When you’re tasked to oversee an organization’s foray into AI or ML, one of the most critical considerations is learning how to build a machine learning team. Once you’ve overcome this hurdle, the next big challenge you’ll face is to keep it running at 100%.
Machine learning projects require the right combination of experts and stakeholders to define success and execute your plan. However, as with any other workforce, ML specialists will come and go from the team. The complexity of the MLOps stack means you need robust and repeatable operations so that when your engineers go on vacations, take family-related leave, or take advantage of new opportunities, your operations stay stable.
Fortunately, continuity gaps do not have to cause your existing efforts to slow down or grind to a halt. The following are a few ways to regain momentum in a machine learning team that is continually shifting.
ML Hiring Tip: Use Language-Agnostic Tools
A crucial piece of advice on building your machine learning team is determining the necessary skillsets every project should have. For example: data engineers, data scientists, machine learning engineers, and research scientists should have the relevant skills and tech stack knowledge to see through the various stages of the ML lifecycle.
Who’s writing code?
Here’s a quick look at the titles and languages you’ll find on machine learning teams:
If your data pipeline tools rely on expertise in one specific language, the absence of key team members can impede project success. Incorporating language-agnostic solutions like Pachyderm into your tech stack helps ensure your machine learning teams can work without interruption when capacity challenges arise.
Skip the Self-Built Systems
Self-developed technologies are often proposed to alleviate an organization’s cost burden with machine learning projects. However, they can yield inconsistent long-term results. Ensuring continuity of support is a challenge when AI, ML & Data Science professionals are at the top of every tech recruiter’s list right now (we would know.. we’re hiring them, too).
The opportunity cost of losing the lead on a bespoke system can be high. When considering a self-built data pipeline project, do not underestimate the future investment that must be dedicated to troubleshooting issues, building new features, and the accompanying technical debt.
ML operations scaled on brittle data pipelines are more likely to stop or slow down as bugs crop up unexpectedly. Most self-built tools are not designed for scale or support, so be prepared for models to fail or deliver inconsistent results every time a new version gets released.
Build for Reproducibility
Like any continuous delivery workflow, machine learning data and models constantly change and update. Communication is vital to help new hires integrate into the team and preserve project momentum. One way to achieve that is through repeatability: knowing how your data has been used before and what it has produced helps provide your teams with a better picture of where a project is going and demonstrate their work so far and track the consistency of their results.
Automated data versioning preserves repeatability and continuity even as teams change. Pachyderm’s git-like data lineage preserves the history of transformations undergone by data used in ML models. As a result, your team can keep track of all data changes across pipeline stages and versions for total data and model reproducibility.
With Pachyderm, even as your machine learning team changes, they can repeat ML processes with the same or different datasets to identify patterns, gain invaluable insights, or determine areas for improvement. Let us show you how with a demo!
Bridge Continuity Gaps with Pachyderm
The business value of machine learning can be massive if an MLOps stack is built to be resilient. Once you have built a machine learning team, it’s also imperative to understand and take steps to ensure continuity. With a robust data science foundation, you can hire new talent faster, retain more people, and stay ahead of the competition.
For further reading on building sustainable ML teams, learn to Reduce the time to value of your ML projects with the Three Core Principles to Accelerate Machine Learning Success.