It has become increasingly difficult for companies to stay competitive as they face multiple challenges, such as data labeling, infrastructure management, model deployment, and performance evaluation. This is where MLOps comes into play, a method for putting a machine learning solution into production and all the steps it takes to get there..

Introduction to MLOps

Machine learning operation (MLOps) facilitates teamwork and accelerates the development of machine learning models and frameworks by monitoring, validating, and managing machine learning models.

This is similar to how DevOps helps software developers develop, test, and deploy software faster, more efficiently, and with fewer bugs. Just as DevOps simplifies the application development lifecycle, MLOps is based on DevOps principles and supports the data processing lifecycle. .

4 Key Benefits of Machine Learning Operations

The key benefits of MLOps include:

• Modern technological innovations that are complemented by sophisticated machine learning lifecycle management.
• The ability to create scalable processes, models, and frameworks.
• Improves the accuracy of models by tracking and controlling the entire lifecycle of a model. It also allows businesses to quickly recognize and correct problems.
• Helps machine learning engineers work more efficiently with DevOps workers.

Top 5 Machine Learning Tools and Platforms

1. Qwak

Qwak is a state-of-the-art solution that streamlines MLOps operations and allows firms to maintain models while connected to their services. The Qwak project was founded in 2021 by four co-founders, including Alon Lev, Yuval Fernbach, Lior Penso, and Ran Romano.

Qwak is an end-to-end machine learning (ML) production platform that aims to reduce the complexity between the research and production stages of machine learning. In addition, Qwak allows machine learning engineers and data scientists to design, implement, and track their models in production with the least technical complexity.

The Qwak platform includes a machine learning (ML) CI/CD framework, version control, model metrics, and a feature repository. The focus is on enabling enterprises to achieve machine learning-driven progress without spending endless resources on modern technologies and networks.

Features:

• Deep Learning
• ML Algorithm Library
• Model Training
• Natural Language Processing
• Predictive Modeling

2. Amazon SageMaker

SageMaker is Amazon’s free platform for deploying and commercializing machine learning models. SageMaker enables all developers and machine learning engineers to build, train, and deploy machine learning frameworks rapidly.

Amazon SageMaker is a fully managed service that covers the entire machine learning workflow, including labeling and data preparation, algorithm selection, model training, deployment optimization, prediction, and execution.

Features:

• Automated bias, model drift, and concept drift detection.
• Automated data loading, data transformation, model building, training, and tuning via Amazon SageMaker Pipelines.
• CI/CD through source and version control, automated testing, and end-to-end automation.
• Workflow logging for training data, platform configurations, model parameters, and learning gradients.
• Security features for policy management and enforcement, infrastructure security, data protection, authorization, authentication, and monitoring.

3. Google Cloud AI Platform

Google Cloud AI Platform supports machine learning lifecycle management with a variety of features, including AI Hub, an overview dashboard, data labeling, notebooks, assignments, and models. If you have the right model, you can use it to make accurate predictions.

Google Colab are free notebooks integrated with the Google Research platform. In addition, the AI ​​Hub includes various public resources, including a portable platform called Kubeflow Pipelines, services, open-source platforms like TensorFlow modules, and technical documentation.

Features: 

• Model Development (Language Support, Pre-Built Algorithms, Model Training)
• Machine/Deep Learning Services (Computer Vision, Natural Language Processing, Natural Language Generation)
• Deployment (Managed Service, Application Scalability)

4. MLflow

MLflow is an open-source platform and tool for managing the entire machine learning (ML) lifecycle, including testing, validation, implementation, and a centralized registry of models.

Currently, MLflow includes four main aspects:

1. The MLflow Monitoring Component is an API and user interface for monitoring ML code variables, code versions, performance metrics, and result files, and then visualizing the output.
2. MLflow Projects is a platform for combining code in a way that makes it scalable and standards-based reproducible.
3. MLflow Models is a defined format for packaging machine learning models that can be used in a number of downstream applications.
4. MLflow Model Registry is a centralized model repository, API, and user interfaces for managing the entire lifecycle of the MLflow framework.

Features:

• Compatible with a wide range of ML libraries, codes, and languages.
• MLflow tracking to record and query experiments.
• MLflow projects to package data science code in a reproducible format.
• Model registry to store, annotate, discover, and manage models in a single location.
• MLflow models to deploy models in varied serving environments.

5. IBM Watson Machine Learning

IBM Watson Machine Learning is a comprehensive IBM Cloud solution that makes it easy for developers and machine learning engineers to work together to integrate predictive capabilities into applications.

In addition, a machine learning service is a set of REST APIs that can be called from any programming language to build applications that make better decisions, solve complex problems, and optimize user output.

It consists of three main functions below:

1. Machine learning integration: Use the management and execution of machine learning platforms (e.g., real-time learning systems). Choose from widely used machine learning frameworks, including TensorFlow model, Keras, sklearn, and gradient boosting (like xgboost sklearn).
2. Numerous interfaces: You can manage your artifacts using the CLI along with Python clients. Integrate AI into your application with the IBM Watson ML REST API.
3. Connected with Watson Studio: Build and develop machine learning models, using the most advanced tools and knowledge in a social context designed specifically for machine learning engineers.

Features:

• Offers an open, extensible AI and data platform that runs on any cloud.
• Helps users to analyze their data automatically.
• Creates a model ready for deployment in any deployment space.
• Deploys and trains multiple versions of models.
• Builds analytical models and neural networks with any data.
• Analyzes and visualizes data seamlessly. 
• Creates neural network design flows using the neural network modeler.

Conclusion

Machine learning platforms and tools have experienced exponential growth, andmany open-source models have emerged. As data and modern technologies continue to grow and succeed, implementing effective machine learning strategies now will help businesses of all kinds manage efficiently and thrive in the future.