This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course.

Cognos Analytics users can now connect to the more powerful data science capabilities in Watson Studio: AutoAI, Jupyter Notebook, and GPUs. With this integration, both data science and business intelligence teams can share a single ecosystem to make the most of their organizations' data. The integration between the two offerings serves as a bridge to empower data scientists and business analysts to collaborate on the cloud. Data scientists can easily script against governed Cognos data in Watson Studio and share results back into the Cognos ecosystem. You will refine the data and build a model using Watson Studio and IBM Watson Machine Learning.

IBM Developer Advocates Anam Mahmood and Sidra Ahmed conducted a workshop on 3rd February. Their goal was to show how everyone can easily use Jupyter Notebooks in IBM Watson Studio to run small pieces of code that process your data and immediately show you the results of your computation in an interactive environment and quickly build machine learning models. The session was divided into two sections. The first half of the workshop was led by Sidra, where she welcomed the audience and talked about the agenda. She then explained to them about Data Science, Artificial Intelligence, Machine Learning, and Deep Learning.

During the progress of any data science project, most data scientists tend to utilize tools and gadgets that would help them reach their goals faster and more efficiently. They use these tools to speed up routine tasks to save their energy and brain-power to find solutions for the current problem they are trying to solve. Because of this desire to speed up a project's workflow, there are so many data science tools out there that you can choose from, whichever suits the task at hand. And believe me, when I say this, there are hundreds of tools you can choose to finish your project; at the end of the project, you will discover that you used multiple of these tools to finish one project. Since any data science project consists of different steps, from gathering and collecting data to clean it, analyzing, and visualizing it, there are tools designed and developed for each of these steps. Tools to automatically collect data for you from all over the web, or tools to visualize your data and help you tell the story hidden within, or tools to help you clean your data and use the most relevant part of it in your analysis.

Every once in awhile an idea comes along that's so good it makes you wonder why it took so long for someone to think of it. IBM's AI Fact Sheets is one of those ideas. AI Fact Sheets are a lot like packaged food nutrition labels. They contain information about an AI model's development, capabilities, benchmark performance, and more. Big Blue today announced its plans to "commercialize key automated documentation capabilities from IBM Research's AI Factsheets methodology into Watson Studio in Cloud Pak for Data throughout 2021." In other words: businesses and developers using Watson Studio in Cloud Pak for Data will soon have access to an automated AI Fact Sheets tool to create transparency and info reports.

For the last year and a half I have been using Watson Studio and DSX Local as my development environments for exploring machine learning and implementing models. Inspired by Siraj Raval's video on Azure Machine Learning I decided to take the plunge and check out Microsoft's ML environment. This posting covers my first impressions, the good and the bad, and contrasts Azure ML with Watson Studio / DSX. Getting a first taste of Azure ML from a standing start is relatively easy -- here's a quick overview of the preparatory steps: As Siraj notes in his video, Microsoft touts a hybrid (on prem cloud) approach. Before getting into the experience of using Azure ML, I'd like to contrast my experience of "hybrid" Microsoft vs. IBM: Full disclosure: I am an IBM employee, but I can see pros and cons to both approaches.

Many are the time when businesses have workflows that are repetitive, tedious and difficult which tend to slow down production and also increases the cost of operation. To bring down the cost of production, businesses have no option rather than automate some of the functions to cut down the cost of production. By digitizing repetitive tasks, an enterprise can cut cost on paperwork and labour which further eliminates human error thus boosting efficiency leading to better results. For businesses to gain from the above benefits, they must choose the right automation tools otherwise it will all be in vain. Automating process involves employing artificial intelligence platforms that can support the digitalization process and deliver the same or better results that human being would have achieved. Artificial Intelligence (AI) is when a machine mimics the cognitive functions that humans associate with other human minds, such as learning and problem solving, reasoning, problem solving, knowledge representation, social intelligence and general intelligence.

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.

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.

Strategic investments in AI can be a game changer. To fulfill the promise of AI, organizations are now tackling skill-set gaps, deployment and governance processes. In particular, businesses are seeking an alternative where citizen data scientists can quickly get started, and expert data scientists can speed experimentation time from weeks and months to minutes and hours. They need a multimodal data science and AI environment where data and analytics specialists collaborate with other experts and optimize model performance end-to-end. AutoAI is available within IBM Watson Studio with one-click deployment through Watson Machine Learning.