Machine learning solutions and workflows are meant to save time and vastly improve operational efficiency, but you still need the right human team to ensure every aspect is optimized and running in all dimensions. These folks know enough to have a sense of good use cases for machine learning. Where everyone gets stuck is actually making it work, hiring people and making them successful.
There is no "one size fits all" solution, when it comes to manage successful ML teams.
The Business Problem needs to be Solved
Before getting started with finding the right people, you should take stock of the business problem at hand. The goal of an ML initiative may be to optimize rote business processes (e.g. automation) or it may be to establish a core piece business offering. No matter the case, it is imperative to first establish how the ML model fits within the greater workflow. Once your organization understands the implications of ML on the business, then it can begin to assemble the optimal team. And in most cases, you won't need to hire a full-stack ML team. Identify your organizations archetypes and their Machine Learning maturity level.
Most successful data-driven companies address complex data science tasks that include research, use of multiple ML models tailored to various aspects of decision-making, or multiple ML-backed services. In the case of large organizations, data science teams can supplement different business units and operate within their specific fields of analytical interest. Obviously, being custom-built and wired for specific tasks, data science teams are all very different. Find ways to put data into new projects using an established Learn-Plan-Test-Measure process.
Challenges for startups
Startups, in the early stages of operations, are typically bootstrapped and have limited budgets to deploy for building machine learning teams.
It might feel impossible for a small, unfunded or underfunded company to get machine learning expertise in-house. At this size, you are going to rely on your ML practitioner to implement everything end-to-end from data collection and cleanup to deployment. The actual machine learning-specific part of the task is almost certainly very small. If your startup has a core product or service founded on ML, then it's imperative to hire machine learning talent early on to build the MVP, and raise funding to hire more talent and scale the product.
Require more hands-on machine learning talent that can operate across the entire machine learning lifecycle - from data engineering, algorithmic and model development to deploying and monitoring machine learning models in production instead of specialized talent to focus individually on the various aspects of the machine learning lifecycle.
A seasoned engineer who has gone back to school or done some online work in machine learning can work out well because the goal isn't perfection- it's getting a system working end-to-end and then slowly optimizing all the steps. Hiring someone who is heavier on the engineering and data side of things is definitely the way to go.