What Are Machine Learning Frameworks and How to Pick the Best One

There’s no question that artificial intelligence (AI) and machine learning (ML) technologies are already impacting nearly every sector across the globe. Plenty of companies are investing heavily in data science, ML, and AI initiatives to solve their business problems, which may give them an edge over their competitors.

When we look at it from an application perspective, all these initiatives attempt to solve business problems. This, in turn, leads to a wide range of solution domains (from analytics and reporting to ML/deep learning/AI).

In this blog post, we evaluate the top machine learning frameworks and give recommendations based on our experience and findings. More specifically, we’ll be comparing Dataiku, DataRobot, AWS Sagemaker, and Azure Studio.

Jump to our comparisons of the best ML Frameworks

What is a Machine Learning Framework?

There are a variety of innovative tools used in machine learning to unlock various levels of success. A machine learning framework is a set of tools and algorithms that facilitate activities involved in the machine learning life cycle (see Figure 1) such as:

Data engineering
Model development
Hyper parameter tuning
Testing and logging
Monitoring and deployment

What’s the Difference Between ML Frameworks and ML Tools?

As a side effect of the continuous drive towards ML and AI, there are plenty of tools and frameworks that have been developed in this field. To give you a better understanding of the key differences between the two, we summarized them below.

Machine learning tools focus on the productivity of data scientists (i.e. jupyter notebook, R studio, etc.), but may not encompass an end-to-end machine learning life cycle.

Machine learning frameworks provide end-to-end support for the machine learning life cycle. (Data Engineering, Visualization, Machine Learning Development, MLOps, etc.)

What are the Challenges of a Machine Learning Framework?

The following are key challenges for a machine learning framework that fulfills business requirements:

Skill Agnostic: The Machine learning framework needs to enable people with a wide range of skills such as statistics, analytics, visualization, development, data engineering, distributed systems, cloud computing, and containerization.
Technology agnostic: Since the technology landscape is continuously evolving, the machine learning framework needs to be extensible, adaptable, and scalable.
Machine learning life cycle: Though ML development can be viewed as similar to any other application development project; it needs to be far more dynamic to be able to monitor data quality and model drift. It needs to have mechanisms for retraining and redeployment.

There are a variety of ML/AI frameworks available on the market, but only a handful can be used as a one-stop-shop. In the early days, the machine learning capabilities of an organization are limited by the availability of:

Open-source algorithms
Skilled data scientists
Infrastructure to process every growing and evolving datasets (social media, IoT, credit card data, etc.)
Budget

Companies like Dataiku and DataRobot developed frameworks that include vast open-source algorithms and transform them into simple applications that can be used by data engineers or data scientists, however, one limitation that these frameworks have is their cost.

With the advent of Big Data and cloud systems, a few cloud service providers like AWS, Azure, and Google developed machine learning frameworks that can be used as a cost-effective model (pay as you go). In the next section, we compare machine learning frameworks developed by cloud service providers (AWS Sagemaker, Azure Studio) with data science frameworks developed by companies that are very focused on data science applications (Dataiku, DataRobot).

Comparison of Machine Learning Frameworks by Category

For this comparison, we’ll explore each machine learning category in detail while establishing a clear leader for each section.

Usability

Machine learning frameworks need to enable users with different backgrounds such as data scientists, statisticians, mathematicians, programmers, analysts, and business users. Therefore, the effectiveness of an ML framework is directly proportional to the usability features like:

No code tools for data engineering, statistical analysis, visualization, and AutoML.
Interactive notebook environments for developers.
Project management and collaboration
Traceability and transparency

We found that DataRobot, Dataiku, and Azure Studio provide competitive features for non-coders. However, Dataiku supports both coders and non-coders. Moreover, it provides visual end-to-end workflow as well as features for effective collaboration and project management for the machine learning pipeline. Dataiku, then, enhances usability, traceability, and transparency.

Dataiku is the winner for this category.

Extensibility, Adaptability, & Scalability

As the enterprise technology landscape continues to evolve, any machine learning framework needs to be able to utilize the existing technology stack of an enterprise. At the same time, the framework should provide features that help enterprises to extend, advance, and/or change their technology stack with minimum on any of the existing ML development.

All four frameworks provide excellent features for extensibility and scalability—but an enterprise that has more than one cloud provider (due to region-specific constraints or acquisitions and mergers) needs a framework that is cloud-agnostic. Both Dataiku and DataRobot are cloud-agnostic and can adapt well to existing technology stack.

Dataiku and Datarobot are winners from the adaptability aspect.

Data Pre-Processing & Post-Processing

A lot of data engineering takes the form of data pre-processing (data cleaning, imputation, and transformations) and data post-processing (transform model output into business rules). While this can always be done using traditional ETL tools, if these features are available as a one-stop-shop on a data science/ML framework, they are useful as the ML life cycle can be developed and maintained end-to-end on a dedicated framework. DataRobot does an excellent job automating data prep steps; however, AWS Sagemaker, Azure Studio, and Dataiku have an edge for Data Preparation.

Dataiku has a wide range of features for workflow design, analysis, and custom plug-ins — therefore it is the winner in this category.

Model Development

Model development is at the core of any machine learning framework. All leading ML frameworks provide two ways of model development:

Auto ML: Auto ML is all about automating machine learning tasks like feature engineering, hyperparameter tuning, selection of best model, and providing explainable AI and lineage of model experiments. All four ML frameworks are very competitive in Auto ML, but automated machine learning is a core component of DataRobot, who takes the win for AutoML. 
Traditional Model Development: All four frameworks provide competitive features for developing models from scratch; there is no clear winner.

