IBM Data Science Reviews - Page 6

Multi-cloud storage option with no limit to storing data.

Large-scale data management across multiple environments.

Integration of AI helps to enable data formation. Review collected by and hosted on G2.com.

It is a bit complex since its heavily loaded with so many features it will take time for the user to understand and implement solutions.

Enhance third-party integrations smoothly so we can quickly implement integration from external sources. Review collected by and hosted on G2.com.

It is great to easily access data analytics and data modelling. A visually attractive dashboard helps proiperly understand your data all in one place. Review collected by and hosted on G2.com.

It can be clunky/crash when using larger amounts of data. It does lack some of the more intermediate/advanced programming languages in comparison to other similar softwares too. Review collected by and hosted on G2.com.

The storage engine can index, process, store and retrieve data based on natural language processing and machine learning-based ranking. Additionally, it supports custom machine learning models to predict in text-based search. Review collected by and hosted on G2.com.

It doesn't offer connectivity to NoSQL databases in the lower versions. It requires migrating to the latest major version to get all the advanced features related to natural language querying. Review collected by and hosted on G2.com.

Runs anywhere, connects security openly, installs easily in any environment—on a customer’s premises, private cloud, or public cloud

Connects all application data to self-service analytics that are integrated directly into the Cloud Paks platform

Simplifies development of cloud-native applications with open-source standards, common services, and integrated DevOps toolsets

Improves productivity by intelligently automating workflows and decisions (both routine and more complex)

Manages both container and VM deployment, orchestration, upgrades, security, and compliance across hybrid resources in a consistent way and supports all major flavors of Kubernetes, like OpenShift, EKS, AKS, IKS, GKE Review collected by and hosted on G2.com.

There should be policy enforcement and governance with quota restrictions with ease of life cycle management to install and upgrades of core services. Review collected by and hosted on G2.com.

Easy collaboration to work with the organization and flexibility to deploy in all environments.

Handle large quantities of data and train these data models.

Helps in processing automated filling of data with multiple technologies. Review collected by and hosted on G2.com.

Multiple tabs are to be navigated to perform a workflow.

Takes time to integrate and train the model as per requirements.

Less maintenance and high switching costs. Review collected by and hosted on G2.com.

It's a great multilayered platform that enables developers, data engineers, data scientists, business intelligence developers and data analysts to leverage services through their data. There are so many advantages of using this that many businesses have gone live with them and now maintain an integrated platform of automation capabilities. People who code in Kubernetes have heavily used this as it's easy to deploy, analyze, configure and manage the applications. The integrated testing in most cases is successful. The single intuitive dashboard that it provides is excellent. Review collected by and hosted on G2.com.

The templates sometimes get too cumbersome to customize and there are more manual steps when it comes to downtime. The RTO and RPO for when security breaches occur are significant. The disaster recovery mechanisms can be improved as they still need a lot of room and scope of improvement. Moreover, the initial setup and the infrastructure are too time-consuming and hard to understand. I feel like if you exactly know why you need this and its services for data analytics and in what kind, that is when you should think of deploying it. Starting the setup without clarity and analyzing use cases as per the organization's needs can be frustrating and challenging. Review collected by and hosted on G2.com.

A plethora of excellent features. It is beginner friendly and has an easy-to-use interface. The support provided across multiple cloud platforms is what makes it easily adaptable. The support provided for running Cloud Pak for Data on-premises while providing best-in-class security is an amazing add-on! Review collected by and hosted on G2.com.

The downside is that it requires a lot of infrastructure for initial deployment and isn't suitable for small-scale projects. Also, the pricing is too high for startups trying to understand the ecosystem. Review collected by and hosted on G2.com.

It has good features and options for sharing files and sensitive content. I liked the cloud storage, data analysis and text. Review collected by and hosted on G2.com.

Sometimes it can be a bit difficult to navigate its user interface. Review collected by and hosted on G2.com.

Ease of deployment of Machine Learning Models Review collected by and hosted on G2.com.

The documentation is not up to date to latest version Review collected by and hosted on G2.com.

1. Catalog for AI services

2. Searching and indexing

3. Good for hybrid and multi cloud architectures

4. Data migration support.

5. Customer support is great.

5. Data virtualization and automation. Review collected by and hosted on G2.com.

1. UI could be better.

2. Learning curve in the beginning, since it is complex.

3. Pricing

4. Third-party integrations could be better

5. Should have more automated workflows. Review collected by and hosted on G2.com.