The Appen platform combines human intelligence from over one million people all over the world with models to create training data for ML projects. Appen users can upload data to the Appen platform, and they provide the annotations, judgments, and labels needed to help create ground truth for models.
N/A
IBM Cognos Analytics
Score 7.5 out of 10
N/A
IBM Cognos is a full-featured business intelligence suite by IBM, designed for larger deployments. It comprises Query Studio, Reporting Studio, Analysis Studio and Event Studio, and Cognos Administration along with tools for Microsoft Office integration, full-text search, and dashboards.
$11.25
per month per user
Saturn Cloud
Score 7.7 out of 10
N/A
Saturn Cloud is an ML platform for individuals and teams, available on multiple clouds: AWS, Azure, GCP, and OCI. It provides access to computing resources with customizable amounts of memory and power, including GPUs and Dask distributed computing clusters, in a wholly hosted environment. Saturn Cloud is presented as flexible and straightforward for new data scientists while giving senior and experienced staff the
capabilities and configurability they need.…
It is well suited for the users and potential employee who are free of any job perspective and need their free time to be utilized. Users can use their free time to be used for submission of interesting tasks.
Whereas the number of tasks are very less and processing time is also very extensive and recruitment takes time more.
Well suited: Financial reporting - It can handle complex, pixel perfect, muti-page reports with scheduled delivery to stakeholders (like sales report by region on quarterly periodicity) Operational dashboard across departments - It can combine multiple data sources (ERP, CRM, excels etc) with filters, and embedded AI insights Less appropriate: Live dashboards - As stated earlier as well, IBM Cognos Analytics doesn't suit well for live dashboards or event driven data. For ex: live web traffic data or IOT device data, etc Data science - Although IBM Cognos Analytics is great tool for data exploration but it should not be used as a substitute for Python or R, which has edge over advanced modelling and stats based workflows like predictive modelling or clustering
Saturn Cloud is a powerful data science platform that offers numerous benefits to organizations. It simplifies and streamlines the development, deployment, and scaling of data science and machine learning models. The platform addresses common business problems such as scalability, collaboration, efficiency, and cost-effectiveness. With Saturn Cloud, organizations can easily handle large datasets and complex computations, collaborate effectively among data science teams, automate repetitive tasks, optimize workflows, and utilize flexible and cost-efficient cloud resources. By leveraging Saturn Cloud, organizations can accelerate their data science projects, improve productivity, and achieve better outcomes in areas such as predictive modeling, recommendation systems, fraud detection, and more.
IBM Cognos Analytics enables customer data segmentation, which is essential for marketing, improving and streamlining purchasing behavior and preferences. This helps companies create more targeted and effective marketing campaigns.
Our clients Through data analysis, we can identify and observe trends in the behavior of other clients, allowing us to anticipate needs and adjust strategies to avoid consequences.
While Saturn Cloud offers a range of pre-built templates and workflows, there is currently limited support for customization. For example, users may not be able to modify the pre-configured environments that come with the templates, or may find it difficult to integrate their own custom libraries and tools. Offering more flexibility in this area could help users tailor the platform to their specific needs and workflows.
While Saturn Cloud offers a variety of pre-built environments for data science and machine learning workloads, some users may prefer to use custom Docker images instead. However, the platform currently has limited support for Docker, which can be a limitation for users who need to work with specific dependencies or custom libraries. Adding more robust support for Docker could help to make the platform more versatile and adaptable to a wider range of use cases.
For an existing solution, renewing licenses does provide a good return on investment. Additionally, while rolling out scorecards and dashboards with little adhoc capabilities, to end users, cognos is very easily scalable. It also allows to create a solution that has a mix of OLAP and relational data-sources, which is a limitation with other tools. Synchronizing with existing security setup is easy too.
We have a strong user base (3500 users) that are highly utilizing this tool. Basic users are able to consume content within the applied security model. We have a set of advanced users that really push the limits of Cognos with Report and Query Studio. These users have created a lot of personal content and stored it in 'My Reports'. Users enjoy this flexibility.
