MLOps - Machine Learning Operations

MLOps (Machine Learning Operations) are practices, methods and tools that are used to manage and implement machine learning-based solutions The result is improved performance, quality and reliability of these solutions. As Hostersi, we implement and optimize solutions, based on Amazon Web Services, that will allow the effective implementation of machine learning models.

MLOps combines elements from fields such as software engineering, machine learning, DevOps, cloud computing and process automation. As a result, it enables the effective deployment of machine learning models, the automation of processes related to the management of these models, as well as their rapid iteration and update.

As Hostersi, we help automate and optimize the processes involved in creating and maintaining machine learning models:

• we reduce the time it takes to put machine learning models into production,
• we increase the reliability and quality of models,
• we streamline the process of updating and maintaining models,
• we improve the performance and scalability of machine learning-based systems,
• we increase transparency and control over machine learning processes.

MLOps - what we do together with the customer

Our goal is to help get the scripts up and running on AWS, "at scale," and ideally, run and manage them in the most automated way possible. Most often this is done in the following few steps:

• we design and implement the Data Lake on Amazon S3 (structure, format and data content),
• we design and implement (or help implement) the process of loading data (ETL) into the Data Lake,
• we help choose the optimal option to run the Machine Learning environment, and then configure the environment, most often it is Jupyter on, for example, AWS SageMaker or regular AWS EC2 on AWS Deep Learning AMIs.

MLOPS - what we do ourselves

• we prepare networks (Amazon VPCs, Security Groups, etc.) and Amazon EC2 instances, secure them and make them securely available to the user,
• we optimize the management of the Machine Learning environment from a cost perspective, e.g. by implementing automatic shutdown of notebooks/instances to reduce costs.

MLOps - what the customer does

• the client prepares a data sample for analysis and implementation of Machine Learning scripts (to validate and clean the data, train and test the model, run the model on a sample of production data) e.g. in Python language on a local Jupyter notebook.

Summary

Within our organization, we have structures specialized in MLOps, based on Amazon Web Services. We have years of experience in configuring the network so that importing data from outside to the cloud is secure, fast, stable and reliable. Our experts will be happy to answer your questions and provide you with a detailed cooperation offer.

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