MLOps Blog

Best MLOps Platforms to Manage Machine Learning Lifecycle

9 min

Harshil Patel

9th February, 2023

Machine Learning Tools

The machine learning lifecycle is the process of developing machine learning projects in an efficient manner. Building and training a model is a difficult, long process, but it’s just one step of your whole task. There’s a long process behind the machine learning lifecycle: collecting data, preparing data, analysing, training, and testing the model.

Organizations have to manage data, code, model environments, and the machine learning models themselves. This requires a process, where they can deploy their models, monitor them, and retrain them. Most organizations have multiple models in production, and things can get complex even with one model.

Typical ML lifecycle

Gathering Data: First step of the ML lifecycle, the goal here is to collect data from various sources.
Data preparation: After collecting all the data, you need to clean and transform the data for processing and analysis. It’s an important process of reformatting data and making corrections.
Analyse Data: Now that data is ready, it can be used to build models, select analytical techniques, and more. Once done, the output is reviewed to select ML techniques.
Train the model: Various machine learning algorithms are used to train the dataset, it’s required to understand the flow, patterns and rules.
Test the model: Once the training is complete, you can test your data and check the accuracy of the model, and how it performs.
Deployment: Now that all the things are working fine, it’s time to deploy the machine learning model to the real world. You can monitor how your model is performing, and see how to improve the workflow.

See detailed explanation about Machine Learning lifecycle.

Machine learning lifecycle management platforms

Managing machine learning models in production is a difficult task, so to optimize this process, we will discuss a few best and most used machine learning lifecycle management platforms. These range from Small-scale to Enterprise-level cloud and open source ML platforms, which will help you improve your ML workflow from collecting data to deploying applications to the real world.

1. Amazon SageMaker

Amazon SageMaker is an ML platform which helps you build, train, manage, and deploy machine learning models in a production-ready ML environment. SageMaker accelerates your experiments with purpose-built tools, including labeling, data preparation, training, tuning, hosting monitoring, and much more.

Amazon SageMaker offers around 17 built- in services related to ML, and they’re likely to add more in the coming years. Make sure you’re familiar with the basics of AWS, because you never know how much it’s going to cost you per hour to allocate those servers.

Features:

Amazon Sagemaker has many features that make building, training, monitoring and deployment of ML models easy for you.

Sagemaker comes with many ML Algorithms for training your dataset (big datasets). This helps you improve accuracy, scale and speed of your model.
Sagemaker includes both supervised unsupervised ML algorithms, such as linear regression, XGBoost, Clustering and customer segmentation.
The end-to-end ML platform speeds up the process of modeling, labeling, and deployment. AutoML features will automatically build, train and tune the best ML Model based on your data.
SageMaker gives you the option to integrate APIs and SDKs, making it easy for setup, and you can use machine learning functions anywhere.
It has over 150 pre-build solutions, which you can deploy quickly. This helps you get started with Sagemaker.

Check how Amazon SageMaker compares to Neptune and how you can integrate Neptune into Amazon Sagemeker pipelines.

Pricing:

SageMaker is not free. If you’re a new user, you might get the first two months free. With AWS, you only pay for what you use. Building, training, and deploying your ML models on SageMaker, you’ll be billed by the second. There’s no extra fee or any additional charges. Pricing is broken into ML storage, instances, and data processing.

See Build, Train and Deploy ML model (ASM)

2. Azure Machine Learning

Azure ML is a cloud-based platform which can be used to train, deploy, automate, manage, and monitor all your machine learning experiments. Just like SageMaker, it supports both supervised and unsupervised learning.

Features:

Azure has features for creating and managing your ML models:

Azure ML platform supports both Python, R, Jupyter Lab and R studios with automated machine learning.
The drag-and-drop feature provides a code-free machine learning environment which helps data scientists to collaborate easily.
You get the option to train your model on your local machine or in the Azure machine learning cloud workspace.
Azure machine learning supports many open source tools including Tensorflow, Scikit-learn, ONNX, Pytorch. It has its own open source platform for MLops (Microsoft MLOps).
Some key features include collaborative notebooks, AutoML, data labeling, MLOps, Hybrid and multi-cloud support.
Robust MLOps capabilities enable creation and deployments. Easy to manage and monitor your machine learning experiments.

