Today, when the world relies heavily on technology, ML and cloud computing are some of the most powerful technologies. Regardless of the size of the organization, MLOps consulting plays a key role.
This article covers machine learning operations, cloud computing, and the top 3 cloud-based MLOps.
MLOps – what is it?
MLOps is a set of practices that improve communication and cooperation between Data Scientists and others. These activities aim to avoid Machine Learning solutions getting stuck in the experimental stage.
Cloud computing
Cloud Computing is a technology for data processing and storage. However, the data isn’t stored on computer disks, but in virtual space, on servers outside the local network, or in the so-called cloud. Computers, tablets, and laptops are the only tools thanks to which we have access to data. There are many classifications of cloud computing. From the business point of view, we have three different types of clouds:
- Private
- Public
- Hybrid
Private clouds are created for specific enterprises. This infrastructure can be managed by an external company or an internal IT department. The public cloud is operated by an external company and ties in with the “on-demand” model. The client determines which services he wants to use and pays for these parameters exactly. Hybrid clouds are a combination of private and public clouds.
Machine learning and cloud computing
Both Machine Learning and cloud computing play a huge role in the development of the company. ML is responsible for creating intelligent machines, while cloud computing ensures data storage as well as access security.
To create a machine learning algorithm, you need a lot of processing power, a lot of data storage, and multiple servers working at the same time. A significant role of cloud computing is that it provides new servers with predefined data and allows them to switch resources over to the cloud. By using cloud computing, you can spin up as many servers as you like and work on the algorithm.
MLOps cloud-based tools
It is possible to host Machine Learning operations both on-premises and in the cloud, with each having its advantages. Today we will focus on cloud-based MLOps that provide organizations with many opportunities. For example, cloud service providers (CSPs) can offer you any tools and compute capabilities you need for your MLOps processes. The goal of this section is to highlight 3 of the most popular cloud-based MLOps tools, i.e., Microsoft Azure, Google Cloud, and Amazon Web Services (AWS).
GOOGLE CLOUD
Amazon SageMaker is an open-source machine learning library released by Google. The TensorFlow library works on the principle of deep learning of user behavior by analyzing and flexible data processing on computers, smartphones, or server rooms. In addition to the library itself, Google has also made available models of neural networks that recognize photos and handwriting and analyze text and speech.
Another example of Google’s offer is Google Kubernetes Engine (GKE). It is an open-source platform for managing, automating, and scaling containerized applications. Kubernetes Engine is designed to simplify Kubernetes runs. Your workstations and server will need less maintenance, and you’ll be able to spend more time developing and maintaining your models. Thanks to containers, you can be sure that your applications have everything they need to run. As you add them, you can automatically manage and distribute them using Kubernetes.
AWS
Amazon SageMaker is a comprehensive ML AWS platform that offers the following services:
- Data preparation
- Building models
- Model training
- Implementation of models
- Model management
Thus, Amazon SageMaker is a service that provides all developers and researchers with the ability to quickly build, learn, and deploy Machine Learning (ML) models. SageMaker simplifies all stages of the difficult machine learning process to help you develop high-quality models.
AWS offers the following additional services:
- SageMaker Autopilot – A tool that allows you to create and train models using your data, which you can then deploy via the AWS Pipeline.
- AWS CodeBuild – Builds source code, performs tests, and creates and deploys code packages. An automated CI/CD pipeline can be created with CodeBuild and AWS CodePipeline.
- AWS CodePipeline – Provides continuous delivery of code changes. With it, chances are implemented iteratively in ML models already at the production stage.
AZURE
Azure DevOps is a CI/CD tool for collaborating on the development of the code and application across teams. Organizations can choose between on-premises and cloud options based on their budgets and requirements. GitHub, Campfire, Slack, Trello, and many more are supported by Azure DevOps.
Azure ML Pipelines is designed to facilitate all aspects of the ML life cycle. It provides not only data preparation but also model training, validation, as well as monitoring.
Conclusion: Find out more about MLOps consulting
Every year the demand for machine learning and cloud computing is growing steadily. It’s the same story with MLOps consulting. No wonder, since cloud computing offers the perfect environment for ML models with a lot of data. Therefore, when an organization uses both of these technologies, it can get really great results.
