Source: Deep Learning on Medium
A data science platform can change the way you work. It’s more than just a tool, it’s a way to wrangle data and turn every member of your team into a high performing unit, capable of pivoting and scaling without missing a beat. The right one is transformative to your work.
Which platform is the right one for you and your team? Let’s take a look at ten of the best options out there.
Why You Need A Platform
If you’re spending a lot of time on basic operations, it might be time to adopt a platform. Signs of this could be that you have no idea how many models you have, or you’re spending a lot of time maintaining your models once deployed.
If you find the lack of collaboration within your team frustrating, that could also be a sign to adopt a platform. Good platforms can create logical workflows and facilitate integrations as well as give you version controls.
If you’re scaling, and you aren’t sure how you’re going to deploy to scale, a platform is a must. Many of the platforms listed below are specifically designed to ease scale and create better models with less laborious maintenance.
Your data science platform should be:
- inclusive of all the tools (and team members) you need
It should foster collaboration and encourage each member of the team to produce high-quality work.
KNIME shines in end-to-end workflows for ML and predictive analytics. It pulls big data from huge repositories including Google and Twitter and is often used as an enterprise solution. You can also move to the cloud through Microsoft Azure and AWS integrations. It’s well rounded, and the vision and roadmap are better than most competitors.
RapidMiner is good for solutions requiring sophistication, but it never loses its ease of use. It’s highly approachable and one of the few platforms to strike such a good balance that it’s beloved by “citizen data scientists” and highly trained data scientists with advanced degrees. It’s excellent for visual workflow and for when you need an ML boost.
Alteryx could allow your organization to build a data science culture without necessarily having a full data scientist at the helm. If your organization has more “citizen data scientists” than ones with advanced degrees, this could be a good option. It allows you to build models within a workflow and offers model management/deployment.
If you’re primarily using your workflow for business insights, TIBCO has a mature platform designed to glean those insights and offer uses in product refinement and prototyping. It’s most useful for business exploration and could be an excellent prospect for creating a culture of innovation and offering complex problem-solving capabilities through analysis.
H2O offers deep machine learning capabilities that expand your reach into AI. It’s a leader in machine-learning unified platforms. Plus, it’s open source and offers a segment for predictive analytics. It’s also snagged the interest of a few enterprises including PayPal, Dun, and Bradstreet. Its open source ML is an industry standard at this point.
Cloudera is a hugely popular platform optimized for the cloud and enterprise data solutions. It has automatic data pipelines and has support for full Hadoop authentication and encryption. It’s excellent for running the types of sensitive data large corporations often have, allowing Spark queries within a safe environment. It can also share models as REST APIs without rewrites.
Anaconda, which uses an interactive notebook concept, is excellent for Python or R enthusiasts. It’s a bit of a niche product because of the programming language, but it’s the only one to offer indemnity for the Python community. It provides enhanced collaboration features and model reproducibility for data discovery and analytics.
Data analysis in the finance sector is exploding, and MATLAB is designed for use within those parameters. It has excellent customer relations and is easy to understand. Even if you aren’t in Fintech, it’s an attractive option for cloud processing, neural systems, and machine learning, allowing you to scrub insane amounts of data (including unconventional data such as IoT data). It’s expensive for the citizen data scientist, but if you’ve got the budget behind your organization, it could be worth it.
Power BI, right? The Azure learning studio is Microsoft’s hold on data analytics and science, offering users the opportunity to build, test, and then model complex predictive analytics solutions. You can preprocess your data, use the studio for thorough documentation, and share any experiments in the gallery. Plus, you get the full support of Microsoft.
This platform comes from the makers of Apache Spark. It offers a blend of data science, data engineering, and business analytics. It provides excellent ecosystem integration, making it a good choice for businesses that already have a host of beloved tools. It shares revision history and integrates with Github. It handles production aspects of analytics (pipelines or monitoring, for example) and it continuously trains for state of the art ML models. It’s all the benefits of potential scale while remaining agile. Plus, you can turn your security over to their managed service.
Choosing A Platform
Your platform needs to conform to the needs of your organization or business. As you do a little research on the best choice for you, keep in mind what will provide real value for your organization and stay away from getting features just because. If the platform doesn’t integrate with your existing environment, it’s only going to be a distraction.
Editor’s note: Ready to learn about all of these frameworks and more? Attend ODSC East 2019 this April 30 to May 3 in Boston and learn from the experts directly!