Building the Right Architecture for MLOps

Machine learning projects are expanding, with the global machine learning (ML) market expected to grow at a CAGR of 38.8%, from $21.17 billion in 2022 to $209.91 billion by 2029. To accelerate the speed of development and shorten the time to market, businesses are combining DevOps principles with ML development and data engineering. Called MLOps or Machine Learning Operations solution, it involves streamlining the production, maintenance, and monitoring of machine learning models and is a collaborative effort between IT, data scientists, and DevOps engineers. It involves automating operations using ML-based approaches to customize service offerings and improve productivity and efficiency.

Some of the benefits of MLOps include faster and easier deployment of ML models. It helps with continuous improvement in a cost, time, and resource-efficient way by facilitating collaboration between different teams and tasks. The models can also be easily reused for other use cases using MLOps. As validation and reporting are an integral part of the system, it makes monitoring easy.

Preparing to Build the MLOps Architecture

The development of an MLOps project is just like any other project, but with a few additional steps to ensure an easy and seamless flow.

● Setting up the Team: Planning and assembling the right team is the first step. Depending on how complex the project is, the team will include one or more ML engineers, data engineers to manipulate data from various sources, data scientists for data modeling, and DevOps engineers for development and testing.

● ETL: For data modeling to develop machine learning algorithms, data needs to be extracted from various sources, and a pipeline created for seamless data extraction in the system. The data needs to be cleaned and processed using an automated system that helps with seamless transformations and delivery.

● Version Control: Like in DevOps, version control plays an important role in MLOps too, and Git repository can be used for this as well.

● Model Validation: In DevOps, testing is important and includes unit testing, performance, functionality, integration testing, and so on. The equivalent in an ML project is a two-step process – model validation and data validation.

● Monitoring: Once the software has gone live, the role of DevOps ends until the time of further enhancement. In an MLOps project, though, periodical monitoring of ML model performance is essential. This is done to validate it against the live data using the original validation parameters. This will help identify any problems, and the modeling will have to be reworked

Must read: MLOps on AWS: Enabling faster

Databricks for MLOps Architecture: 5 Things to Consider

While this makes MLOps sound ideal and easy, in reality, one of the challenges it faces is the need for a huge infrastructure, including computing power and memory capacity that on-premise systems cannot meet without additional costs. Therefore, cloud architecture is a better alternative that allows for quick scaling up and down based on need and thereby keeps costs based on need.

It also needs constant monitoring due to the ever-changing requirements and the need for the models to reflect these changes. As a result, businesses must frequently monitor the parameters and modify the variables of the model as and when required.

Some key challenges may also arise in MLOps with regard to managing data, code, and models across the development lifecycle. Multiple teams handling the various stages of development, testing, and production collaborate on a single platform, leading to complex needs for access control and parallel use of multiple technologies.

Databricks, with its robust ELT, data science, and machine learning features, is well-suited for building the MLOps architecture. Some of the factors that make Databricks consulting services ideal for MLOps include:

● Lakehouse Architecture: Databricks uses a Lakehouse architecture to meet these challenges and unify data lakes and data warehouse capabilities under a single architecture and use open formats and APIs to power data workloads.

● Operational Processes: The process of moving the ML project through the development cycle should be clearly defined, covering coding, data, and models. Databricks allows the code for ML pipelines to be managed using the current DevOps tooling and CI/CD processes. It simplifies operations by following the same deployment process as model training code for computing features, inference, and so on. MLflow Model Registry, a designated service, is used to update code and models independently, enabling the adaption of DevOps methods for ML.

● Collaboration and Management: Databricks provides a unified platform on a shared lakehouse data layer. In addition to facilitating MLOps, it allows ML data management for other data pipelines. Permission-based access control across the execution environments, data, code, and models simplifies management and ensures the right levels of access to the right teams.

● Integration and Customization: Databricks used open formats and APIs, including

– Git

– Related CI/CD tools

– Delta Lake and the Lakehouse architecture

– MLflow

Additionally, the data, code, and models are stored in open formats in the cloud account and supported by services with open APIs. All modules can be integrated with the current infrastructure and customized by fully implementing the architecture within Databricks.

● Managing the MLOPs Workflow: Databricks provides developers with a development environment to allow data scientists to build the ML pipelines code spanning computation, inference, model training, monitoring, and more. In the staging environment, these codes are tested and finally deployed in the production environment.

Check out our MLOps solution Capabilities

Indium’s Approach

Indium Software has deep expertise in Databricks and is recognized by ISG as a strong contender for data engineering projects. Our team of experts works closely with our partners to build the right MLOps architecture using Databricks Lakehouse to transform their business. Ibrix is an Indium Unified Data Analytics platform that integrates the strengths of Databricks with the capabilities of Indium to improve business agility and performance by providing deep insights for a variety of use cases.

Inquire Now! To know more about how Indium can help you build the right MLOps architecture using Databricks solutions.