IBM Watson Machine Learning Reviews

I'm mainly checking how easy it is to use this platform to implement products. My idea isn't very flexible, and it's quite easy to do something basic. 

I was particularly interested in trying the AutoML feature to see how it handles data and proposes new models. The variety of models it provides is impressive. However, I'm not completely sold on IBM yet. I'm also checking out Google and some free platforms on the market.

In future releases, I would like to see a more flexible environment. It's a good product for customization and developing products. 

But when we need the most control over the delivery, Watson isn't the best. We can't fix everything because we're working with a machine that's creating a product. And the ability to go in-depth and tweak our model easily would be really nice.

I have been using it for three months now. I work with the latest version.

My team isn't using it directly yet. We're still evaluating the technology to see if it's the right fit for our needs. We've experimented with virtual systems and explored their implementation and deployment on our web pages. 

We're mainly assessing the level of customization and identifying the most fitting business case for using this platform.

While IBM seems quite unique in its approach, there are other options like TensorFlow and other providers offering different perspectives. So, similar to others, IBM stands out in its own way.

I was also working with Google.

I've only been using the free tier, but it's quite competitive on a service basis. 

Heavy data usage and management can drive up the costs, but that's true for most platforms. Ultimately, pricing should be seen as an investment in return for potential profits.

My advice would be to carefully evaluate if your business case aligns well with the services offered by IBM. 

Medium-sized companies might be a good fit for IBM, and I recommend reaching out to them because they have extensive experience working with similar businesses. 

Startups might be better off with Google or Amazon due to their expertise with large servers and cost-effective technology, at least initially. Large companies, with their teams of experts, can probably adapt to any platform effectively.

Overall, I would rate the solution an eight out of ten.

We started to use the solution for POCs in our organization. So, we are not in a state where we use its services at a full-fledged level. Additionally, we use it to solve our queries and understand whether it is actually helping us solve our problems. Basically, this is how we have been using the solution for a year and a half. Since we are getting results from the solution, we are moving ahead with its implementation in our organization.

The solution has been helpful for our organization, especially for those processes that require manual decision-making and expertise. By using Knowledge Studio and the crawlers, we were able to utilize the knowledge of our experienced staff to improve our understanding and use of Watson Discovery.

The most valuable aspect of the solution is its crawlers. Our company typically uses crawlers before utilizing IBM Watson Studio or Knowledge Studio to train them and obtain the necessary information. I particularly like Knowledge Studio because it allows me to train it to fit my specific requirements or needs, which is easy. Once trained, using Watson Discovery becomes easier.

I had not considered how the solution could be improved because I was focused on how it was helping me to solve my issues. If I consider how we want to use it in our organization, certain areas of improvement can be addressed. For instance, we want to use it with Generative AI, not like ChatGPT, but in a way intended for industrial use. It would be beneficial to incorporate more AI into the solution.

I have experience with IBM Watson Machine Learning for a year and a half. Also, we use the web version in our organization.

It is a stable solution. Stability-wise, on a scale of one to ten, I rate the solution somewhere between nine to ten.

Scalability-wise, I rate the solution ten out of ten. A team of ten or twenty individuals is using the solution for POC, and we are currently testing it. However, we plan to expand its usage to over a hundred people.

Personally, I have found the local technical support for the solution to be brilliant, which is why I would rate it a ten out of ten. I have not used online support before, so I don't know about it very well.

Positive

IBM Watson Machine Learning is the first solution we have implemented in our organization.

The solution's initial setup was easy. Setup-wise, I rate this solution an eight or nine out of ten.

The deployment process took a few days.

Our company is still working on the pricing front, so I won't be able to comment on the solution's pricing. We are still in the process of analyzing its pricing to get a better understanding.

I would recommend the solution to others because I used it for a POC and had a very good experience. Overall, I rate the solution a nine or ten out of ten. Considering my use of the solution and its involvement in the start of my journey, I rate the solution a ten out of ten.

We use different artificial intelligence models to build questions and get answers for clients. 

We can give the client actionable insights. We can deploy voice agents and chatbots quickly with Watson. We can also have assistance in conversational artificial intelligence applications that power large language models. We can deliver automated self-service support across channels and touchpoints. There is learning integration with other business tools. We can use this with different clients in different areas. We can use our business data, and we can, with our team, build, train, turn, and deploy different models on Watson and integrate them into existing chatbots that can help deliver contextual responses.

