How to Organize Your Machine Learning Project

Carefully created custom systems

Having zero bottlenecks

Creating scalable algorithms

Implementing version control

Code testing

Up-to-date documentation

Checking these items usually means you’ll deliver great stuff. What does this have to do with today’s newsletter? Well, in case you’ve been living under a rock, the importance of Machine Learning in our future will rise.
Future challenges are complex and only complex systems will solve them.
This means the importance of highly organized data and clear workflows for Data Analysts and ML Engineers will rise, also. As will the importance of productive collaboration between team members and stakeholders.
So, with that in mind, let's dive deeper into the critical organizing factors that mean the difference between success and failure for machine learning projects today.

1. Machine Learning project = Partnership

It might sound obvious, but... excellent communication, attention to detail and ability to work in a team are top skills that ML engineers should have.

2. Privacy Matters

In short: data is your gold - protect it. Creating a simple and organized "working space" often has the fringe benefit of reducing security risks. Exposing credentials or data by having a bad process and a messy, unsecure environment, while running experiments, it's just too devastating. Protecting your sensitive data just another high priority task. There is no success if you'll just lose your data.

3. Have a clear problem understanding and success metrics

When you are working as an enterprise unit (i.e. you not doing science research), you need to understand more than just the “math” behind your data science project.

You'll need to:

Understand the systematic interdependencies

Create cool software that will be easy to integrate and use

Understand how different gears work together, connecting to each other (via API calls for example) and create another tool that will interface with other working parts

Don’t forget that Machine Learning product is actually working software!

4. Choosing the correct algorithm

It might be mind-boggling —there are dozens of supervised and unsupervised machine learning algorithms, and each takes a different approach to learning.

There is no best way or one size that fits all. Discovering the right algorithm is partly just trial and error — even highly experienced data scientists can’t tell whether an algorithm will work without trying it out. Algorithm choice also depends on the size and type of data you’re working with, the insights you want to get from the data, and how those insights will be used.

How to keep growing in this complex world?

As more organizations start to examine and invest in machine learning and artificial intelligence, they’re looking to obtain more experts to blend these technologies into their business actions.
Data science and Machine Learning challenges such as those on Kaggle are a great way to get exposed to different kinds of problems and their nuances.
P.S. wanna get hackernoon’s top tech stories delivered directly to your inbox at noon (somewhere) err’day? subscribe to our all-new noon notification newsletter right here, right now.

even more ML stories

If you have a few (or many) experiences to share, you can become a contributor on Hacker Noon too. Simply create an account, and join 10,000+ contributors sharing their knowledge and expertise with the rest of us. Maybe you'll get featured on our next newsletter too...