Top 15 MLOps Tools Need to Use in 2024- StarAgile

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What Is MLOps?

Machine Learning Operations, or MLOps for short, is an emerging field that focuses on the development, deployment, and management of machine learning models in production environments. MLOps involves the integration of data science, software engineering, and DevOps principles to create a seamless workflow for building and deploying machine learning models. In this blog, we will explore 20 of the best MLops tools available today and discuss their features and benefits.

What are MLops Tools?

MLops tools are software solutions that help data scientists and software engineers manage the machine learning development lifecycle, from data preparation to model deployment and monitoring. These tools automate and streamline various aspects of the MLOps workflow, including data processing, model training, model deployment, and model monitoring. Some MLops tools are designed for specific tasks, while others provide end-to-end solutions for managing the entire machine learning development process.

Top 15 MLops Tools:

1. Kubeflow

Kubeflow is an open-source platform that enables organizations to build, deploy, and manage machine learning models at scale using Kubernetes. It offers several machine-learning tools and features that enable data scientists to build highly accurate predictive models. With features like model serving, hyperparameter tuning, and model deployment, Kubeflow simplifies the process of building and deploying machine learning models in production environments

Type: Workflow management and model training platform
Function: Enables data scientists to build, deploy, and manage ML workflows on Kubernetes
Features: Experiment tracking, hyperparameter tuning, model versioning, deployment, and serving
Uses: Efficiently manage and scale ML workflows on Kubernetes, reducing time and costs

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2. TensorBoard

TensorFlow is an open-source platform for building and deploying machine learning models. It provides a set of libraries and tools for building, training and deploying machine learning models.

Type: A visualization tool
Function: Visualize TensorFlow models and experiments
Features: Graph visualization, statistics, profiling, and debugging
Uses: Debugging, performance optimization, and understanding complex models

3. MLflow

MLflow is an open-source platform for managing the machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Type: Workflow management and model versioning platform
Function: Enables data scientists to track and compare experiments, package code, and manage model versions
Features: Experiment tracking, model packaging, model versioning, and deployment
Uses: Efficiently manage and share ML experiments and models across teams

4. Hugging Face

Hugging Face is an open-source platform for natural language processing that provides pre-trained models for a range of tasks such as text classification, sentiment analysis, and machine translation.

Type: Natural language processing (NLP) library
Function: Provides state-of-the-art pre-trained models and tools for NLP tasks
Features: Pre-trained models for various NLP tasks, fine-tuning tools, and pipelines
Uses: Quickly build and deploy NLP models for various tasks, such as text classification and language translation

5. Data Version Control (DVC)

Data Version Control (DVC) is an open-source tool for data and model versioning. DVC helps in keeping track of the changes in data and models throughout the ML pipeline.

Type: Data versioning and pipeline management tool
Function: Enables data scientists to track and version data sets, and build and manage ML pipelines
Features: Data versioning, experiment tracking, and pipeline management
Uses: Efficiently track and version data sets, and build and manage complex ML pipelines

6. KubeFlow Pipelines

Kubeflow Pipelines is an open-source platform used for building, deploying, and managing machine learning workflows in Kubernetes clusters. It enables data scientists and ML engineers to develop and deploy complex workflows in a streamlined and efficient manner.

Type: Workflow management platform
Function: Enables data scientists to build, deploy, and manage ML workflows on Kubernetes
Features: Graphical pipeline editor, experiment tracking, and model versioning
Uses: Efficiently manage and scale complex ML workflows on Kubernetes, reducing time and costs

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7. Seldon

Seldon is an open-source machine learning platform that helps organizations deploy and manage machine learning models at scale. It enables data scientists to build, train, and deploy models using their preferred programming language and framework. With features like model serving, A/B testing, and canary deployments, Seldon simplifies the process of deploying machine learning models in production environments.

Type: Model deployment platform
Function: Enables data scientists to deploy and manage ML models on Kubernetes
Features: Model deployment, scaling, and monitoring
Uses: Deploy and manage ML models at scale on Kubernetes

8. AWS SageMaker

SageMaker is a fully-managed service provided by Amazon Web Services (AWS) that helps in building, training, and deploying machine learning models at scale. It includes built-in algorithms, notebooks, and model hosting services.

Type: ML platform
Function: Provides a fully managed platform for building, training, and deploying ML models on AWS
Features: Pre-built algorithms, experiment tracking, model versioning, and deployment
Uses: Efficiently build, train, and deploy ML models on AWS, reducing time and costs

9. Google Cloud AutoML

Google Cloud AutoML is a suite of machine learning products offered by Google Cloud Platform that allows businesses and developers to create custom machine learning models for their specific needs without requiring extensive knowledge of machine learning. Google Cloud AutoML makes machine learning more accessible to businesses and developers by providing pre-built models and a simple interface for creating custom models. Users

Type: ML platform
Function: Provides a suite of tools for building custom ML models without requiring extensive ML expertise
Features: Custom model training, model deployment, and model evaluation
Uses: Build and deploy custom ML models without extensive ML expertise

10. AlgoTrader

AlgoTrader is a powerful algorithmic trading platform that is designed to support high-frequency trading and automated trading strategies. It is built on top of the popular open-source programming language, Java, and is used by financial institutions, hedge funds, and other trading firms to automate their trading strategies. AlgoTrader has a range of features that make it an attractive option for traders looking to automate their trading strategies.

