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. The possibilities are almost literally endless, and organizations are beginning to realize the ways in which machine learning can benefit them. Companies have begun implementing machine learning into their business processes, and there is a lot to be learned from their experiences.
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 the nascent stages of building sophisticated infrastructure to productionize ML models. There is room for improvement, and Algorithmia has outlined the challenges that have arisen thus far along with potential solutions to help organizations avoid the common pitfalls of enterprise machine learning.
Machine Learning Roadmap Overview
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.
This roadmap is intended to act as a framework for organization management and data scientists to successfully implement AI within their companies. Follow each step to avoid the challenges ML pioneers faced and chart your course to machine learning maturity through ML workflow optimization.
Whether you plan to use deep learning algorithms, neural networks, a combination of programming languages such as Python, and any other machine learning tools, the roadmap will ensure your project has value.
The following is a preview of our roadmap, but if you’re ready to jump right in, you can download the full roadmap here.
Using the Machine Learning Roadmap
Every company or data science 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 – Chart a path to get from your current location on the roadmap to the goal destination you’ve pinpointed for your organization. Defining the path you will take is crucial to reaching maturity. You must be sure of your process but also willing to be flexible to accommodate any challenges you encounter on your unique journey.
- Orient and align stakeholders – Enterprise ML projects require buy-in from DevOps, IT, product teams, data scientists, ML engineers, and the organization’s leadership. Gaining support from each of these groups needs to be part of your plan, which is why it’s included in the roadmap.
- Navigate common pitfalls – Surveying hundreds of companies that have implemented ML has shown us the most common mistakes organizations make in this endeavor. For example, many organizations strive to be perfect or reinvent the wheel. These flawed goals will delay your process and impede your progress toward ML maturity.
The roadmap comprises four stages: Data, Training, Deployment, and Management. The stages build on one another but could also occur concurrently in some instances. Whether you follow these stages in order or pursue some simultaneously, it is important to address every stage at some point on your machine learning path.
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 to Machine Learning Maturity
Getting started on your journey to ML maturity is exciting, but don’t get ahead of yourself. The first step, even before charting your path or aligning your stakeholders, is to pinpoint your organization’s current location on the Roadmap to Machine Learning Maturity. To figure out how to reach your destination, you have to know where you stand right now.
At each stage, the roadmap charts three variables to gauge ML maturity: people, tools, and operations. These are the variables you should use to determine your organization’s current location on the ML roadmap. In the full Roadmap to Machine Learning Maturity, you will learn how to rate these factors within your organization as either low, medium, or high maturity. That way, you will be aware of the strengths and weaknesses that you have to work with, and you will better understand how to get each of these variables to high levels of maturity. These variables develop further at every stage as an ML program becomes more sophisticated so you can use this framework to track your progress as you get closer to maturity.
For more information about building a sophisticated machine learning program and to use the roadmap, read our whitepaper, The Roadmap to Machine Learning Maturity.