Pricing:

Azure Machine learning provides a 12-month free service and few credits to explore Azure. Same as other platforms, Azure has a pay-as-you-go pricing model. After the free credits/service is over, you pay for what you use.

Azure – Getting Started

3. Google Cloud AI Platform

Google Cloud is an end-to-end fully managed platform for machine learning and data science. It has features which help you manage service faster and seamlessly. Their ML workflow makes things easy for developers, scientists, and data engineers. The platform has many functions which support machine learning lifecycle management.

Features:

Google Cloud AI Platform includes a number of resources which help you to perform your machine learning experiments efficiently.

Cloud storage and bigquery helps you prepare and store your datasets. Then you can use a built-in feature to label your data.
You can perform your task without writing any code by using the Auto ML feature with an easy-to-use UI. You can use Google Colab where you can run your notebook for free.
Google Cloud Platform supports many open source frameworks like KubeFlow, Google Colab notebooks, TensorFlow, VM images, trained models, and technical guides.
Deployment can be done with Auto ML features, and it can perform real-time actions on your model.
Manage and monitor your model and end-to-end workflow with pipelines. You can validate your model with AI explanation and What-if-tool, which helps you know your model outputs, its behavior, and ways to improve your model and data.

Pricing:

Google AI cloud platform offers you flexible pricing options depending on your project and budget. Google charges differently for every feature. You might see few features that are totally free, and few charged right from the second you start using them. You’ll get a 300$ free credit when you start using GCP. When the free trial is over, you will be charged monthly based on what tools you have used.

GCP – Getting Started

3. Metaflow

Metaflow is a Python-based library that helps data scientists and data engineers develop and manage real-life projects. It’s a workspace system specialized for ML lifecycle management. Metaflow was first developed at Netflix to increase the productivity of scientists.

Features:

Metaflow was open-sourced by Netflix and AWS in 2019, it can integrate with SageMaker, Python, and deep learning base libraries.

Metaflow provides a unified API to stack, which is required to execute from prototype to production-based data science projects.
Data is accessed from a data warehouse, which can be your local file, or from a database.
Metaflow has a graphical user interface, it helps you design your work environment as a directed acyclic graph (D-A-G).
After deployment in production you track all your experiments, versions and data.

Metaflow – Getting Started

5. Paperspace

Gradient by Paperspace is a machine learning platform which can be used from exploration to production. It helps you build, track and collaborate on ML models. It has a cloud-hosted design for managing all your machine learning experiments. The majority of the workflow was built around NVIDIA GRID, so you can expect a powerful and faster performance.

Features:

Paperspace Gradient helps you explore data, train neural networks, and deploy production-based ML Pipelines.

Paperspace Gradient supports almost all the frameworks and libraries you might be using or planning to use.
Single platform to train, track and monitor all your experiments and resources.
You get the option to integrate your machine learning project with github repo via its GradientCI github feature, supported by jupyter notebooks.
You will get free powerful GPUs which you can launch in one-click.
Develop ML pipelines with modern deterministic processes. Manage versioning, tagging and lifecycle seamlessly.
You can easily transform your existing experiments into a deep learning platform.
Get more GPU options, they have NVIDIA M4000 which is a cost effective card while NVIDIA P5000 helps you optimize heavy and high-end machine learning workflow. They’re planning to add AMD to optimize the machine learning workflow.

Pricing:

Paperspace Gradient has many plans depending on your usage. It’s free if you’re a student or a beginner with limited usage and costs on paid instances. The paid plans start from 8$ to 159$ per month. You can contact their sales team to personalize your plan.

Paperspace Gradient – Getting Started

6. MLflow

MLflow is an open-source platform for managing the machine learning lifecycle – experiments, deployment and central model registry. It was designed to work with any machine learning library, algorithm and deployment tool.

Features:

MLflow was built with REST APIs, which makes its workspace look simple.

It can work with any machine learning library, language or any existing code. It runs in the same manner in any cloud.
It uses a standard format for packing an ML model that can be used in downstream tools.
MLflow mainly consists of four components, MLflow tracking, MLflow projects, MLflow models and MLflow registry.
MLflow tracking is all about recording and querying your code and data experiments.
MLflow projects is a package of data science which provides code in reusable and reproducible format. It also includes an API and cmd tool for running ML and data science projects.
MLflow models help you deploy different types of machine learning models. Each model is saved as a dir containing arbitrary files.
MLflow registry which helps you store, annotate, explore and manage all your machine learning models in a central repository.

