Make sure you stay informed about the latest SAS applications and solutions for data scientists. Welcome to our data science experience, this month Federica Citterio, Data Scientist at SAS introduces her views on NLP. Federica Citterio, talks on the applications and importance of NLP for data scientists. Image captioning: combine computer vision and natural language processing. What can you achieve with NLP? Explore the NLP resources available for you.

Check out the new dedicated Data Science Experience page and stay informed on the latest SAS applications and solutions for data scientists. We will discuss a variety of interesting topics such as AI, Machine learning, Computer Vision, Model Management and more. These will be introduced by Data Scientists in a monthly series.

Data Science Experience is now Watson Studio. Although some images in this code pattern may show the service as Data Science Experience, the steps and processes will still work. In this Code Pattern we will use Apache SystemML running on IBM Watson Studio to perform a Machine Learning exercise. Watson Studio is an interactive, collaborative, cloud-based environment where data scientists, developers, and others interested in data science can use tools (e.g., RStudio, Jupyter Notebooks, Spark, etc.) to collaborate, share, and gather insight from their data. Apache SystemML is a flexible machine learning platform that is optimized to scale with large data sets.

We sat down with Steven Astorino, IBM VP of Development, Hybrid Cloud, z Analytics, and Canada Lab Director, to chat about his work in machine learning and his upcoming talk at Reactive Summit in Montreal. So, first of all, could you talk about your background? What initially drew you to machine learning and data science? One aspect of my job really revolves around data science and machine learning. I manage IBM products like Data Science Experience, Watson Studio, SPSS Decision Optimization, Watson Explorer--that's all on the ML/data science part of the house AI.

IBM has ushered its customers into the era of enterprise data science for more than a decade, starting with the investment of the ILOG CPLEX and SPSS acquisitions. As the data science market evolved, new macro trends developed, and IBM invested in advanced technologies and platforms to respond to this shift. In 2016, IBM introduced Data Science Experience and several Watson offerings, which blurred the lines between our new and old technologies. We have now made the decision to simplify our portfolio for our customers under one single brand -- IBM Watson Studio. IBM Watson Studio was first announced in the IBM Public Cloud at our Think Conference in March 2018, which included the integration of the capabilities of Data Science Experience Cloud and a new interface for SPSS Modeler.

More and more cloud-computing experts are talking about "multicloud". The term refers to an architecture that spans multiple cloud environments in order to take advantage of different services, different levels of performance, security, or redundancy, or even different cloud vendors. But what sometimes gets lost in these discussions is that multicloud is not always public cloud. As machine learning (ML) continues to pervade enterprise environments, we need to understand how to make ML practical on multicloud -- including those architectures that span the firewall. Let's look at three possible scenarios.

What does a data-driven business look like? Is it endless lines of code and algorithms running on cloud infrastructure, sending signals back to a predictive analytics lab? Is it a huddle of Ph.D. data scientists poring over graphs that no one else can understand? Both experts and technologies have a place in it, but relying too much on either can blow the whole thing. A business that runs day-to-day on data needs a culture that meshes machine intelligence and human business sense.

IBM has spent the past several years putting a laser focus on what it calls cognitive computing, using its Watson platform as the foundation for its efforts in such emerging fields as artificial intelligence (AI) and is successful spinoff, deep learning. Big Blue has leaned on Watson technology, its traditional Power systems, and increasingly powerful GPUs from Nvidia to drive its efforts to not only bring AI and deep learning into the cloud, but also to push AI into the enterprise. The technologies are part of a larger push in the industry to help enterprises transform their businesses to take advantage of such trends as the rise of the cloud, the increasing use of mobile technologies and the skyrocketing growth of data that is being generated by these companies and needs to be processed and analyzed. Much of the work with AI, deep learning and analytics have been done in the cloud, promoted by hyperscale cloud providers like Amazon Web Services (AWS), Microsoft Azure and Google Cloud. IBM also has put many of its capabilities into its own cloud.

New AI technologies like machine learning and deep learning are fitting ever more snugly into the shifting enterprise landscape. Deep learning in particular is being adopted by an increasing number of enterprises for expanded insights and with the aim to better serving their clients. Thanks to more powerful systems and graphics processing units (GPUs), we are able to train complex AI models that enable these insights. IBM has long been one of the leaders in analytics and over the last year or two introduced two key new products, Data Science Experience and IBM PowerAI, designed to enable enterprises to more easily start using advanced AI technologies. Today we're unveiling that we are bringing these two key software tools for data scientists together.

New AI technologies like machine learning and deep learning are fitting ever more snugly into the shifting enterprise landscape. Deep learning in particular is being adopted by an increasing number of enterprises for expanded insights and with the aim to better serving their clients. Thanks to more powerful systems and graphics processing units (GPUs), we are able to train complex AI models that enable these insights. IBM has long been one of the leaders in analytics and over the last year or two introduced two key new products, Data Science Experience and IBM PowerAI, designed to enable enterprises to more easily start using advanced AI technologies. Today we're unveiling that we are bringing these two key software tools for data scientists together.