Machine learning (ML) will drastically alter how many industries operate in the future. Natural language processing will enable seamless and instantaneous language translation, forecasting algorithms will help predict environmental trends, and computer vision will revolutionize the driverless car industry.
Nearly all companies that have initiated ML programs have encountered challenges or roadblocks in their development. Despite efforts to move toward building robust ML programs, most companies are still at nascent stages of building sophisticated infrastructure to productionize ML models.
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.
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.