An example of using real data from SupplyKick as an ML experiment
So, what exactly is Machine Learning?
Machine learning is a growing concept in computer science, where scientists attempt to teach computers to learn abstract concepts just as a human would. This is a major paradigm shift from traditional programming, in which programs operate using a set of logical rules that are explicitly stated by programmers. In machine learning, the programmers instead develop a “model,” a structure that is able to learn the data that the user feeds into it.
The first thing we need to do was get an actual dataset to manipulate. We did so using Amazon’s Marketplace Web Services API. We pulled in the responses from this API and stored them as CSV files that are easy for our program to read. We then clean the data by filtering out repeat entries or records that don’t contain any data at all. Because the goal is to predict future sales, we ordered the rows by date and totaled all orders on the same day. Finally, through experimentation, we found that the model fits best to data for individual products; rather than for all products, so we filtered down to only rows containing a specific product. The result of all of this cleaning is plotted in blue.
Let’s try getting some predictions! After no training, here’s how well the model fits to the training data.
It’s pretty terrible, but this is what we expect without any training. This would be like walking into a calculus test without even having heard what “math” is. After training on the data 100 times we are given the following result.
It seems to be picking up the general trends in the data but doesn’t quite hit the mark. Lets try doing another 900 iterations through the data, for a total of 1000 training runs.
Now the prediction is clearly fitting to the actual sales trend. Also, keep in mind that although 1000 iterations sound like a lot, the entire training was completed in about 15 seconds. However, the real challenge is fitting the model to data that the model has never seen before. Below are the predictions on the “test” dataset.
Though these predictions aren’t quite as good as on the training set, this is expected, and they are still very accurate overall and likely more precise than a human’s predictions.
What this could look like for you
- 3Blue1Brown- YouTube channel with amazing visual representations of abstract mathematical concepts
- Two Minute Papers- YouTube channel that distills recent machine learning research papers down to 2-minute videos
- Siraj Rival- YouTube channel with tutorials on lots of machine learning concepts
- Google Machine Learning Crash Course- Web tutorial series on machine learning (has more of a business/real-world focus)
- Machine Learning Mastery- Website with tons of machine learning tutorials and resources
- Colah's Blog- In depth explanation of ML concepts by Google Brain Scientist
- Distill- Explains abstract concepts through interactive machine learning widgets
This year it was clear that microservices are taking over. The longest lines were for sessions detailing how to run microservices on Docker or on how to implement them with Serverless techniques using AWS Lambda. The concept of breaking larger applications into smaller services is not new, but with the public descriptions of Service Oriented Architectures like those of AWS and Netflix, it is widely used. We're particularly interested in the organizational benefits of assigning small teams to building and supporting tightly-scoped, loosely coupled services.
An event like this is also a good opportunity to check in with services that have existed for a long time to stay current on new features. A good example of these are AWS Storage Gateway and Elastic Filesystem which have both seen further development since their initial launch. A service that impressed us thoroughly is Aurora: Amazon's enterprise-grade MySQL-compatible database engine. It shows how effectively AWS can squeeze out performance and reliability improvements when they are in control of the whole infrastructure stack. Several of our clients use Aurora, and it is good to see that this will pay dividends as we inherit regular improvements from Amazon's continued development.
But its not easy to get started. There is a rapidly growing list of services to learn in AWS alone, not to mention the other major providers like Microsoft Azure and Google Cloud Platform. Most of the vendors in the summit exhibit hall were involved in managing, monitoring, securing, or migrating cloud applications. You don't have to navigate this ecosystem alone. Leaf's Cloud Services team would love the opportunity to help!
Images Credit: https://aws.amazon.com/summits/chicago/
Leaf Software Solutions is proud to be celebrating our 30th anniversary providing technology solutions to our clients. With major changes happening on a constant basis, it is easy to lose sight at how much technology has grown over the course of multiple decades. We decided to take this opportunity to revisit some of the major shifts that have happened within our industry over the past 30 years, how Leaf has adapted to these changes, and trends we see emerging now in 2017.
By: Andrew Kaczorek and Chris Chalfant
The story of Joseph Graves Associates starts in the heyday of the original IBM PC. The PC AT shown above was driven by a 286 CPU that contained 134,000 transistors and performed an average of 2.66 million instructions per second. Compare this to a modern Intel i7 Haswell processor at 1.4 billion transistors that performs around 238,000 million instructions per second. That means that an average desktop computer is 90,000 times faster than the PCs used when Leaf began.
The next major shift happened in the early to mid nineties as the Internet became widespread. When Leaf began, the standard for business communications was a 2400 baud modem. In 1995, a typical business might have a 1.5 megabit connection, 685 times faster than the modem. A standard internet connection of 1 gigabit today is 500,000 times faster than network speeds prevalent when Leaf was founded.
Expect to see a wide variety of topics including:
- Cloud-native and serverless applications
- Continuous build and deployment automation
- Rapid prototyping practices
- Internet of things
Amazon Web Services
Best Companies Group
Best Places To Work
Chamber Of Commerce
Chief Financial Officer
Cloud Financial Software
Doc Date Verify
Dynamics 365 Apps
Indiana State Museum
Internet Of Things
Joseph Graves Associates
LCD Welcome Screen
Microsoft Dyanmics 365
Microsoft Dynamics 365
Microsoft Dynamics CRM
Microsoft Dynamics GP
Microsoft Visual Basic
Migrating Cloud Applications
Mobility & Security
Not For Profit
Off The Circle
Professional Services Tools Library
Project Service Automation
Python Tornado Web Framework
Repetitive Stress Injuries
Single-Page App Framework
Traditional Vs Machine Learning
Unit Test Fixtures