general strategies to deal with this:<p>1. Take a well understood, and well documented existing need, and replicate the solution with the new toolkit. Acknowledge from the outset that this will be a step backwards, but go through the details anyway to better understand the technology. The goal isn't to make the system better, but to improve your understanding of ML and it's real world application. By choosing a well understood system, you are only learning applied ML rather than trying to simultaneously learn ML and the problem. Work toward parity with your existing methods. This part is rarely a big step forward, but I <i>guarantee</i> that this process will generate 100 good ideas about where to go next.<p>2. Find problems that were previously ignored, because they couldn't be solved. Something no one is even thinking to ask for, because none of the prior tools could do the job. This is the ideal situation because you are in a greenfield space where anything is an improvement. For ML specifically look at anywhere a lot of data is being generated but no one has the time to read it all unless something goes wrong.<p>When learning any new technology there is always a gap between learning it in the lab, and trying to execute with it IRL. The best way to maximize your own ability is to simply start applying it and building experience. Don't wait for a perfect halo project.
Using a broad definition of ML/data science, here are a few ideas:<p>First, coding toy problems (related to shipping or not) that implement linear regression, genetic algorithms, or neural networks, etc. will be a useful start<p>Analyze shipping and tracking EDI data to predict whether a shipment will be late (0.0 to 1.0 output, 1.0 being it will be late for certain)<p>Predict the likelihood a customer will churn (stop using your services) based on changes in volume, billing amounts, and other characteristics<p>Predicting this year's peak season shipping volume based on past years' data. See if you can beat the marketing/sales folks' predictions<p>Identify factors correlated with the most profitable shippers<p>Predict the likelihood a package is damaged<p>Use a genetic algorithm to improve driver routing<p>Reconfigure pickup times / drop off times to improve profitability<p>Use EDI shipping data to build a network graph of who is shipping to whom, segmented by type of some sort. Say you find that many A-type firms are shipping to B-type firms; any B-type firms that are not already customers could be interesting targets.<p>Score prospects to estimate their profitability by comparing their characteristics to existing customers' profitabilities<p>Use a neural network (or something else) to analyze EDI shipping data, damage data, and make packaging recommendations to customers<p>Analyze tracking EDI data, segmented by delivery area (zip+4?) and see if there are areas where drivers are more efficient at delivering faster. Maybe start an initiative to look at what separates the most efficient drivers from the least.<p>Reporting: not sexy, but really useful in this space<p>Bona fides: I used to work in the supply chain consulting space and consulted at firms like yours. Things are surprisingly basic in the shipping space - less meaty data science than one might think.<p>Edit: Formatting
To get started, I'd pick the most important business problem you have and then solve it using the simplest machine learning approach.<p>You mentioned using Pytorch. Instead, I recommend a classical machine learning using a library like scikit-learn (<a href="https://scikit-learn.org/" rel="nofollow">https://scikit-learn.org/</a>). Use a random forest classifer and you'll get pretty good results out of the box.<p>If your data is in a postgres database across multiple tables, you will likely have to perform feature engineering in order to get it machine learning ready. For that, I recommend a library for automated feature engineering called Featuretools (<a href="http://github.com/featuretools/featuretools/" rel="nofollow">http://github.com/featuretools/featuretools/</a>). Here's a good article to get started with it (<a href="https://towardsdatascience.com/automated-feature-engineering-in-python-99baf11cc219" rel="nofollow">https://towardsdatascience.com/automated-feature-engineering...</a>)<p>Finally, you will need to define a prediction problem and extract labeled training examples. I see people in this thread have suggested ideas of problems to work on. The key here is make sure that you pick a problem that you can both predict and take an action based off the prediction. For example, you could predict that there will be an influx of shipments to fulfill tomorrow, but that might not be enough to time to hire more people to help you fulfill them.<p>If you're curious what the process looks like end-to-end check out this blog series on a generalized framework for solving machine learning problems that was applied to customer churn prediction: <a href="https://blog.featurelabs.com/how-to-create-value-with-machine-learning/" rel="nofollow">https://blog.featurelabs.com/how-to-create-value-with-machin...</a><p>Full disclosure: I work for Feature Labs and develop Featuretools.
