Distributed systems built on Reactive Microservices introduce new challenges and require new tools to meet those challenges. The Lightbend Reactive Architecture: Advanced learning path teaches managers, developers, and architects new techniques to help cope with the realities of distributed architectures. You will learn how the Lightbend Reactive Platform can be used to build the distributed systems of tomorrow.
After completing this learning path, you'll understand 12-factor apps and how microservices are managed with the IBM Cloud Kubernetes Service and Istio. You'll get hands-on experience working with containers, Kubernetes, and how to deploy containerized apps. You'll learn how to deploy microservices in a cluster and how to connect, manage, and secure those microservices.
Learn how to code in Python for data science, then analyze and visualize data with Python with packages like scikit-learn, matplotlib and bokeh. This is an action-packed learning path for data science enthusiasts and aspiring data scientists who want to learn data science hands-on with Python.
Designing cloud-native systems and microservices requires architects and teams to think differently. The Lightbend Reactive Architecture learning path teaches development managers, architects, and software developers - how to think about Reactive Systems from design through to production.
Through our guided lectures and labs, you'll first learn Neural Networks, and an overview of Deep Learning, then get hands-on experience using TensorFlow library to apply deep learning on different data types to solve real world problems.
When a butterfly flaps its wings what happens? Does it fly away and move on to another flower or is there a spike in the rotation of wind turbines in the British Isles. Learn data science today and enter a world where we work to create order out of chaos that will blow you away!
Are you interested in understanding 'Big Data' beyond the terms used in headlines? Then select this learning path as an introduction to tools like Apache Hadoop and Apache Spark Frameworks, which enable data to be analyzed on mass, and start the journey towards your headline discovery.
To light a fire, do you use a match, a lighter, or a torch? Depends on the size of the fire, much like the decisions that lead one to use Python, R, or Scala. Spark your interest in selecting the tools you need to tackle Big Data with ease, that will not just blow out.
Are you interested in moving beyond the elephant in the room and understanding Hadoop as a foundational tool set in your future? Then select this learning path to gain exposure to the tools used in Big Data, Hadoop's core components and supporting open source projects.
Solid understanding and experience, with core tools, in any field promotes excellence and innovation. Apache Spark, as a general engine for large scale data processing, is such a tool within the big data realm. This learning path addresses the fundamentals of this program's design and its application in the everyday.