Below is an example MLFlow project which can be defined by a simple YAML file called MLproject:

name: My Project
conda_env: conda.yaml
entry_points:
  main:
    parameters:
      data_file: path
      regularization: {type: float, default: 0.1}
    command: "python train.py -r {regularization} {data_file}"
  validate:
    parameters:
      data_file: path
    command: "python validate.py {data_file}"

MLFlow – Getting Started

Learn more

Check how you can make MLflow projects easy to share and collaborate on

Read the case study of Zoined to learn why they chose Neptune over MLflow.

7. Algorithmia

Algorithmia is an enterprise-based MLOps platform that accelerates your research and delivers models quickly, securely, and cost-effectively. You can deploy, manage, and scale all your ML experiments.

Features:

Algorithmia helps you securely deploy, serve, manage and monitor all your machine learning workloads.

Algorithmia has a different plan for workspace. You can choose Algorithmia enterprise or Algorithmia teams depending on your project.
Algorithmia’s platform helps you build and manage all kinds of machine learning operations and easily ramp up your ML capabilities.
It delivers models 12x faster and in a cost-effective way. Algorithmia will work for you at all stages of your machine learning life cycle.
Algorithmia uses an automated machine learning pipeline for version control, automation, logging, auditing and containerization. You can easily access KPIs, performance metrics and data for monitoring.
Comprehensively document your machine learning models, and query them from your existing experiments. Algorithmia features will show you a clear picture of risk, compliance, cost and performance.
It supports more than 3900 languages/frameworks. Optimize model deployment with MLOps and its flexible tools.
Algorithmia offers advanced features, which makes it easy to work with CI/CD Pipelines and optimize monitoring.
You can deploy your machine learning model on cloud, on your local machine, or any other kind of environment.

Pricing:

Algorithmia has three plans: Team, Enterprise Dedicated, and Enterprise Advanced. The Team plan comes with pay-as-you-go pricing (PRO for $299/month), for an enterprise-level plan you need to contact their sales team.

Algorithmia – Getting Started

8. TensorFlow Extended (TFX)

Tensorflow Extended is a Google-production-scale ML Platform. It provides shared libraries and frameworks to integrate to your machine learning workflow.

Features:

TFX is a platform for developing and managing machine learning workflows in production.

TensorFlow extended lets you orchestrate your machine learning workflow on many platforms like Apache, Beam, KubeFlow, etc..
TFX components provide functions to help you get started developing machine learning processes easily.
It’s a sequence of components that implements a machine learning pipeline, which is designed to help perform high-end tasks, modeling, training, and managing your machine learning experiments.
TFX Pipeline includes ExampleGen, StatisticsGen, SchemaGen, ExampleValidator, Transform, Trainer, Tuner, Evaluator, InfraValidator, Pusher, BulkInferrer.
Automated data generation to describe expectation about data, a feature to view and inspect the schema.
TFX libraries include:
TensorFlow Data Validation to analyze and validate machine learning data. It’s a high-end design to improve the workflow for TFX and Tensorflow.
TensorFlow Transform to preprocess the data with Tensorflow. It helps to normalize input value by mean and SD method.
TensorFlow Model Analysis to evaluate the Tensorflow models. It provides metrics for large amounts of data in a distributed manner. These metrics can be computed in jupyter notebooks.
TensorFlow Metadata provides metadata that’s helpful while training machine learning models with TF. It can be generated manually or automatically during data analysis.
ML Metadata (MLMD) is a library for recording and retrieving metadata for machine learning workflow.

9. Seldon

Seldon is an open-source platform for deploying machine learning models on Kubernetes. It helps data scientists seamlessly manage workflow, audits, ML experiments, deployment and more.

Features:

Seldon creates a seamless pipeline for any enterprise from deployment to governance. It comes with an open-source Python library to inspect and interpret machine learning models.

Seldon has mainly three products: Seldon Deploy, Seldon Code and Seldon Alibi.
It’s built on Kubernetes, and runs on any platform (cloud or local machine)
Seldon supports the majority of top-rated libraries, languages and toolkits.
Any non-kubernetes experts can deploy machine learning models and test their model in an ML workflow.
You can monitor your model performance and easily inspect errors and debug them.
Seldon comes with a machine learning model explainer, which detects any red flags in your workflow.
The enterprise plan of Seldon comes with more features for faster delivery, proper governance and full lifecycle management.