My general tip is to look for things that are almost but not quite automatable, where a human needs to do a quick look-over.<p>One big example is fraud: it's next-to-impossible to define a 100% accurate set of rules to filter fraud, but it's often easy to train an algorithm to catch the worst offenders, or flag suspicious cases to significantly narrow the amount a human needs to review.
Find a system or process that uses a series of rules to categorize, label, or action things, especially one that is occasionally incorrect. Model these rules with a ML algorithm using the rule outputs & user corrections as labels. See if you can build an ML system that out-performs the rules. (If you can, it'll probably be by looking at data that the rules didn't consider.)<p>Note: your ML system will likely be less explainable than the existing rules. This won't matter as much if the current rule collection is already more complex than a human can deal with. It will matter a LOT if your decisions are subject to regulation.
<p><pre><code> > I can't come up with any problems
> that I couldn't solve with a
> relational database (postgres) and
> a data transformation step.
</code></pre>
Congratulations, you have seen through the hype! Most "machine learning" claims you see are solvable just with linear regressions on slightly cleaned up data.
> pytorch, numpy<p>If your problems aren't audio / image based, then consider using traditional ML instead.<p>If you are just starting out. Check out SKLearn, Scipy and graphical models like CRFs. They are tried in tested methods that also require less specialized skills.<p>As someone else said, a lot of AI, ML tools are simply repackaged old school OR methods. The older methods get 95% there, with <50% of the effort.<p>Cutting edge ML isn't required for most problems. Especially non visual or time series problems.
If you explore the word2vec family of algorithms, you can improve text search by pulling in external datasets. E.g. use a model trained on Wikipedia to find synonyms, or build a neural network that maps user search terms to documents in your database.
If you're looking for problems to solve in the transportation / shipping context, one that comes to mind is estimated delivery. Try predicting the day (maybe even down to the hour) of when something will arrive at its destination once it enters your company's network. It may require feeding it the origin and destination, the product mix in the trailer, customer priority, weather conditions, time of year (peak season?), etc.
There must be someone in your company doing some form of business analysis? Probably in spreadsheets. Talk to them, especially about aspects of their work that involve probability. Don't expect to find a problem that looks exactly like one in the Tensorflow tutorial; you may have to get creative and you may have to do some maths with a more old-school flavour.
Start somewhere simple - like using ML to make search easier. Try using a collaborative filter to improve search for your company - even by doing something as simple as updating the placeholder text.
> for the life of me I can't come up with any problems that I couldn't solve with a relational database<p>It sounds like you have the right tool for the job now, so great. Keep using it. Dependencies should be added to projects as conservatively as possible. The best dependency is no dependency. You shouldn't go seeking out dependencies. Your app doesn't depend on machine learning, so why would you make it depend on something it doesn't actually depend on? Future maintainers (including yourself) would hate you for it.
Try implementing a genetic learning algorithm to send and reply to your work emails. For the fitness score try using your yearly salary in dollars. I haven't yet implemented this, but theoretically there's no upper bounds to how much money this will earn you.
Some CRUD applications have scope for adding recommendations... based on other things the client has create d predict and auto-populate fields in advance or give them recommendations if changing some fields in existing records can help them. Again, it will depend on type of CRUD application.<p>There's also scope of using ML in analytics and monitoring side apart from the main application and is generally better tolerated by the product team.
Here is some marketing material that may spark some ideas:<p><a href="https://www.ibm.com/watson/supply-chain/resources/csc/desktop/index.html" rel="nofollow">https://www.ibm.com/watson/supply-chain/resources/csc/deskto...</a><p>One concrete example IBM likes to talk up is predicting shipping delays due to weather events and automatically recommending alternate suppliers.