If we have predictive events, we can integrate the risk into the different artificial intelligence products, for example. We can help to reduce the coding complexity to enable development teams, and we can focus on driving value for businesses, for example. We can create content quickly with generative artificial intelligence.

We can build models to generate various content, such as ideas for marketing and sales campaigns or emails.

We can construct different dashboards that we can use to monitor and check accuracy. It helps us make better decisions.

We can enable and change developer productivity with artificial intelligence-recommended code based on natural language input or exciting source code. With the tool, we can help reduce coding complexity to enable development teams to focus on driving value for the business.

Sometimes training the model is difficult. We need to have at least a few different components to evaluate and understand the behavior of different users to have a very, very high accuracy in the models and to have a very good response to the tool. Therefore, the learning of the model is sometimes complex. 

We may need a different code validation tool to understand the behavior of the models. 

I haven't had any issues with the stability of the tool. 

Technical support is very, very good.

Positive

We do have alternative solutions, including Python. 

Sometimes it can be complex to deploy, however, it depends on what the client needs or the ecosystem that the client has. If a client has a difficult objective, the training is more difficult.

The maintenance is easy. We have a dashboard that helps us monitor the behaviour of the model, as well as the accuracy and metrics.

The solution is expensive.

We are IBM customers. 

I'd rate the solution eight out of ten.

We use this solution to understand the intent of our customers when they ask for products. As a result, we can understand user sentiments and predict what they are trying to achieve.

It has been beneficial to our customers. The success rate is good, and it reduces a lot of direct agent interactions allowing us to resolve many inquiries by self-service.

The supporting language is limited, and other languages could be added. In addition, because it is on cloud, we cannot have any sensitive information because of regulations by the central banks and the government cybersecurity-related issues. We do not use any transactional details, just manually driven ones.

We have been using this solution for almost one year, and it is deployed on public cloud. We use OpenShift as our provider.

It is pretty stable. There are no complaints of downtime, and if there are issues, they alert us before the system can go down. We have roughly eight million customers, so about 5,000 active users per day. We do not need to maintain and just need one staff to do configurations.

It is scalable because it is purely a cloud-based solution, so we can bend and decrease the capacity as much as we want.

Their technical support is amazing, and I rate them an eight out of ten.

Positive

We have used Whatsapp Chatbots, and we liked it because it is on-premises, and we had more control. However, the implementation is hard, and you need the expertise to use it. You also need to have Python and programming tools to implement it.

The initial setup was straightforward, and the configuration was not complex. The only limitations are the licenses and the capacity. It is cloud-based, so all of the new features are available. Our deployment was done in-house.

We have seen an ROI. It has improved self-service and customer satisfaction.

I rate the pricing an eight out of ten, with one being the least expensive and ten being the most expensive. They are no hidden or extra costs, and you can get a bundle based on your usage.

I rate this solution an eight out of ten. Regarding advice, it is a very powerful tool if utilized correctly.

Most of the use cases have been around taking in a lot of human-created data by providers. For example, electronic medical records including the notes, the information, and the clinical aspect of care. What they're typically trying to do is adjust a lot of that type of information. 

Sometimes there is member feedback or member sentiment that's captured. The idea is to try to quickly assess and analyze that information and then prioritize it. The goal is to put the data in buckets for particular use around quality improvement and around identifying risks earlier, and things of that nature.

The solution has allowed us to remove a lot of manual labor and cut costs around getting people to perform certain tasks.

The most valuable aspect of the solution's the cost and human labor savings. 

The ability to automate a lot of the work helps save organizations time and money. It also accelerates the whole digitization process. It's tremendously valuable.

I haven't dealt with the solution as significantly in the last probably two or three years. That said, my last deeper dive into that was around the need for the product within the organization. I'm sure it's gotten better and better as the program has gotten better, however, early on, they relied heavily on building out these massive reference tables. That was a ton of the work that had to be done

Honestly, I haven't seen any comparative report that has run the same data through two different artificial intelligence or machine learning capabilities to get something out of it. I would love to see that.

The top three industries should go head to head to feed the same data and then get evaluated for accuracy. Something like that would be tremendously interesting. I don't know if something like that exists.

