MLOps Services Tools and Comparison | A Quick Guide

What is MLOps?

Artificial Intelligence and machine learning (ML) applications are no longer the buzzwords of research institutes; they are becoming an essential part of any new business growth. According to business analysts, most organizations can still not successfully deliver AI-based applications. They are stuck in applying data science models (which were trained and tested on a sample of historical data) into applications that work with real-world and massive data.

An emerging engineering practice called MLOps can address such challenges. The name indicates that it aims to unify ML system development (Dev) and ML system operation (Ops). Automating MLOps means automating and monitoring all ML system construction steps, including integration, testing, releasing, deployment, and infrastructure management.

According to the survey, data scientists are not focused on data science tasks. They spend most of their time on other relevant tasks such as data preparation, data wrangling, management of software packages and frameworks, infrastructure configurations, and integration of various other components. Data scientists can quickly implement and train a Machine Learning Model with excellent performance on an offline dataset by providing relevant training data for particular use cases. However, the real challenge is not to build an ML model. The problem lies in creating an integrated ML system and continuing to operate it in production.

Machine Learning has become so prevalent that it is now the go-to method for businesses to handle various issues. Click to read about Fairness in Machine Learning

Machine Learning Model  Operationalization

Today, businesses are searching for ways to put Machine Learning in their arsenal to improve their decision-making. But in reality, it has been seen while adopting ml in business workflow, and organizations face many problems. The main problem of the organizations is that they need help to produce the model and extract the business value from it. So here comes MLops in the picture. Inspired by the principles of DevOps, it tries to automate the whole ML lifecycle so that businesses can get what they need seamlessly, which is their business value.

Enable producibility, visibility, managed access control, and the computing resources to test, train, and deploy AI algorithms. Click to explore about our, MLOps Platform - Productionizing ML Models

What is the Architecture of MLOps?

The MLOps (Machine Learning Operations) architecture is a set of practices and procedures for managing the machine learning lifecycle, from data preparation to model deployment and maintenance. It aims to provide a standard and flexible way of working on learning models and to ensure that they can be easily maintained and updated over time. The MLOps architecture has several key features, including:

Data Management

This stage focuses on collecting, organizing, and maintaining data for machine learning models. It may include setting up an automated data transfer system to streamline the data flow from source to model

Model Development

In this phase, machine learning models are designed using various algorithms and techniques. Tasks include selecting optimal hyperparameters, validating the model, and evaluating its performance

Model Deployment

This stage involves integrating models into production environments like web or mobile applications. Often, an API is created to enable other applications to interact with the model

Model Monitoring

Regular monitoring ensures the model continues to perform as expected. An alert system can notify developers when the model’s performance deviates from set expectations

An effective MLOps operation must be supported by various tools and technologies, such as management models, automated measurement systems, and continuous integration/continuous delivery(CI/CD) pipelines. By providing a structured and structured approach to managing machine learning models, the MLOps architecture can help organizations realize the full potential of machine learning and stay ahead of the world's rapid evolution in AI and machine learning.

How to do Operationzation of ML Models?

It is a collection of practices for communication and collaboration between operations professionals and data scientists. Applying these practices simplifies the management process, increases the quality, and automates the deployment of Deep Learning and Machine Learning models in large-scale production environments. It works with data developers, machine learning engineers, and DevOps to turn the algorithm into production systems once it's ready. It aims to improve production models' automation and quality while considering business and regulatory requirements. The critical phases of MLOps are:

• Data gathering
• Data Analysis
• Data transformation/preparation
• Model training and development
• Model validation
• Model serving
• Model monitoring
• Model re-training

Automate ML Workflow and enable the sequence data to be transformed and correlated together in a model. Click to explore about our, Streamlining ML Projects with MLOPs and Azure ML

Why do we need ML Model Management?

Before, organizations dealt with fewer data and few models. But now, the tables are turning. Organizations are making decision automation in an extensive range of applications, which generates many challenges when deploying ML-based systems.

To understand MLOps, it is essential to understand the ML systems lifecycle, which involves different teams of a data-driven organization.

• Product team or Business Development - Team that defines business objectives with KPIs
• Data Engineering  - Data Preparation
• Data Science - Defining ML solutions and developing models.
• DevOps or IT- Complete setup deployment and monitoring alongside scientists.

What are the different types of MLOps frameworks?

