Software Alternatives, Accelerators & Startups

Metaflow VS Salesforce Platform

Compare Metaflow VS Salesforce Platform and see what are their differences

Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Metaflow logo Metaflow

Framework for real-life data science; build, improve, and operate end-to-end workflows.

Salesforce Platform logo Salesforce Platform

Salesforce Platform is a comprehensive PaaS solution that paves the way for the developers to test, build, and mitigate the issues in the cloud application before the final deployment.
  • Metaflow Landing page
    Landing page //
    2023-03-03
  • Salesforce Platform Landing page
    Landing page //
    2023-06-05

Metaflow features and specs

  • Ease of Use
    Metaflow is designed with a strong focus on user experience, providing users with a simple and user-friendly interface for building and managing workflows. Its Pythonic API makes it easy for data scientists to work with complex data workflows without needing to learn a lot of new concepts.
  • Scalability
    Metaflow supports scalable data workflows, allowing users to run their workflows seamlessly from a laptop to the cloud. It integrates well with AWS, enabling users to utilize Amazon's scalable infrastructure for processing large datasets.
  • Versioning
    Metaflow provides built-in support for data and model versioning, making it easier for teams to track changes and reproduce results. This feature is crucial for maintaining consistency and reliability in machine learning projects.
  • Integration with Popular Tools
    Metaflow integrates well with popular data science and machine learning tools, including Jupyter notebooks and AWS services, enhancing its usability within existing data ecosystems.
  • Error Handling and Monitoring
    Metaflow offers robust error handling and monitoring capabilities, allowing users to track the execution of workflows, identify errors, and debug issues efficiently.

Possible disadvantages of Metaflow

  • AWS Dependency
    While Metaflow supports other infrastructures, it is tightly integrated with AWS. Users who do not use AWS may find it less convenient compared to other tools that are more agnostic in their cloud support.
  • Limited Support for Non-Python Environments
    Metaflow primarily supports Python, which might be a limitation for teams or projects that rely heavily on other programming languages for their workflows.
  • Learning Curve for Advanced Features
    Although Metaflow is designed to be user-friendly, utilizing its advanced features and realizing its full potential can have a steep learning curve, especially for users without prior experience with workflow management systems.
  • Community and Ecosystem Size
    Compared to some of its competitors, Metaflow has a smaller community and ecosystem, which might limit the availability of third-party resources, plugins, and community support.
  • Enterprise Features
    Some advanced enterprise features, while robust, may not be as developed or extensive compared to other dedicated data processing and workflow management platforms.

Salesforce Platform features and specs

  • Customization
    Salesforce Platform offers extensive customization options that allow businesses to tailor the platform to suit their specific needs. From custom objects and fields to custom workflows and processes, users have a high level of control over their environment.
  • Integration
    The platform supports integration with a wide range of third-party applications and services through APIs. This flexibility ensures that businesses can create a seamless workflow across different software systems.
  • Scalability
    Salesforce Platform is highly scalable, making it suitable for businesses of all sizes. As a cloud-based solution, it can easily handle growth in terms of users, data volume, and functionality without significant downtime or degradation in performance.
  • Mobile Accessibility
    With Salesforce Mobile App, users have access to their data and applications from anywhere, enhancing productivity and ensuring that critical tasks can be completed while on the go.
  • Security
    Salesforce Platform offers robust security features, including data encryption, regular security updates, and compliance with various industry standards and regulations, providing peace of mind for businesses concerned about data protection.
  • Community and Support
    Salesforce has a vast community of users, developers, and experts, along with extensive documentation and support resources. This community can be invaluable for troubleshooting, best practices, and ongoing learning.

Possible disadvantages of Salesforce Platform

  • Cost
    Salesforce Platform can be expensive, particularly for small and medium-sized businesses. The costs can quickly add up with additional features, customizations, and third-party integrations.
  • Complexity
    While the customization options are a significant benefit, they can also add complexity, especially for users without technical expertise. This can lead to a steep learning curve and may require additional training or hiring specialized personnel.
  • Performance
    While generally reliable, the platform can experience performance issues, particularly during peak usage times or with complex customizations. This can potentially affect the efficiency and response times for users.
  • Dependency on Internet
    As a cloud-based solution, Salesforce Platform requires a stable internet connection to be fully functional. This dependency can be a drawback in areas with unreliable internet service.
  • Customization Limits
    Despite its flexibility, there are still limits to what can be customized within Salesforce. In some cases, achieving certain functionalities may require complex workarounds or may not be possible at all within the provided framework.
  • Data Migration
    Migrating data to and from Salesforce can be challenging, particularly for large datasets or complex data structures. This process often requires careful planning and execution to avoid data loss or integrity issues.

Metaflow videos

useR! 2020: End-to-end machine learning with Metaflow (S. Goyal, B. Galvin, J. Ge), tutorial

More videos:

  • Review - Screencast: Metaflow Sandbox Example

Salesforce Platform videos

Salesforce Platform Overview (1)

Category Popularity

0-100% (relative to Metaflow and Salesforce Platform)
Workflow Automation
100 100%
0% 0
Cloud Computing
0 0%
100% 100
DevOps Tools
100 100%
0% 0
Cloud Hosting
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Metaflow and Salesforce Platform

Metaflow Reviews

Comparison of Python pipeline packages: Airflow, Luigi, Gokart, Metaflow, Kedro, PipelineX
Metaflow enables you to define your pipeline as a child class of FlowSpec that includes class methods with step decorators in Python code.
Source: medium.com

Salesforce Platform Reviews

3 easy app makers you can start on today
Salesforce Platform: If you use the popular customer relationship management system, Salesforce’s low-code tools allow you to build custom apps that can include AI and connect with the company’s various cloud services.

Social recommendations and mentions

Based on our record, Metaflow seems to be more popular. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Metaflow mentions (14)

  • 20 Open Source Tools I Recommend to Build, Share, and Run AI Projects
    Metaflow is an open source framework developed at Netflix for building and managing ML, AI, and data science projects. This tool addresses the issue of deploying large data science applications in production by allowing developers to build workflows using their Python API, explore with notebooks, test, and quickly scale out to the cloud. ML experiments and workflows can also be tracked and stored on the platform. - Source: dev.to / 7 months ago
  • Recapping the AI, Machine Learning and Computer Meetup — August 15, 2024
    As a data scientist/ML practitioner, how would you feel if you can independently iterate on your data science projects without ever worrying about operational overheads like deployment or containerization? Let’s find out by walking you through a sample project that helps you do so! We’ll combine Python, AWS, Metaflow and BentoML into a template/scaffolding project with sample code to train, serve, and deploy ML... - Source: dev.to / 10 months ago
  • What are some open-source ML pipeline managers that are easy to use?
    I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home. Source: about 2 years ago
  • Needs advice for choosing tools for my team. We use AWS.
    1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling. Source: about 2 years ago
  • Selfhosted chatGPT with local contente
    Even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf. Source: over 2 years ago
View more

Salesforce Platform mentions (0)

We have not tracked any mentions of Salesforce Platform yet. Tracking of Salesforce Platform recommendations started around Sep 2021.

What are some alternatives?

When comparing Metaflow and Salesforce Platform, you can also consider the following products

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

Google App Engine - A powerful platform to build web and mobile apps that scale automatically.

Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.

Dokku - Docker powered mini-Heroku in around 100 lines of Bash

Azkaban - Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs.

Google Cloud Functions - A serverless platform for building event-based microservices.