Pricing:

Seldon Core is free to deploy to your Kubernetes cluster. Seldon Enterprise Solutions comes with a 14-day free trial. You can contact their sales team or request a demo to know more about Seldon Solutions.

10. HPE Ezmeral ML Ops

HPE Ezmeral is Hewlett Packard service which offers machine learning operations at enterprise level. From sandbox to model training, deployment, and tracking. It can be performed seamlessly with any machine learning or deep learning framework.

Features:

It provides speed and agility to machine learning workflows for every stage of the ML lifecycle.

You can run HPE Ezmeral on your local machine or on cloud platforms like AWS, GCP, etc..
It provides a container based solution for the machine learning lifecycle. Build,train, deploy and monitor your machine learning models.
Perform high-end training and get secure access to big data.
Seamlessly track model version, registry. Update your ML models when needed. Easily monitor model performance.
You can run A/B testing to validate the model before deploying to a large scale.
You can transform any non-cloud apps without re-architecting. You can build applications and deploy anywhere you want.

Pricing:

HPE Ezmeral is an enterprise-scale platform, so you don’t get any free trial or pay-as-you-go pricing. You need to get personalized quotes for your projects. Basic storage price starts from $0.100/GB, and compute unit price – $18.78

Comparison

We saw many platforms where you can build, train, deploy and manage your machine learning model. Most platforms are very closely related to each other with similar features, but there are some differences. We’ll compare a few top-rated platforms and their best abilities.

Creating an environment

Amazon Sage Maker – SageMaker Studio has a good interface and skips all the complexity. You can prepare your data via jupyter notebooks.

Google Cloud Platform – The notebook cloud setup is easy. You can deploy the solution you want by simply searching on the top. GCP mainly runs on cloud shell features. You can integrate data from google Colab.

Microsoft Azure – Azure lets you import saved data with the drag-and-drop option. You can drag your dataset to the experiment canvas.

Build & train model

Amazon Sage Maker – You can split data into training and testing sets with the help of Python code. This is an advantage of SageMaker which lets you perform tasks automatically. Using the predetermined XGBoost algorithm, you can train the model using gradient optimization by running on a jupyter notebook.

Google Cloud Platform – It doesn’t have pre-built, customised machine learning algorithms, but it provides a platform to run the model using TF. You can train models in any language by going into the datasets page and defining model details. Create custom containers to install the training workflow.

Microsoft Azure – In Azure, if you want to split data you have to select columns in the dataset and split the data modules. Azure lets you select the feature to train your algorithm. You can simply work with the canvas module, drag the ‘train model’ module and connect with your data.

Test, score and deploy the model

Amazon Sage Maker – To deploy the model on the server you need to run a few lines of code in a Jupyter notebook. Make sure you terminate your process after the test to avoid any additional charges.

Google Cloud Platform – GCP doesn’t provide automated hyper-parameter tuning, instead it has Hypertune which lets you optimize machine learning models with accuracy. Model built on GCP is packed into a Python module and will be deployed on Google Cloud ML.

Microsoft Azure – for testing and deployment, you need to drag and connect your train model with your split data and score model. Once done, you can view output in rows and columns. To evaluate how your model performed, you can drag the ‘evaluate model’ function into the canvas and easily connect it.

Which one should you choose?

The majority of ML platforms offer a robust process with GUI-based tools to improve the ML workflow. Different tools might have varying design and workflows.

Some platforms are really easy for beginners. Azure offers a drag-and-connect option, really simple for tasks like accessing, cleaning, scoring and testing your machine learning data.

Few platforms are really very complex when it comes to managing your ML workflow with Python coding and notebooks. SageMaker, GCP, and a few others are made to serve the needs of data scientists and ML developers who are comfortable with Jupyter notebooks.

There are pros and cons to every platform. It’s a personal choice because no matter what platform you choose, your model accuracy will not differ much. Workflows are different, but you can import your algorithm. Pricing is an important topic here, as most of them have a pay-as-you-go option which allows you to pay only for the features you use. Most platforms we discussed have this feature, so no issue with the pricing part.

If you’re a solo Data Scientist, ML engineer or have a small team and want to try out a platform before deploying your models you can go with the platform which provides you a free trial or free credits. It will help you understand how things work and whether you and your team are comfortable using a platform.