Many MLOps frameworks are on the market, from open source to enterprise solutions. Each framework has advantages and disadvantages depending on an organization's needs and requirements. Some of the most popular MLOps are:  

Kubeflow

Kubeflow is an open-source MLOps framework based on Kubernetes. It provides tools and best practices for building and deploying machine learning models at scale, including management, testing, deployment, and visualization support.  

• Pros: Open source, extensible, customizable, and community-driven.  
• Cons: The learning curve requires Kubernetes expertise.  

MLflow

MLflow is an open-source MLOps framework that provides an integrated platform for managing the machine learning lifecycle, from data preparation to model deployment. It includes version control, technical testing, deployment and maintenance support, and integration with popular machine learning libraries such as TensorFlow and PyTorch.  

• Pros: Open source, easy to use, integrates with popular machine learning libraries.
• Cons: limited scalability, fewer customization options.  

AWS SageMaker

AWS SageMaker is a commercial MLOps framework provided by Amazon Web Services (AWS). It provides tools and services for building, training, and implementing machine learning models, including support for management, automated evaluation, deployment, and visualization.  

• Pros: Scalable, easy to use, integrates with other AWS services.  
• Cons: Expensive, limited customization options.  

Databricks

This commercial MLOps project provides an integrated platform for building and deploying machine learning models based on Apache Spark. It includes version control, technical testing, deployment and maintenance support, and integration with popular machine learning libraries such as TensorFlow and PyTorch.  

• Pros: Extensible, easy to use, and integrates with popular machine learning libraries.  
• Cons: Expensive, limited customization options.

As a result, organizations should weigh the pros and cons of different MLOps systems before choosing the one that best suits their needs and requirements. Open-source systems like Kubeflow and MLflow offer greater choice and community support, while commercial solutions like AWS SageMaker and Databricks offer greater scalability and integration with other services. 

What are the best practices for MLOps?

• Shift to Customer-Centricity—Today's end customer does not want to know about the brand, product, selection, or model. Still, their goal is to achieve their goals by working on real data business challenges.

• Automation – Automates data pipelines to ensure continuous, consistent, and efficient business value delivery to avoid rewriting custom prediction code.

• Manage Infrastructure Resources and scalability—Applications should be deployed so that all resources, infrastructure, and platform-level services are appropriately utilized.

• Monitoring - Track and visualize all models' progress across the organization in one central location and implement automatic data validation policies.

What are the Challenges of MLOps?

Managing systems at a large scale is not an easy task, so here are the following significant challenges that teams have to face:

AWS charges each SageMaker customer for the computation, storage, and data processing tools used to build, train, perform and log machine learning models and predictions.Know more about Amazon SageMaker

MLOps Services are essentials for Enterprises

It as a service means that MLops is a set of practices that enables the maintenance and deployment of ML systems that are reliably functional in production. It combines Data Engineering, DevOps, and ML. It helps to normalize the processes involved across the lifecycle of ML systems. Its services include:

Design algorithms

Design patterns are regularized best practices to solve problems when designing software systems. Five patterns (workflow pipelines, cascade, feature store, multimodel input) help add resilience, reproducibility, and flexibility to ML in production. Designing infrastructure for ML will have to give ML engineers, data engineers, and data scientists easy ways to implement design patterns.

The design includes requirements engineering, ML use-case prioritization, and data availability checks.

Model Development

Model development includes Data engineering, ML model engineering, and Model Testing and validation. Anyone wanting to learn about MLOps must first understand the model development process, a significant element of the ML project's life cycle. Depending on the conditions, the process can range from simple to complex.

It plays an essential role for Data engineers as they often blaze the trail to productionalizing ML for the organization. This often leaves data engineers with a difficult task at hand. Here, it enters a solution that manages and monitors the lifecycle of ML models. With its help, data engineers can validate, update, and test the deployments from a centralized hub no matter which type of ML models they are running.

Model Operations

In it, MLOps include ML pipeline Automation and full CI/CD pipeline automation.

Machine learning Pipeline Automation

There is an understanding that on the model, training/validation needs to be performed continuously on new data and managed in a CI/CD pipeline. The ML pipeline is now evolving.

• Experiments can happen faster, and data scientists can think of hypotheses and rapidly deploy them in production.
• The model can be re-trained and tested with new data based on results from the live model performance.
• All components used to train and build the model are shareable and reusable across multiple pipelines.

Continuous Delivery Pipeline for Machine Learning

Engineers need an automated CI/CD system for machine learning pipelines in production. This helps the data science team rapidly explore hyperparameters, feature engineering, and model architecture ideas. Engineers can implement these ideas to automatically build, deploy and test the new pipeline components to the target environment.

