1. The business arguments for MLOps
Welcome back! Let's discuss the business arguments for MLOps.
2. MLOps unlocks new capabilities
Let's look at a scenario to understand the business impact MLOps can have. CreaData owns its entire supply chain process and transfers raw materials into finalized sporting equipment such as rackets and balls.
They sell their products in 100+ stores across the country, but they are running into a few issues. They have a variety of products out of stock in different areas. CreaSport has a one-size-fits-all approach which doesn't account for regional differences. The tennis products are regularly out of stock in the New York City area, and the pickleball equipment is regularly out of stock in Chicago. Their second problem is that the finance analyst discovered they sell certain products at a loss. Raw material costs go up and down, so full visibility of the supply chain is necessary to set future prices and proactively react to market price changes.
3. MLOps unlocks new capabilities
MLOps can help solve this problem by creating and automating data pipelines to forecast demand planning.
This results in increased business value by reducing the number of empty shelves in stores and thus increasing sales.
Secondly, it also enables CreaData to optimize prices based on raw material costs. By creating full visibility across the supply chain, CreaData is able to implement raw materials price changes immediately to the end consumer at the moment they occur.
4. Why do we need MLOps?
Why do we need MLOps?
First of all, we need a professional and efficient approach to productionalize our models and realize value. This is especially true if we want to scale ML and manage hundreds of models in parallel. Generating business value through MLOps can mean increased productivity, reduced time-to-market, reduced costs, and better planning or decision-making.
Without MLOps, we may create business or security risks. Applications might be unavailable for our customers, or even worse, third parties might gain access to internal data.
It will also help to understand what machine learning applications are doing and how they affect business outcomes. This is crucial to ensure knowledge availability in case of staff turnover. It is also vital for the management to understand these applications on a high level to align machine learning applications with the broader business strategy.
5. What will MLOps bring for businesses: examples
Below are some examples of the business impact of MLOps on selected firms. The Adecco Group, a staffing firm headquartered in London, used MLOps, for example, to automatically pre-review job applications. This led to a 37% reduction in job applications, which needed to be reviewed manually. Before joining DataCamp, I co-developed an automatic credit scoring tool for BASF, a global chemical company, to automate credit management decisions.
6. MLOps is challenging
But be aware MLOps is challenging!
MLOps is much more than modeling and delivering accurate models.
It requires not only profound machine learning skills: developing, testing, and deploying models. We also need data engineering and software engineering competencies.
Data engineering is responsible for managing the flow of data. We need the right data to be continuously available. Software engineering ensures that the MLOps application fits neatly into the wider software landscape. And then there is still the architecture to be set up and maintained to operate our machine learning models.
7. How do we start?
So, how do we start?
Successful machine learning applications need considerable initial investments in technology and people. They also require diverse skills to be set up and maintained and a cultural change towards continuous improvement. We will talk about this in much more detail in the next chapter.
8. Let's practice!
Let's practice!