The thing about machine learning is that almost anyone can do it successfully. It doesn’t take much to get a Jyputer notebook and work on scraped data. You can get into the Jupyter lab environment to do almost anything you want in the R programming language. However, how do you actually transition from a Jyputer notebook to the real world? The question of operationalizing machine learning is typically where most engineers get stuck.
It is a problem for them to understand the fundamentals of designing machine learning algorithms and gathering the data needed to build a model. However, MLOps is the area where most people get stuck because they don’t understand the complexities of running a machine learning model in a production environment. The difficulty you go through is much worse than with other machine learning activities. What is Jupyter notebook used for? That is a question many people need to ask because they don’t know how to connect machine learning as a toy versus machine learning in a production environment doing work for business.
Jyputer Notebook Example
A big example of working with a Jyputer notebook can be found in doing basic data science projects on the Internet. For example, there was one user who was able to scrape ads on a website and process the data using exploratory data analysis. That person was also able to create a model and evaluate it to a point where it performs exceptionally well. However, the issue came when that user needed to take that model from their Jupyter lab and move it to a production environment. You can use tools like Python and its associated libraries to do many great things in the machine learning world. However, can you use these tools to build a model in a production environment?
The Reason MLOps Exists
As you can see, there are tremendous challenges when moving from Jupyter lab to a production environment in machine learning. These challenges led to the creation of MLOps. MLOps are tools and practices that aim to standardize how we bring machine learning models to production. By focusing on common standards, we are able to develop ways of bringing a machine learning model to production in a way that almost anyone can achieve.
The focus is on continuous delivery and continuous integration. You also have the same version control system for code and machine learning models. The combination of all of these items gives you an important machine learning system that delivers results. However, it is crucial to understand that machine learning models don’t always get into production with MLOps. That is because a few challenges still need to be overcome.
Issues with MLOps
The main thing is that MLOps is based on our learnings in DevOps. Because of that, the same principles and downsides still apply. You also have the fact that a machine learning model can potentially decay and get worse over time. That means you need to always be on your toes by keeping different versions of your model. You also have the problem of evaluating and monitoring your model.
Your model won’t always perform as you want because things can change wildly in a production environment. For example, something drastic could happen that causes a significant change in your data. Any of these issues can change the way your machine learning results become. Because of that, you need to master all the principles and practices to achieve MLOps success in the machine learning world.
xpresso.ai Team Enterprise AI/ML Application Lifecycle Management Platform