Owner/Full Stack Software Engineer at Maraphonic, Inc.
Real User
Top 20
2024-07-30T15:28:00Z
Jul 30, 2024
IBM Cloud Pak for Integration includes monitoring capabilities to track the performance and health of your integrations. You can quickly roll back to a previous version if an issue arises. Additionally, it supports incremental deployments, allowing you to shift traffic to a new version of an API gradually. For example, you can start by directing 10% of traffic to the new version while the rest continue using the legacy version. If everything works as expected, you can gradually increase the traffic to the new version over time. IBM Cloud Pak for Integration has a client base that includes numerous organizations using AI and machine learning technologies. We leverage an open-source machine learning framework and integrate it with Kafka to help create and manage various products and data retrieval processes. For companies with private data, the framework first retrieves relevant data from a GitHub database, which is then combined with the final request before being sent to a language model like GPT. This ensures that the language model uses your specific data to generate responses. Kafka plays a key role by streaming real-time data from file systems and databases like Oracle and Microsoft SQL. This data is published to Kafka topics, then vectorized and used with artificial intelligence to enhance the overall process. It's like an old-fashioned approach. The best way is to redesign it with products such as Kafka. Overall, I rate the solution an eight out of ten.
I recommend using it because, in today's context, the cloud plays a significant role. Within the same user interface, you can develop applications and manage multiple applications, making it a more user-friendly option. Moreover, you can explore various other technologies while deploying on the cloud, broadening your knowledge of cloud technologies. In my case, the transition led to my learning of Kubernetes, enabling multi-scaling and expanding my technical skills. It was a valuable experience, and I had the opportunity to learn many new things during the migration process. I can easily rate it an eight or nine out of ten.
Sales Manager at a tech services company with 51-200 employees
Real User
2022-01-04T21:33:07Z
Jan 4, 2022
I rate Cloud Pak for Integration eight out of 10. It requires a particular high-profile skillset, and it's not easy to implement. It is definitely stable and scalable, but it needs expertise.
Chief Innovation Officer at a consultancy with 201-500 employees
Real User
2021-06-22T20:51:31Z
Jun 22, 2021
We are an IBM partner. It's something that we use for our clients especially. We're specialized in the product, and it's something that we have really good experience with. It's multi-cloud. You can have it on-premise or any other kind of cloud. We use both the 2020 and 2021 versions of the solution. I'd rate the solution at a nine out of ten. We've mostly been quite satisfied with the product in general.
API (application programming interface) management is the process of managing different API functions, such as designing, releasing, documenting, analyzing, and monitoring APIs in a safe environment.
IBM Cloud Pak for Integration includes monitoring capabilities to track the performance and health of your integrations. You can quickly roll back to a previous version if an issue arises. Additionally, it supports incremental deployments, allowing you to shift traffic to a new version of an API gradually. For example, you can start by directing 10% of traffic to the new version while the rest continue using the legacy version. If everything works as expected, you can gradually increase the traffic to the new version over time. IBM Cloud Pak for Integration has a client base that includes numerous organizations using AI and machine learning technologies. We leverage an open-source machine learning framework and integrate it with Kafka to help create and manage various products and data retrieval processes. For companies with private data, the framework first retrieves relevant data from a GitHub database, which is then combined with the final request before being sent to a language model like GPT. This ensures that the language model uses your specific data to generate responses. Kafka plays a key role by streaming real-time data from file systems and databases like Oracle and Microsoft SQL. This data is published to Kafka topics, then vectorized and used with artificial intelligence to enhance the overall process. It's like an old-fashioned approach. The best way is to redesign it with products such as Kafka. Overall, I rate the solution an eight out of ten.
I recommend using it because, in today's context, the cloud plays a significant role. Within the same user interface, you can develop applications and manage multiple applications, making it a more user-friendly option. Moreover, you can explore various other technologies while deploying on the cloud, broadening your knowledge of cloud technologies. In my case, the transition led to my learning of Kubernetes, enabling multi-scaling and expanding my technical skills. It was a valuable experience, and I had the opportunity to learn many new things during the migration process. I can easily rate it an eight or nine out of ten.
I rate the solution an eight out of ten.
I rate Cloud Pak for Integration eight out of 10. It requires a particular high-profile skillset, and it's not easy to implement. It is definitely stable and scalable, but it needs expertise.
We are an IBM partner. It's something that we use for our clients especially. We're specialized in the product, and it's something that we have really good experience with. It's multi-cloud. You can have it on-premise or any other kind of cloud. We use both the 2020 and 2021 versions of the solution. I'd rate the solution at a nine out of ten. We've mostly been quite satisfied with the product in general.