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MLflow vs TensorFlow: What are the differences?

Key Differences between MLflow and TensorFlow

MLflow and TensorFlow are both popular open-source tools for machine learning (ML) and deep learning. While they both serve similar purposes, there are several key differences between the two:

  1. Architecture and Scope: TensorFlow is a comprehensive framework for building and deploying ML and deep learning models, offering a wide range of functionalities such as neural networks, deep learning algorithms, and distributed training. MLflow, on the other hand, is a platform-agnostic tool primarily focused on managing and tracking ML experiments, enabling easy model versioning, and promoting collaboration across teams.

  2. Ease of Use: TensorFlow provides a high-level API (such as Keras) that simplifies the process of building and training models, making it beginner-friendly. MLflow, although it does not offer its own ML library, can be integrated with various popular libraries like TensorFlow, PyTorch, and scikit-learn, allowing users to leverage their preferred ML libraries while still benefiting from MLflow's experiment tracking and management capabilities.

  3. Model Deployment: TensorFlow is well-known for its robust infrastructure for deploying ML models in production, offering tools like TensorFlow Serving and TensorFlow Lite for deploying models at scale on different platforms. In contrast, MLflow is primarily focused on managing the ML lifecycle and tracking experiments, rather than providing specific deployment tools, although it can still be integrated with deployment frameworks like Seldon and Kubeflow for managing models in production.

  4. Community Support: TensorFlow has a vast and active community, with extensive resources, tutorials, and pre-trained models available. It is also actively supported and maintained by Google. MLflow, while gaining popularity, has a relatively smaller community compared to TensorFlow. However, MLflow benefits from being open-source and has the support of Databricks, the company behind its development.

  5. Model Compatibility: TensorFlow has its own serialization format (SavedModel) that allows for efficient storage and loading of models. This format includes model weights, architecture, and the computational graph. MLflow, on the other hand, focuses on model packaging and interoperability, allowing users to package models in different formats (e.g., a Docker image or a Python function) and use them across different platforms without being tied to a specific serialization format.

  6. Integration with Other Tools: TensorFlow integrates well with various popular tools and libraries in the ML ecosystem, such as TensorFlow Extended (TFX) for building end-to-end ML pipelines, TensorFlow Probability for probabilistic modeling, and TensorFlow.js for running models directly in the web browser. MLflow, although less comprehensive in terms of its own ecosystem, can be integrated with a wide range of ML libraries and frameworks, enabling users to utilize their preferred tools while still leveraging the benefits of MLflow's experiment tracking and management features.

In summary, TensorFlow is a comprehensive ML and deep learning framework with a focus on model building, training, and deployment. MLflow, on the other hand, is a platform-agnostic tool aimed at managing the ML lifecycle and promoting collaboration. While TensorFlow provides a more all-inclusive solution, MLflow excels in experiment tracking and model packaging, offering flexibility in terms of model compatibility and integration with other tools.

Decisions about MLflow and TensorFlow

Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.

I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.

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Xi Huang
Developer at University of Toronto · | 8 upvotes · 92K views

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

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Pros of MLflow
Pros of TensorFlow
  • 5
    Code First
  • 4
    Simplified Logging
  • 32
    High Performance
  • 19
    Connect Research and Production
  • 16
    Deep Flexibility
  • 12
    Auto-Differentiation
  • 11
    True Portability
  • 6
    Easy to use
  • 5
    High level abstraction
  • 5
    Powerful

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Cons of MLflow
Cons of TensorFlow
    Be the first to leave a con
    • 9
      Hard
    • 6
      Hard to debug
    • 2
      Documentation not very helpful

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    What is MLflow?

    MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

    What is TensorFlow?

    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

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    What companies use MLflow?
    What companies use TensorFlow?
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    What tools integrate with MLflow?
    What tools integrate with TensorFlow?

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    What are some alternatives to MLflow and TensorFlow?
    Kubeflow
    The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
    Airflow
    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
    DVC
    It is an open-source Version Control System for data science and machine learning projects. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.
    Seldon
    Seldon is an Open Predictive Platform that currently allows recommendations to be generated based on structured historical data. It has a variety of algorithms to produce these recommendations and can report a variety of statistics.
    Metaflow
    It is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. It was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.
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