This is user friendly , better than its counterparts. Anyone familiar working with other cloud solutions for GPU will agree on this. Hence the rating of 10 was given to this. I personally love the fact that I get so much compute time for being a free user which is very efficient in terms of budget
Reports can typically be viewed through any browser that can access the server, so the availability is ultimately up to what the company utilizing it is comfortable with allowing, though report development tends to be more picky about browsers and settings as mentioned above. It also has an optional iPad app and general mobile browsing support, but dashboards lack the mobile compatibility. What keeps it from getting a higher score is the desktop tools that are vital to the development process. The compatibility with only Windows when the server has a wide range of compatibility can be a real sore point for a company that outfits its employees exclusively with Mac or Linux machines. Of course, if they are planning on outsourcing the development anyways, it's a rather moot point
Overall no major complaints but it doesn't handle DMR (Dimensionally Modeled for Relational) very well. DMR modelling is a capability that IBM Cognos Framework Manager provides allowing you to specify dimensional information for relational metadata and allows for OLAP-style queries. However, the capability is not very efficient and, for example, if I'm using only 2 columns on a 20-column model, the software is not smart enough to exclude 18 columns and the query side gets progressively larger and larger until it's effectively unusable.
Why is their web application not working as fast as you think it should? They never know, and it is always a a bunch of shots in the dark to find out. Trying to download software from them is like trying to find a book at the library before computers were invented.
Onsite training provided by IBM Cognos was effective and as expected. They did not perform training with our data which was a bit difficult for our end-users.
The online courses they offer are thorough and presented in such a way that someone who isn't already familiar with the general design methodologies used in this field will be capable of making a good design. The training environments are provided as a fully self contained virtual machine with everything needed already to create the environments. We've had some persisting issues with the environments becoming unavailable, but support has been responsive when these issues arise and straightening them out for us
Make sure that any custom tables that you have, are built into your metadata packages. You can still access them via SQL queries in Cognos, but it is much easier to have them as a part of the available metadata packages.
Appen offers projects mostly related to my native language and also according to my expertise . It offers very interesting projects to be completed , which requires not very expertise and less time to be completed for each task. It is also very convenient to use after selection for the task and also well rewarding against the time consumed for the task completion.
Power BI is stronger for quick ad-hoc analysis and dashboards, but IBM Cognos Analytics is better when consistency, precision, and mass distribution matter. Tableau is best for interactive analysis, while IBM Cognos Analytics is better for standardized, repeatable enterprise reporting. Sigma shines for customizable dashboards and drill-down analysis while IBM Cognos Analytics holds an edge in data discovery and visualization.
Saturn Cloud provides an R server, that's super important. Even you can write R on CoLab with different settings, but it is inconvenient and slow. Saturn Cloud can give me a different IDE environment that I'm more used to, even if I'm using Python. Whereas CoLab is more dedicated to Jupyter notebook
The Cognos architecture is well suited for scalability. However, the architecture must be designed with scalability in mind from day one of the implementation. We recently upgraded from 10.1 to 10.2.1 and took the opportunity to revamp our architecture. It is now poised for future growth and scalability.
Although we are still in the implementation phase with Saturn Cloud, we anticipate significant positive impacts on our business objectives.
The platform is expected to enhance our computational capabilities with its easy access to top-tier NVIDIA GPUs, which should accelerate our AI and machine learning projects. We believe this will lead to reduced development times and faster deployment of our generative AI models.
While Saturn Cloud provides excellent computational resources and reliable uptime, I find that their user interface could be improved. The UI can be unintuitive at times, making it a bit challenging to navigate and configure certain settings. Enhancing the user interface to be more streamlined and user-friendly would significantly improve the overall experience. Having pre-configured stacks readily available would also save time and make the platform even more efficient to use.
