Do you want to know how machine learning infrastructure works? Do you find the idea of getting started daunting?
With the Global Machine Learning market expected to rise by around 40 percent year on year until at least 2024, more and more companies are getting involved. Machine learning is being used to solve some of the world's most pressing challenges.
It is even helping businesses solve internal questions that they might not otherwise have an answer to.
Keep reading to find out everything you need to know about Machine Learning Infrastructure - which we call MLOps here at Bitstrapped - and how to get started.
Machine learning infrastructure is made up of five key components that must work together to deploy machine learning models into production environments successfully.
All machine learning projects begin with data, which can be ingested and encoded for machine use through feature extraction. After the data has been prepared, it's split into training and testing datasets to train the model.
The machine learning algorithm is then applied to the training set, and the resulting model is validated before being deployed to live environments.
Selecting which machine learning model to use is one of the most important decisions you'll make when creating machine learning infrastructure. However, settling on just one with so many different machine learning models can be difficult.
Different machine learning models are designed for different machine learning problems, and some perform better than others in certain situations. For example, machine learning models that use deep neural networks can detect complex relationships between features in large datasets well but may require more time and computational power.
Some machine learning problems are resolved better by simpler algorithms such as linear regression or decision trees. In contrast, others may require a combination of machine learning models for optimal results.
Before machine learning infrastructure can be created, data must be supplied. This is often referred to as data ingestion. Data ingestion involves taking raw data and processing it into a machine-readable format that machine learning models can use to train themselves.
As every part of Machine Learning rests on having good data, the importance of good ingestion tools cannot be overstated. If you wish to have stable systems, these tools must be flexible, stable, and infinitely scalable. As you consider your toolset, you may come across both ETL Pipelines and Data Lakes.
Ingestion tools require access to all manner of data sources and, of course, ML Pipelines and storage.
Data Ingestion intends to make it easier for engineers to access data without waiting for often lengthy processing and loading.
ML Pipelines help to automate actions and system workflows. Practically, this means they consist of various machine learning components working together in unison.
When you're planning your infrastructure, make sure to decide whether you'd prefer to build out your own tooling or use pre-built offerings. For example, both Apache Airflow and ML Flow offer industry-recognized toolchains.
Visualization tools are essential for machine learning infrastructure development like machine learning pipelines.
Each machine learning algorithm is designed to detect different types of machine learning problems, so there isn't just one visualization tool that will work well across the board.
Some machine learning problems are better suited than others when it comes to visualization. Therefore, you should carefully evaluate each potential visualization tool before using it. Bad visualization can ruin an otherwise functional ML experiment.
You should build monitoring into your process in every step where it's possible to do so. Machine Learning is complex enough without losing sight of what's happening in the background.
Data selection and ingestion are only the first parts of machine learning infrastructure development. To deliver machine learning to your user or customer, you also need to ensure it performs as expected.
This process is often called Model Testing and can be broken down into three broad steps:
Testing machine learning models is far more complex than testing other software. The machine learning model itself may be worth the cost of running the infrastructure by itself, so you need to make sure it works well before releasing it into production.
Machine Learning features are not always extracted automatically by algorithms. Sometimes they need to be manually prepared for machine learning by engineers or data scientists. Feature engineering is about transforming input features into features that work better for machine learning problems at hand.
Model validation is a dynamic process that occurs throughout machine learning infrastructure development. It ensures that everything works as expected by measuring machine learning errors and comparing those with a desired model accuracy threshold.
The machine learning model has been successfully tested. Now it's time to release it into production. But, have you considered the infrastructure requirements for machine learning thoroughly? There are a few things you should make sure you cater for:
Machine Learning Infrastructure should be able to recover from errors in the machine learning process. Therefore, each machine learning component needs to have failover capabilities in case of machine failure or unexpected downtime.
Monitoring is essential in performant infrastructure for machine learning. Make sure your monitoring system can provide insights when something goes wrong with machine learning components that may lead to machine failure, unplanned downtime, or unexpected performance issues in production. You also need an alert system that warns you when components are failing.
To minimize machine downtime, machine learning infrastructure should be able to provide at least 99% availability guarantees. Even the smallest failure can translate into thousands of dollars lost if it happens in production.
Make sure to encrypt your machine learning pipeline endpoints with SSL certificates that only authorized users can access. Also, machine learning algorithms should run within isolated containers with limited privileges on shared computing nodes for maximum security and privacy.
The machine learning model has been trained, tested, and deployed. Now it's time to make inferences from machine learning models based on new data or machine feedback. This is where you will start to see the benefits of Machine Learning for your business.
There are two common approaches to machine inference:
In a machine learning pipeline, batch inference happens after machine training is finished and the model is deployed to production. It allows you to process machine learning results offline by summarizing its output into a single dataset that can be used for further processing like statistical analysis, visualization, etc.
Batch inference is usually more computationally efficient than online inference because it requires fewer network roundtrips while providing generally consistent machine learning outputs across different datasets.
Online machine inference happens as soon as machine learning models detect changes in the production environment. It provides real-time predictions for dynamic machine learning environments where new data is constantly arriving. This data, along with feedback based on previous errors and performance metrics, is continuously refined and made better.
Having a solid MLOps strategy deploying a machine learning infrastructure allows you to start small, grow fast, reduce costly mistakes, release products more frequently, improve the quality of services, and make every development cycle count by providing exactly what you need within budget constraints.
If all this seems daunting, get in touch today, and our team of MLOps experts will guide you through the entire process from start to finish. You'll be able to start on your ML journey quickly and easily. A 30-minute discovery call with our experts is free.
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