Table of Contents
- Data Science Platforms Primer
- Report Methodology
- Decision Criteria Analysis
- Evaluation Metrics
- Key Criteria: Impact Analysis
- Analyst’s Take
- About Andrew Brust
Data science platforms help enterprises implement data-driven operations by predicting business outcomes through the use of machine learning (ML) and deep learning algorithms. Practitioners using these platforms include data scientists, who have expertise in computer science and statistics, and ML engineers, who focus on the operational aspects of deploying and monitoring models once data scientists have trained and verified them.
The biggest challenges companies face in leveraging data science are the relatively small number of trained data scientists and the historically ad hoc, manual approach involved in the work. For example, data scientists have traditionally conducted data exploration and model training and optimization using their own tools, on their own computers, with relatively little tracking, consistency, or collaboration and reuse of code.
The steps involved in building optimal ML models—for example, feature engineering, testing different hyperparameter values, and building multiple candidate models—are quite time-consuming, especially when done manually. Pressure to produce models quickly can thus short-circuit the optimization work, resulting in less-accurate models.
This is where data science platforms come in. These platforms provide tools to serve the end-to-end data science lifecycle (including data preparation, training, testing, and deploying ML models) so that data scientists can focus on building better models rather than the “plumbing” of ML work.
As massive volumes of data continue to be processed, streamlining the data science workflow by accelerating model development and deployment becomes an imperative of increasing urgency. And while important open-source technologies exist in this arena, commercial data science platforms supply the fit-and-finished end-to-end solutions needed to provide the required efficiency gains.
When evaluating vendor offerings, decision-makers should consider their company needs, goals, budget, and employee skill sets.
This GigaOm Key Criteria report details the criteria and evaluation metrics for selecting an effective data science platform. The companion GigaOm Radar reports identify vendors and products that excel in those criteria and metrics. Together, these reports provide an overview of the category and its underlying technology, identify key data science platform offerings, and help decision-makers evaluate existing platforms to help them decide where to invest.
How to Read this Report
This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding, consider reviewing the following reports:
Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.
GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.
Solution Profile: An in-depth vendor analysis that builds on the framework developed in the Key Criteria and Radar reports to assess a company’s engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.