In March 2021, a 200,000 tonne ship got stuck in the Suez Canal, and the global shipping industry suddenly caught the world’s attention. It made us realize ships play an important role in our daily lives. Really important in fact; 90% of the things we consume arrive by ship. Take a look at this map. By visualizing vessel routes over time, the pattern creates a map of the earth. Note the lack of vessels travelling close to the coast of Somalia where piracy is common.
The best way to appreciate key concepts involving digital transformation is to look at real-world examples. In a recent Kong webinar, I sat down with Solutions Engineer Ahmed Koshok as he reviewed several real-world case studies that help illuminate the role of microservices in making digital transformation successful for organizations. The case studies included Papa John’s, NextJ Systems, and Yahoo! Japan.
Apps of today differ from those of the past. Evolving organizations like Cargill need to scale quickly to support millions of users, have global availability, manage petabytes or more of data and respond in milliseconds. That’s why modern apps now leverage API automation.
We are roughly a decade removed from the beginnings of the modern machine learning (ML) platform, inspired largely by the growing ecosystem of open-source Python-based technologies for data scientists. It’s a good time for us to reflect back upon the progress that has been made, highlight the major problems enterprises have with existing ML platforms, and discuss what the next generation of platforms will be like.
Everyone knows that more and more data is moving to the cloud. According to the latest research, 94% of all enterprises use cloud services and 48% of businesses store classified and important data in the cloud. While the cloud is ubiquitous, in practice it consists of data infrastructures in various locations around the world. The question of where the cloud data infrastructure storing your specific data is located is becoming increasingly important.
Whether you're implementing a microservice architecture that will be scalable and resilient or forward-thinking for interoperability possibilities, APIs provide the essential level of abstraction that enables communication between separate pieces of software. Modifying an API architecture once it is live is no small feat, so taking the time before building one to identify your needs and goals for your API is a worthwhile step that will help you create the API you want.
Everyone wants to manage their data, and if it’s a feature store, even better! But for optimal data management, we must first discuss lightweight zero upfront setup costs and maximizing utility with ClearML-data. ClearML-data mimics the light weightiness of git for data (who doesn’t know git?) and gives it a spin. It is an open-source dataset management tool which is extremely efficient and conveys how we view DataOps and its distinction from git-like solutions, including.
Amid the rapid pace of change felt by organizations this year, it’s no surprise that digital transformation projects have been high on the agenda for CIOs across the globe. To achieve both their organizational and transformation goals and become digitally agile as a result, CIOs are often tasked with creating the conditions needed to enable an intelligent and flexible digital core.