Type: Commercial
Function: AlgoTrader is a platform that enables organizations to automate their trading strategies using machine learning algorithms. It provides a user-friendly interface that simplifies the process of building, testing, and deploying trading algorithms.
Features:
Strategy Building: Provides tools for building, testing, and deploying trading strategies using machine learning algorithms.
Backtesting: This enables testing the trading algorithms on historical data to evaluate their performance.
Risk Management: Provides tools for managing risk and implementing risk controls.
Uses: AlgoTrader is used by financial organizations to automate their trading strategies, improve the accuracy of their trades, and reduce the risk associated with manual trading.

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11. Big Panda

BigPanda is an AI-powered incident management platform that helps IT Ops, NOC, and DevOps teams detect, investigate, and resolve incidents faster. It uses machine learning algorithms to correlate and prioritize alerts from various monitoring tools and provide actionable insights. With BigPanda, teams can reduce the noise and focus on the incidents that matter most, enabling faster problem resolution and improved uptime.

Type: Commercial
Function: Big Panda is an AI-powered platform that provides real-time incident management and root cause analysis. It enables organizations to identify and resolve incidents quickly and efficiently by automatically correlating alerts from multiple sources.
Features:
Alert Correlation: Correlates alerts from multiple sources to identify the root cause of incidents.
Incident Management: Provides real-time incident management to resolve issues quickly and efficiently.
Root Cause Analysis: Performs root cause analysis to identify the underlying causes of incidents.
Uses: Big Panda is used by IT operations teams to manage and resolve incidents quickly and efficiently, improve service uptime and reduce downtime.

12. Polyaxon:

Polyaxon is an open-source platform that enables organizations to build, train, and deploy machine learning models at scale. It offers several machine-learning tools and features that enable data scientists to build highly accurate predictive models. With features like hyperparameter tuning, model tracking, and model deployment, Polyaxon simplifies the process of building and deploying machine learning models in production environments.

Type: Model management platform
Function: Provides end-to-end machine learning platform with support for versioning, experimentation, and deployment of ML models
Features: Supports different ML frameworks and tools, version control for models, tracking of model performance metrics, visualization of model results, automatic hyperparameter tuning, reproducibility of experiments
Uses: Helps in streamlining the machine learning workflow, makes it easier to collaborate with other team members, provides visibility into different experiments and models

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13. Comet.ml:

Comet.ml is a cloud-based machine-learning platform designed to help teams manage and track their machine-learning experiments. It provides a centralized place for data scientists, developers, and project managers to collaborate and monitor their machine-learning experiments. Some of the key features of Comet.ml include:

Type: Experiment management tool
Function: Helps in tracking and managing different experiments, and provides insights into their performance
Features: Supports different ML frameworks and tools, allows for easy tracking of experiments and their hyperparameters, visualizations for experiment results, collaboration with team members
Uses: Helps in keeping track of different experiments, provides visibility into their performance, facilitates collaboration with team members

14. Pachyderm:

Pachyderm is an open-source machine learning platform that provides a unified data versioning and processing system. It is designed to support large-scale, end-to-end machine learning workflows, from raw data to model deployment. It includes features for version control, data lineage tracking, and distributed computing.

Type: Data science platform
Function: Provides infrastructure for end-to-end data science workflows, including versioning of data and models, and automated deployment
Features: Supports different ML frameworks and tools, version control for data and models, automation of workflows, scalable and portable
Uses: Helps in streamlining the data science workflow, facilitates collaboration between team members, reduces the time taken for model deployment

15. Valohai:

Valohai is a platform for machine learning experimentation and development. It provides a centralized hub for managing experiments, models, and data for machine learning projects. Valohai supports a wide range of machine learning frameworks, including TensorFlow, PyTorch, and Keras, and provides features like version control, automatic hyperparameter optimization, and collaboration tools. It allows users to easily build, train, and deploy machine learning models, and provides infrastructure management features like automatic scaling and resource allocation. Valohai can be integrated with various cloud platforms like Amazon Web Services, Google Cloud Platform, and Microsoft Azure, making it a versatile choice for machine learning projects.

Type: Machine learning platform
Function: Provides infrastructure for machine learning workflow, including version control, experimentation, and deployment of models
Features: Supports different ML frameworks and tools, version control for data and models, automated hyperparameter tuning, collaboration with team members, scalable and portable
Uses: Helps in streamlining the machine learning workflow, reduces the time taken for model deployment, facilitates collaboration between team members, ensures reproducibility of experiments

These are some of the popular MLops tools used in the industry today. Each tool has its own unique set of features and advantages, and the choice of tool will depend on the specific needs of the organization. These MLops tools help data scientists and machine learning engineers to streamline the entire machine learning lifecycle from data preprocessing to model deployment.

Read More: Data Scientist Job Description

In conclusion

MLops tools are an essential part of modern data science workflows. They help automate the machine learning pipeline, from data preparation to model deployment and enable data scientists and engineers to collaborate more efficiently. These tools range from data version control systems, data labelling and annotation tools, automated machine learning platforms, and model serving frameworks. By using these MLops tools, organizations can accelerate their machine learning projects, reduce costs, and improve the overall quality of their machine learning models. If you are interested in pursuing a career in data science or want to upskill in the field, consider enrolling in a Data Science Certification Course or Data Science Training program to gain a deeper understanding of MLops tools and other data science concepts.

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