Automated Build and Deployments

In an ML context, continuous integration and continuous deployment (CI/CD) can be a little tricky. Model training can be considered as a build job, but it has to be supported with automatic data quality checks, actual training, and model evaluation. It should also only conditionally deploy the model if performance exceeds a configurable threshold. CI/CD workflow allows the model to be retrained automatically as soon as the performance alarm is raised (especially for real-time scoring).

A good MLOps framework should have features around automating and triggering MLOps pipelines. All four frameworks provide competitive features for automated build and deployments, so there is no clear winner in this category.

Model Monitoring

Monitoring different aspects of a deployed model is essential for successful MLOps. To name a few:

Monitoring input data quality
Monitoring output predictions
Monitoring reliability of service

Since all four frameworks provide competitive model monitoring features, there is no clear winner in this category.

Outsource Labor-Intensive Tasks

One of the distinctive features of a few data science frameworks is the ability to outsource a few of the labor-intensive tasks that are not feasible or not cost-effective otherwise. A few examples:

Labeling data for an image recognition project can be time-consuming and manual labor-intensive. For an enterprise that is looking at developing an image recognition system for their purpose but has limited human resources, this feature to outsource the task of data labeling for some cost can be of great benefit.  
Review of developed model results and evaluation can be another labor-intensive task, a few data science frameworks help outsourcing this task as well.
In case an organization wants to use a machine learning framework like Dataiku or Azure Studio or DataRobot, then there are a few pure-play vendors in this space, such as LabelBox.

AWS Sagemaker with its Data Labeling and Augmented AI Service, comes in as the winner in this aspect.

Cost

Cloud providers have a pay-as-you-go cost model compared to a subscription-based price model. For example, AWS Sagemaker provides a marketplace for the purchase of specific algorithms per need basis.

Subscription-based models may not be cost-effective, especially for small or medium-sized businesses with discrete requirements.

The cloud services of AWS Sagemaker and Azure Studio win this category.

Which ML Framework is Best?

Dataiku

Strength: Usability, adaptability, extensibility (plug-ins), data preparation, end-to-end framework.

Limitations: Deep learning support is weak (requires coding), no augmented AI, outsourced services like data labeling, and cost.

Alternatives: Third-party vendors like Labelbox can be used for data labeling.

Ideal Organization: Dataiku is good for a mid to large size organization that has a large number of use cases, wants to enable users with a wide range of skills, and has a strategic sponsorship for data science initiatives.

DataRobot

Strength: Adaptability, advanced AutoML capabilities.

Limitations: Advanced analytics capabilities, decision modeling, no augmented AI, outsourced services like data labeling, and cost.

Alternatives: Third-party vendors like Labelbox can be used for data labeling.

Ideal Organization: DataRobot is good for a mid to large size organization that is looking more for automation of machine learning models, pipelines, etc., and has a strategic sponsorship for data science Initiatives.

AWS Sagemaker

Strength: Pay-as-you-go cost model, ability to purchase algorithms from the marketplace, a wide range of AI services (Amazon lex, poly, transcribe, etc.), services like augmented AI, and data labeling.

Limitations: It’s not a one-stop-shop, but a conglomerate of AWS Services, difficult to catch up with the competition in terms of features with specific ML providers like Datakiu or DataRobot.

Alternatives: Use end-to-end ML vendors like Dataiku coupled with AWS cloud support.

Ideal Organization: Any organization that doesn’t have a sufficient budget to spend on an ML framework and/or they don’t have a wide range of AI requirements to justify the investment in an end-to-end ML framework

Azure Studio

Strength: Pay-as-you-go cost model, usability, MLOps capabilities such as the registry of packages and models. Strong enterprise data science capabilities.

Limitations: It’s not a one-stop-shop, but a conglomerate of different tools, On-prem or hybrid or multi-cloud is evolving but a limitation. Difficult to catch up with the competition in terms of features with specific ML providers like Datakiu or DataRobot. Not skill agnostic requires expertise in Azure services. Augmented AI capabilities are limited.

Alternatives: Third-party vendors like Labelbox can be used for data labeling. Use end-to-end ML vendors like Dataiku coupled with Azure cloud support.

Ideal Organization: Any organization that doesn’t have a sufficient budget to spend on the ML framework and/or they don’t have a wide range of AI requirements to justify the investment in the end-to-end ML framework.

Conclusion

We compared all four machine learning frameworks and didn’t find any of the frameworks as a clear winner in all aspects.

Each of the four frameworks is good at certain specific aspects. These are the strengths of these four frameworks in the view of industry requirements:

It’s no surprise that cloud providers democratized data science and enabled small companies to use data science tools and cloud infrastructure and services in a pay-as-you-go model. AWS Sagemaker and Azure Studio are clear winners from a cost aspect.
Frameworks like Dataiku and Data Robot are designed as easy-to-use, comprehensive, and end-to-end solutions. This is suitable for mid-size to large enterprises that have a wide range of business problems and users with a wide range of skills.
Azure Studio attempts to provide both an end-to-end solution and a pay-as-you-go price model.
We find Dataiku as a winner in usability, adaptability, and data-preprocessing.
DataRobot comes as a winner in adaptability and AutoML capability.
Sagemaker is the leader in seamlessly outsourcing the time-consuming and expensive task to review thousands of examples and add labels required to train machine learning models.

No framework is universally better than any other, at least not at this time. But this comparison of features and advantages can help you to select a framework that matches your business needs.

Want personalized (and unbiased) advice on your machine learning iniatives? Reach out today to the ML experts at phData!

Comparison Matrix

Special thanks to Mandar Kale for his contributions to this post!