Pick a report with lots of numerical columns that your company is currently having accounting or the CFO "interpret" into a go-no go decision. Implement logistic regression.<p>There may be a way you can do some computer vision tasks for quality control in some parts of your business -- most businesses that deal in physical goods have quality control by visual inspection and in most of those you can end up with a CNN that provides a quick enough, good enough solution. However, sometimes for regulatory reasons it's not practical, or it's something that is not a critical part of the chain, and so on. But you could ask operations staff about whether they sometimes do that kind of task, and whether it takes up a lot of their day. It's not like you have to find the good idea alone.
I am a beginner too.
Below is a problem I am recently working on. You can work on it too if you think you have a lot of PDFs in any part of your shipping business. I started attacking this to mainly learn python and uses python ML libs.<p>My problem statement includes classifying hundreds of thousands of PDFs to different categories based on the content/first few pages. That is, if you have a pdf of a novel by Jeffrey Archer, it should be categorized as Entertainment or Novel etc. If you have a e-book of say Python for Dummies, it should be categorized as Engineering or Technology or Education or Programming and the like.
What's the risk of loss/insurance coverage situation for the items you ship? ML is good for digesting several input variables and assessing the risk of X happening. You might be able to pair that with an up-sale of insurance coverage to the client to generate additional income or use it for cases where the company self-insuring certain shipments might be an effective way to save money for little risk. Depending on how your company typically deals with insurance of course.
We should talk! I do work on automatically coding products for a shipping survey at the Census Bureau. One of the earliest production uses of ML here at Census :)<p>5 minute deck:
<a href="https://github.com/codingitforward/cdfdemoday2018/blob/master/Optimizing%20the%20Commodity%20Flow%20Survey%2C%20v2.pdf" rel="nofollow">https://github.com/codingitforward/cdfdemoday2018/blob/maste...</a><p>Feel free to shoot me a message.
I'll put out another (basic) idea that isn't listed below.<p>If you have access to production logs and metrics, try to model things like page load times, server load, network latency, errors/timeout, number of page views/unique visitors.<p>You might hit on unexpected correlations and maybe unknown bugs (e.g. when page X is loaded with input Y, timeouts and server load increase because of a broken SQL request), or insights on the health of the production platform.
Ask the product and strategic analytics folks what statistical models they're already using, then identify gaps between that and what would be required to add (additional) value by making use of ML techniques.<p>Also consider that ML simply isn't a useful tool for your company. "Let's not spend (more) time trying various ML techniques" is a perfectly valid and useful outcome of an experiment using ML.
It would not be incorporating ML into your app's code base directly but one way that I incorporate ML is to use it to analyze application performance data in hopes of learning how to improve it.<p><a href="http://glennengstrand.info/software/architecture/msa/ml" rel="nofollow">http://glennengstrand.info/software/architecture/msa/ml</a>
I am facing the exact same scenario in the railroad industry. I have the idea, the data, I know how to build the model, train it but my biggest challenge would be delivering it and actually making use of it.<p>In the end, is it really worth all that effort if you end up using Cognitive Services on Azure or ML on AWS? How to you deploy your model and actually USE it in a WPF CRUD app?
Reminded me of this link I saw here earlier:
<a href="https://news.ycombinator.com/item?id=17433752" rel="nofollow">https://news.ycombinator.com/item?id=17433752</a><p>It shows a link to an article called:
"No, you don't need ML/AI. You need SQL"
"I can't come up with any problems that I couldn't solve with a relational database (postgres) and a data transformation step"<p>do it anyway with Machine Learning and see how it goes. at least you will know the expected results.
You have hit upon the dark secret of the industry. ML is a solution looking for a problem. It’s nowhere near as bad as blockchain mind, but ML and Kubernetes both have far more people eager to do them than there is actual work...<p>Having said that there must be a beancounter in your organisation whose job is making forecasts. That person wouldn’t hesitate to lay you off if the company hits a rough patch. I think you get my meaning.