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:
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.)
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.
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 is at the core of any machine learning framework. All leading ML frameworks provide two ways of model development:
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.
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:
AWS Sagemaker with its Data Labeling and Augmented AI Service, comes in as the winner in this aspect.
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?
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.
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.
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
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.
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:
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!
Special thanks to Mandar Kale for his contributions to this post!