What are the Top MLOps Tools?

Tools are available based on the purpose for which one wishes to use them. So, to decide which tools to use, firstly, one must have a clear and concrete understanding of the task for which they will use that tool. Before choosing any tool, one should carefully consider the benefits and drawbacks of each tool before deciding on one for the project. Furthermore, this must ensure the tools are compatible with the rest of the stack. There are tools available for performing the tasks, such as:

Model Metadata Storage and Management

It provides a central place to display, compare, search, store, organize, review, and access all models and model-related metadata. The tools in this category are experiment tracking tools, model registries, or both. The various tools that one can use for metadata management and storage are-

• Comet
• Neptune AI
• ML flow

Data and Pipeline Versioning

Every team needs the necessary tools to stay updated and aligned with all version updates. Data versioning technologies can aid in creating a data repository, tracking experiments and model lineage, reducing errors, and improving workflows and team cooperation. One can use various tools for this, such as;

• DagsHub
• Pachyderm
• lake FS
• DVC

Hyperparameter Tuning

Finding a set of hyperparameters that produces the best model results on a given dataset is known as hyperparameter optimization or tuning. Hyperparameter optimization tools are included in MLOps platforms that provide end-to-end machine learning lifecycle management. One can use various tools for hyperparameter tuning, such as:

• Ray tune
• Optuna
• HyperOpt
• Scikit-Optimize

The primary role of DevOps is to take continuous feedback of the process at every step.  Click to explore about, Role of ML and AI in DevOps Transformation

Run Orchestration and Workflow Pipelines

A workflow pipeline and orchestration tool will help when the workflow contains many parts (preprocessing, training, and evaluation) that can be done separately. Production machine learning (ML) pipelines are designed to serve ML models to a company's end customers that augment the product and/or user journey. Machine learning orchestration (MLO) aids in the implementation and management of process pipelines from start to finish, influencing not just real users but also the bottom line. The various tools that one can use for running orchestration and workflow pipelines are:

• Kedro
• Apache Airflow
• Polyaxon
• Kubeflow
  

Model Deployment and Serving

The technical task of exposing an ML model to real-world use is known as model deployment. Deployment integrates a machine learning model into a production environment to make data-driven business decisions. It's one of the last steps in the machine learning process, and it's also one of the most time-consuming. The various tools that one can use for model deployment and serving are:

• Seldon
• Cortex
• BentoML
  

Production Model Monitoring

The most crucial part after deploying any model to production is its monitoring, and if done properly, it can save a lot of time and hassle (and money). Model monitoring includes monitoring input data drift, monitoring concept drift, and monitoring hardware metrics. The various tools that one can use for model monitoring after production are:

• Akira AI
• AWS SageMaker Model Monitor

A process that enables the developers to write code and estimate the application's intended behaviour. Download to explore Machine Learning

Future of MLops

The future of MLOps, particularly with MLOps for TinyML, is poised to evolve with several groundbreaking developments. Here are some key trends to keep an eye on:

1. AutoML and Auto-Tuning:

   AutoML, which focuses on automating machine learning algorithms, will become more accessible, including for TinyML applications. Auto-tuning, which leverages machine learning to optimize the performance of existing models, will become more prevalent in both cloud and edge environments, including on platforms like Azure MLOps and AWS MLOps.

2. Model Interpretation:

   As machine learning models grow more sophisticated and impact various industries, there will be an increasing demand for model transparency. The need to interpret and explain models will drive innovations in MLOps for TinyML, ensuring that even small-scale models used in IoT devices and edge computing can be understood and trusted.

3. Federated Learning:

   Federated learning, which enables the training of models on data distributed across multiple devices or servers without moving the data to a central location, will become a core part of Azure MLOps and AWS MLOps strategies. This decentralized approach allows organizations to train models while ensuring data privacy and security, particularly in edge and mobile devices using TinyML.

Overall, the future of MLOps will see expanded capabilities in TinyML, enhanced model transparency, and improved privacy measures alongside tighter integration with DevOps processes. For companies leveraging platforms like Azure MLOps and AWS MLOps, staying ahead of these trends and embracing innovation will be crucial to maintaining competitive advantages in machine learning deployments.

Next Steps for MLOps Services

Talk to our experts about implementing MLOps services and tools and how industries and various departments utilize these technologies to streamline operations and enhance decision-making. By leveraging MLOps tools, organizations can automate and optimize IT support and operations, improving efficiency and responsiveness.

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