Machine learning and machine learning operations are big terms in the world of tech these days, with everyone clamoring to get on the ML bandwagon. But what exactly does this entail, and is it possible for relative novices to get involved? Indeed, using MLOps can sound like an intimidating prospect if you aren’t well versed in the practices. Technical processes can seem overwhelming in general, and ideas related to artificial intelligence probably deter many people from getting involved at all.
The field doesn’t have to be so complicated, and, in fact, there are quite a few sources out there to help guide you through the processes involved. There are projects that you can undertake to help familiarize you with how MLOps works, and prepare you for doing even more significant things in the future. Here are a few ideas to get you started.
How does MLOps work?
Machine learning is a branch of artificial intelligence that uses algorithms to try to replicate human behavior in machines. Machine learning operations, then, are a way of operationalizing machine learning—in other words, putting it into practice. The goal is to make ML more efficient and productive by means of utilizing automated functions to create models of datasets used in artificial intelligence. In using automated functions, data scientists are able to create models more efficiently than they otherwise would if they were forced to work with the data manually.
MLOps is being used in more and more areas as these fields grow and become more refined. Some examples of large-scale industries employing MLOps include the following:
- Logistics – A number of different logistics companies have been using ML to create personalized travel arrangement recommendations for customers.
- Retail – Retail companies are using ML to refine fraud detection in order management.
- Manufacturing – Companies create better classification systems by means of using ML to better understand personal preferences in product identification.
These are just a few examples. Since ML is starting to be used everywhere, even the casual observer would do well to learn its basic principles.
Where can you begin if you want to start using MLOps?
Now that we’ve laid out the basic concepts, it’s time to look at some ways that beginners can approach MLOps and create simple projects to get started. Fortunately, there are quite a few tools available online that you can work with. GitHub, for example, is a subdivision of Microsoft that houses numerous tools to help people in their development efforts.
Create a feature store
One project that you can work on is the creation of a feature store. This is a function of ML that allows users to centralize the storage and operations of features that are often used in data processing. When one is developed, the data is made easily available for usage in the creation of ML models in the future. The goal is to streamline and expedite the processes involved in accessing and using data. Many companies use these as a fundamental part of their operations.
There are three major types of feature stores:
Literal – The literal component is the place where features are actually stored. It is simply a repository with no other function than storage and is used for data that will remain static and not be subject to change.
Physical – The physical feature store both computes and houses features. This is the type most commonly used among vendors.
Virtual – The virtual feature store allows for the computation of data, but does not perform this function itself. Rather, it stores data in a cloud and offloads the computation function to another infrastructure.
How can you build your own feature store?
Feature stores contain several major components:
- An offline feature store – An offline store will house large amounts of data from which datasets will be created. Offline stores usually exist in the form of distributed file systems.
- An online feature store – This is where a feature vector is kept that will be used to make predictions for future models. Online feature stores are generally created as key-value stores.
- A feature registry – Feature registries house the metadata of both offline and online stores.
Databases that are commonly used in feature stores include Apache Hive and BigQuery. In order to create your feature store framework, you can use a tool called Butterfree. Butterfree assists users in creating frameworks while enabling the sharing of data across different teams who want to use common data for different purposes. It works by extracting, loading, and transforming layers of data.
Build your own chatbot
Another fun and useful idea that can be utilized without too much hassle is the creation of your own chatbot, particularly if you’ve got a potential client base that needs answers 24 hours a day. It would be to your advantage to understand the mechanisms behind chatbots in order to keep site visitors happy and make sure they stay engaged.
In order to get started, you’ll need to consider a few basic things:
- What type of information do you want to provide?
- How detailed do you want the language of your chatbot to be?
- How many degrees of follow-up do you want your chatbot to be able to provide?
Chatbots can be created using Python. There are three major components that you’ll need to be familiar with:
- The neural network – The neural network of your bot is reflective of the neural network of a human. It is the network through which language functions operate.
- A bag-of-words model – This is a model that will serve the function of an algorithm when processing speech on your network.
- The lemmatization process – What this refers to is the formation of a dictionary of root words from which other words are formed. It is essentially a repository of types, and conjugations, word patterns, are all based upon the roots kept in it.
You can carry this project out with help from the Rasa tool. Rasa provides a framework for the creation of chatbots by means of utilizing natural language understanding (NLU) together with Python. It will allow you to customize the features of your bot as you wish, and program your desired language accordingly.
Just the beginning
These are just a couple of the ways that you can get started with MLOps. There are many projects out there that involve similarly straightforward mechanisms. Once you’ve mastered the basics, you’ll be able to move on to more sophisticated projects.
It is worth taking the time to learn the fundamentals and build a few projects. MLOps will soon become universal, so you want to stay on top of things and keep your business up to speed with the latest developments.
