Enterprise applications trending to adopt Machine Learning as one of their strategic implementation and performing machine learning based deep analytics across multiple problem statements is becoming a common trend. There are variety of machine learning solutions / packages / platform that exist in market. One of the main challenges that the teams initially trying to resolve is to choose the correct platform / package for their solution.
Based on my experience with different machine learning solutions I thought to write this blog to list out the points (features in machine learning term) to consider while choosing a specific ML platform and list pros and cons of each of the solutions in market.
Let’s look at feature set that can be weighted before deciding a ML solution
| High Level Feature | Feature Set | Comments |
| Data Storage | High Storage Volume Need | Ability to store huge volume of data to serve growing storage needs
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| High Availability | High availability of data on partial failures
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| Data Exploration | Visualizing summary tables and patterns in input data | Ability to find patterns in input data, This will be helpful to understand and define features
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| Data Preparation / Cleansing | Feature Extraction | Manipulating the raw data to extract features needed for algorithm execution. This could be time consuming task when we deal with huge volume of data..
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| Distributed Execution | Ability to perform the data manipulation in a distributed way , this is required when you have huge volume of data and need to reduce the time to complete. Many ML solutions are trying to bring this capability.
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| Development | Supported Languages | Scripting languages support for development
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| Ease of development | How easy is the platform to develop scripts and execution?
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| General Purpose Programming | Other than model creation , prediction , will the language support the general purpose programming needs for the application ?
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| Model | Algorithms Supported | Availability of different algorithm implementation packages on the platform. This is a critical requirement as we cannot switch to different products for different solutions.
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| Distributed Execution in Model Creation | Model creation is a time consuming operation and needs lot of experimentation and hence the ability to create the model in a distributed way saves lot of time and will help to do experiments
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| Deep Learning Support | Support for Deep learning algorithms
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| GPU Support | GPU execution support will help to reduce execution by multi folds
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| Flexibility to Tune Model | How flexible are the API exposing the mode parameters that can be tuned
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| Model Examination Flexibility | Ability to examine the model helps to deep dive into what is happening behind the model
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| Ease in switching between Models | Switch between different models for suitable choice
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| Data Visualization | Visualize and Plot the results | Availability of different charts to visualize the output
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| Productionizing | Ease of deploying the model in production use case on web environment | Run in large scale deployment
Ability to deploy the model in web Scale to huge volume of data handling
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| Support | Official / Community Support with Active development | Commercial support availability for the platform / solution
Active community development
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Now let’s look at the different machine learning solutions / platforms available in the market and where they stand with respect address the feature requirements.
| Solution | Language | Pros | Cons |
| RStudio | R | Thousands of packages for different solutions
Easy to develop Deep Model examination and tuning |
Time consuming execution due to single threaded nature.
Not easy productionizing for web environment
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| Spark ML | Scala, Python, R | Scalable Machine learning library
Distributed execution utilizing platform like Yarn , Mesos etc. Faster execution Supports multiple languages like Scala, Python, R
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New to market
Does not have exhaust list of algorithm implementation Knowledge of Hadoop eco system |
| H20 | Scala, Python, R | Easy integration to platforms like Spark through Sparkling water , R
Connect to data from hdfs, S3, NOSQL db etc…
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Compatibility between H20 and Spark with Sparkling water
No support for scala in H20 Notebooks
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| Tensorflow | Python, C++ | Flexible architecture that can deployed to run CPU / GPU
Effective utilization of underlying hardware. Stronger in Deep Learning implementations
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Learning Curve is comparatively more
Generally meant for Neural network based implementation |
| Matlab | Matlab | Advanced tool box with wide variety of algorithm implementations
Algorithms can be deployed as Java or dot net packages for deployment
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Learning of Matlab language
Expensive product
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| Anaconda | Python | Good collection of algorithm implementations
Easy to learn and develop Integration with PYSPARK Good for local usage and trials
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Enterprise license cost
Advanced features is licensed and expensive
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| Turi | Python | SFrame concept aims for distributed machine learning executions
Can read and process from HDFS, S3 etc. Simplified machine learning executions
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Commercial licensed product
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| IBM Watson | PaaS for ML | PaaS platform for Machine Learning on IBM Blue Mix
Easy integration with social, cloud End to end solution development with limited knowledge Easy to deploy
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Limited control in model creation & tuning
Limited control over underlying infrastructure
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| Azure ML | PaaS for ML | PaaS platform for Machine Learning on Microsoft Azure
Workflow based ML solution on Azure Easy to develop ML solutions on Azure cloud
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Limited control in model creation & tuning
Limited control over underlying infrastructure |
| AWS ML | SaaS for ML | PaaS platform for Machine Learning on AWS
Easy to develop ML solutions on AWS cloud
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Limited control in model creation & tuning
Limited control over underlying infrastructure |
To summarize
Machine learning packaged solutions like RStudio, H20, Anaconda, Turi are trying to improve in the space of connecting to distributed storage platform and trying to add capabilities for distributed multi thread / core /node execution to reduce time for execution on data preparation, feature extraction and model creation.
Machine learning PaaS solutions like IBM Watson, Azure ML, AWS ML having benefits of cloud background tries to abstract the overhead of packaging and aims for easy deployment and scalability. But these solutions limits the capabilities on the level of fine tuning the models and algorithms exposed for execution but a common man without knowledge of algorithms should able to execute.
With respect to cost and licensing most of the packaged solutions are free to run on local system with limited compute and storage capabilities , enterprise usage or when the distributed version of these solution needs comes with cost. ML solutions on cloud works with pay as use cloud pricing and service model.
Reference :
http://www.dataschool.io/python-or-r-for-data-science/
https://www.datacamp.com/community/tutorials/r-or-python-for-data-analysis#gs.CfYvf0A
https://timchen1.gitbooks.io/graphlab/content/deployment/pipeline-dml.html
Zephyr 