The rationale for selecting a particular DSML platform will depend on the audience. If the intended users are experienced data scientists, the primary environment is Python you need a platform that offers a significant amount of customization and flexibility. Experienced data scientists generally prefer to build, test, and tweak models manually. These data scientists will have an affinity for a platform that automatically discovers and generates new features to build accurate models faster and explore broader feature space.

Code-Free or Code-First, what degree of automation will accelerate the data science workflow?

Another important criterion is the selection of a no-code (or low code) versus code-first approach to data science. Traditional DSML platforms (code-first) require data science teams to generate features manually, a very time-consuming process that involves a lot of domain knowledge. Once the features are built, AutoML platforms can accelerate the work by selecting the algorithms and building ML models automatically. As an analytics and data science leader, you need to decide how much of this process you need to automate?

On the other hand, a no-code environment means using visual tools, drag and drop functionality. The BI & analytics team or inexperienced data scientists will prefer an enterprise platform with  AutoMl 2.0 capabilities such as end-to-end data science automation, including data preparation, automated feature engineering, ML, and one-click model deployment. 

Five Significant Features to Evaluate on a DSML Platform

1. Data Ingestion and Preparation:

How much manipulation of data must be performed before it is ready for ingestion by the DSML platform? Can you upload data to the platform without having to write additional SQL code?

2. Feature Engineering Automation:

How much manual work is involved in Feature Engineering? Will the platform support automated feature engineering and can the AI engine automatically explore all available database entity relationships and discover and evaluate features based on available columns and relationships?

3. Machine Learning:

Does the system support automated machine learning, state-of-the-art ML algorithms like scikit-learn, XGBoost, LightGBM, TensorFlow, and PyTorch? Can the users perform an automated hyper-parameter search of ML algorithms?

4. ML Operationalization:

How easy is it to deploy ML models in a production environment? Can you monitor models, discover model drift, and quickly retrain models if production data changes over time?

5. Platform Integration, Ease of Use, and Deployment Flexibility:

Can all steps of the data science process be executed seamlessly within a single platform without the need for moving between systems and applications?

Making the right choice from a crowded field in the DSML platform market can be challenging.  Forrester Research had published a report highlighting nine automation focused Machine Learning Solutions. The report underscored the importance of Feature Engineering and Explainability as critical differentiating factors for leaders in the automated ML space. To learn more about automation-focused Machine Learning Solutions, the Forrester Wave report is a great resource. For guidance on top factors that the data science team should consider, you can check out sticky.io, an e-commerce platform, and learn how they evaluated DSML platforms:
Case Study – sticky.io

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