The roadmap of machine learning to real world problems is constantly accelerating. Each year, there are more and more things that machines can now do that only humans could only perform before.
With the race to superiority in machine learning, the pace of technology is gaining momentum every day, and you see it in the announcements, discoveries, and advances that are announced each week.
Algorithmia has come up with an ML Roadmap, that you can download and put to use right away.
Here’s some commentary on the ML Roadmap:
After surveying hundreds of companies, Algorithmia has developed a roadmap that outlines the main stages of building a robust ML program as well as tips for avoiding common ML pitfalls. We hope this roadmap can be a guide that companies can use to position themselves for ML maturity.
Keep in mind, the route to building a sophisticated ML program will vary by company and team and require flexibility.https://info.algorithmia.com/machine-learning-roadmap
Using the Roadmap
Every company or team is situated at a different maturity level in each stage. After locating your current position on the roadmap, we suggest the following:
Chart your path to maturity
Orient and align stakeholders
Navigate common pitfalls
The roadmap comprises four stages: Data, Training, Deployment, and Management. The stages build on one another but could also occur concurrently in some instances.
Data: Developing and maintaining secure, clean data
Training: Using structured datasets to train models
Deployment: Feeding applications, pipelining models, or generating reports.
*Models begin to generate value at this stage.*
Management: Continuously tuning models to ensure optimal performance
Pinpointing Your Location on Algorithmia’s Roadmap
At each stage, the roadmap charts three variables to gauge ML maturity: people, tools, and operations. These variables develop further at every stage as an ML program becomes more sophisticated.
For more information about building a sophisticated machine learning program and to use the roadmap, read our whitepaper, The Roadmap to Machine Learning Maturity here:
With this roadmap to machine learning maturity, organizations can map their progress to maturity and measure where they stand on the ML roadmap.