I have a long history with the solution. I've used it for 15 years - well before IBM purchased it and changed the name.

I would have to assume it's getting better due to the fact that it's being more widely adopted. More companies are paying the vendors to use it. I would have to assume it's gotten better. 

My understanding of the solution is that it is very scalable. If a company needs to expand it, they can do so easily.

I have never dealt with technical support. I can't speak to their level of responsiveness or knowledgeability at this time.

It's been many, many years since the implementation of the product. 

I don't remember it being a complex operation. What I remember was that they were trying to do it kind of as a SaaS solution where they used open connections and set up APIs and pushed data through a cloud-based model. As long as the sender had the ability to communicate in that way, it was pretty simple.

There are other healthcare companies, both on the payer and provider side that are still not there from a technology perspective. So they had to send batch files and lots of things like that. They couldn't use electronic CCDs for clinical records. Due to this, there was a limitation. It may be more complicated when the sender can't match the technology that the receiver would be looking for them to utilize in terms of delivery.

I can't recall the exact pricing. It's my understanding that the gist of the pricing was based on the volume of work. I don't remember any real detail on the pricing. I can't really say if it more expensive than other options or not.

I'm not a consultant or a reseller. I've only dealt with this solution in the past as a user, for the most part.

When done right, this automation can be very helpful. There are companies that were pioneers in this space. For example,  Autonomy, which was purchased by Hewlett Packard. However, in that case, it was an epic failure. They've been in a lawsuit for the last decade.

I've been more focused on other parts of healthcare information. So honestly I'd say in the last probably two, two and a half years, I haven't really dug into the analytic capability.

I would advise other organizations to proceed with caution and do a lot of homework and maybe pilot the solution before diving in.

Overall, I would rate the solution at a seven out of ten.

We primarily use the solution for data science purposes. We use it for the deep learning, machine learning, and classification of the data.

The solution is very valuable to our organization due to the fact that we can work on it as a workflow. That's the processing we wanted to go with. All the data science on the package needs to be able to easily fill the workflow, and it does this.

The solution is extremely scalable, which was an important aspect for us. 

The solution needs to improve on its consumerization. They need to expand on it. Right now, they don't make it very easy.

Scaling is limited in some use cases. They need to make it easier to expand in all aspects.

Their service is a little bad sometimes.

We've been using the solution for at least three years, however, I do believe it has actually been longer than that.

The stability is good. It's reliable. It doesn't crash or freeze and it's not buggy in any way.

We do have some issues with scalability. While we can easily scale some use cases, we can't scale others. We're not sure why and we wish this was better, as being able to scale is important to us.

Over 50 people currently use the solution and most of them are data scientists.

The technical support is okay. However, I find that corporate support is weak. If you're only looking to upgrade, you can hit a wall. That said, the support people are very knowledgeable. It's more of a corporate problem rather than a technical support problem, as I do find the technical support staff pretty helpful overall.

I also have experience with Dataiku. I prefer Watson to use Dataiku, due to the fact that it's more flexible as a solution.

The initial setup is not complex. It's pretty straightforward.

I'm not sure about the licensing costs. That's not really my department.

I'm not sure which version of the solution we're using right now.

I'd recommend the solution to other users. It's more flexible than, say, Dataiku, even though their service can be a little bad sometimes.

I'd rate the solution seven out of ten. It can handle most of the work we need it to do.

We are using this product to do some R&D work. 

Using this product for R&D has helped with our understanding of how machine learning will help us. For example, we are working on image processing for patient recognition.

I like the whole concept of using Watson.

It is has a lot of good features and we find the image classification very useful.

They should add more GPU processing power to improve performance, especially when dealing with large amounts of data.

We have been using IBM Watson Machine Learning for less than six months. It's very new for us.

Stability-wise, my experience so far has been good.

Although Watson is scalable, the performance degrades the more it is scaled.

We have a team of 30 people who are working on it. At this point, we have no plans to increase our usage.

The technical support from IBM is good.

The installation and initial setup are easy.

My team handled the deployment.

The pricing model is good.

We are not using the licensed version; rather, we are just using a trial account.

As a developer, I have not personally evaluated other options. One of the other teams does the product research for bringing solutions into the company.

This is a product that I definitely recommend.

I would rate this solution an eight out of ten.