Most business-critical software was hosted on private data centers in the early 2000s. With time, enterprises overcame their skepticism and moved critical applications to the cloud.
The shift to the cloud was spurred by the fact that it gave decision-makers a sense of control over business-critical applications hosted outside their own data centers.
One of the factors behind the change is machine learning, and it is happening in a similar way as enterprises try out and accept it.
Similar to cloud-native startups, many are now offering differentiated products. A vast majority of large and mid-sized enterprises are struggling to bring functioning models to production or just trying out new applications.
Here are some challenges that can be solved with the help of MLOps.
It is difficult to get cross-team collaboration to work.
A simple model that predicts Churn may be as simple as a model that determines the price of a ride in San Francisco. Creating a model and enabling teams to benefit from it is a complex endeavor.
The early days of the MLOps space have huge potential because it allows organizations to bring artificial intelligence to production environments in a fraction of the time it takes today.
Multiple teams need to coordinate to continuously monitor the models for degradation in order to train them.
There are three core roles involved in modeling.
Data engineers are trained to clean and store data in the right formats so that it can be used for analysis. Data engineers are well versed in handling static and streaming data sets and play with tools like ETL/ELT. A data engineer might create a high-level data pipeline.
The image was taken by Ashish Kakran.
Data scientists can run complex regressions in their sleep. Data scientists use common tools like the Python language, Jupyter notebooks and Tensorflow to analyze data provided by data engineers and create a highly accurate model. Data scientists like to try different models and compare them for accuracy, but after that someone needs to do the work to bring the models to production.
If something goes wrong, an artificial intelligence engineer can quickly detect the issue and start the resolution process.
The three critical personas are able to collaborate to deliver successful artificial intelligence implementations.
There are more than one ML tools.
In the new world of developer-led, bottom-up, teams can choose from a plethora of tools to solve their problems.