Machine-learning-as-a-service (MLaaS) tools for data analytics could increase the accuracy and efficiency of your research in the data science realm without requiring substantial upfront costs from on-site equipment. That's because MLaaS options exist in the cloud. Here are six you should keep in mind if you're planning to invest in machine learning tools or would like to learn more about them. This option from Microsoft features a drag-and-drop interface that doesn't require coding expertise. It takes an applied approach to machine learning, allowing you to integrate the technology into your work swiftly.

As an increasing number of organizations drive AI-powered digital transformation, several key trends in operationalizing AI are emerging. Growth leaders are separating themselves from growth laggards by using AI and machine learning (ML) in modern application development. Below are some statistics provided by 451 Research: Leaders invest in models for digital transformation: More than half the digital transformation leaders adopted ML compared to less than 25 percent of laggards. Furthermore, 62 percent of enterprises are developing their own models. Prevalence of DevOps increases the demand for automation: 94 percent of enterprise companies have now adopted DevOps. Models are becoming integral to the development of enterprise apps—requiring continuous, synchronized and automated development and deployment lifecycles. Data science and DevOps/app teams collaborate more: In 33 percent of enterprises, the data science/data analytics team is the primary DevOps stakeholder. An increasing number of application developers are becoming interested in data science and AI, and many have already learned the fundamentals…

Although more organizations are learning about the value of centralizing data management across hybrid multiclouds and infusing data science and AI into the infrastructure, not everyone has been able to do it. Maybe they lack the resources or capital expenditure. Or maybe they just don't feel ready. These are the kinds of organizations we had in mind when we set out to create an as-a-Service version of our integrated data and AI platform, Cloud Pak for Data -- an offering that is currently available as beta. Of the many attributes of the as-a-Service version, "use case" examples will help organizations understand how services can be integrated together to support specific disciplines.

Whether you are counting cars on a road or people who are stranded on rooftops in a natural disaster, there are plenty of use cases for object detection. Often times, pre-trained object detection models do not suit your needs & you need to create your own custom models. How can you use machine learning to train your own custom model without substantive computing power & time? How can you use your custom-trained model to detect objects, in real time, with complete user privacy, all on a device with limited computing power? In this workshop, you will learn how to build an app that lets you use your own custom-trained models to detect objects.

Based on our interactions and the results of this study, we expect to see organizations not only adopt AI – but scale it across their enterprises, by building/developing their own AI, or putting ready-made AI applications to work. For example, according to the survey, 40% of respondents currently deploying AI said they are developing proof-of-concepts for specific AI-based or AI-assisted projects, and 40% are using pre-built AI applications, such as chatbots and virtual agents. I see the excitement building with clients every day. Consider just a couple of recent examples. Legal software developer LegalMation has leveraged IBM Watson and our natural language processing technology to help attorneys automate some of the most mundane litigation tasks, speeding, for example, the written discovery process from multiple hours to a few minutes.

Based on our interactions and the results of this study, we expect to see organizations not only adopt AI--but scale it across their enterprises, by building/developing their own AI, or putting ready-made AI applications to work. For example, according to the survey, 40% of respondents currently deploying AI said they are developing proof-of-concepts for specific AI-based or AI-assisted projects, and 40% are using pre-built AI applications, such as chatbots and virtual agents. I see the excitement building with clients every day. Consider just a couple of recent examples. Legal software developer LegalMation has leveraged IBM Watson and our natural language processing technology to help attorneys automate some of the most mundane litigation tasks, speeding, for example, the written discovery process from multiple hours to a few minutes.

How has the newer data science technology such as Watson Studio, Watson Machine Learning and Watson OpenScale been received by the business partner community? I mentioned in our previous blog that I was pleasantly surprised at how many IBM Business Partners have established a Data Science practice. The new data science technology has been very well received by our partner community. The partners closed out a very strong Q4 2019 demonstrating the value that they and their customers see in Watson Studio, Watson Machine Learning and Watson OpenScale. This is encouraging and demonstrates that we are building products that resonate in the market with our partners and their customers.

Based on our interactions and the results of this study, we expect to see organizations not only adopt AI – but scale it across their enterprises, by building/developing their own AI, or putting ready-made AI applications to work. For example, according to the survey, 40% of respondents currently deploying AI said they are developing proof-of-concepts for specific AI-based or AI-assisted projects, and 40% are using pre-built AI applications, such as chatbots and virtual agents. I see the excitement building with clients every day. Consider just a couple of recent examples. Legal software developer LegalMation has leveraged IBM Watson and our natural language processing technology to help attorneys automate some of the most mundane litigation tasks, speeding, for example, the written discovery process from multiple hours to a few minutes.

Whether you are counting cars on a road or people who are stranded on rooftops in a natural disaster, there are plenty of use cases for object detection. Often times, pre-trained object detection models do not suit your needs and you need to create your own custom models. How can you use machine learning to train your own custom model without substantive computing power and time? How can you use your custom-trained model to detect objects, in real time, with complete user privacy, all on a device with limited computing power? In this code pattern, you'll build an iOS, Android, or web app (or all three) that lets you use your own custom-trained models to detect objects.

Watch this video to see how to use Watson Machine Learning and IBM Watson Studio to create a data flow using IBM SPSS Modeler to predict chronic kidney disease. Find more videos in the IBM Watson Data and AI Learning Center at http://ibm.biz/learning-centers.