re:Invent Guide: Enterprise Transformation
Machine Learning Hero
I’m Guy Ernest, an AWS machine learning (ML) hero and time traveler. I spent many years in the future, or at least in big companies that are already using ML and artificial intelligence (AI) to invent, innovate, and dramatically boost their businesses. Let’s use this week to get inspired, plan carefully, and acquire the tools necessary to lead your company into the coming future, using cloud concepts, methodologies, tools, and services.
If you’re a large company with a legacy, then your journey is different from the “born in the cloud” startup companies around you. You already built successful business systems for transactions, ordering, ERP, and maybe even CRM. You’re managing multiple OLTP databases and probably even BI and OLAP. Your company already has data analysts and maybe even data scientists. Nevertheless, you don’t use the full potential of your data and you can have better advanced analytics and AI systems to help your company continue competing in today’s fast-moving markets.
From VM to serverless, from buying servers to calling APIs, from software development to ML engineering, the future of IT is changing rapidly. Come learn about what is coming from one of the individuals who is building that future: Matt Garman, VP of AWS compute services.
Start with your customers in mind, and work backward to the services that they want and need from you. Listening and talking with your customers is important to making sure that you serve them correctly and can plan your future systems to serve them even better. In this session, learn how to converse better with them using AI services that enhance your human engagements.
This session provides more inspiration and practical experience-sharing from a domain that is under complex regulation and is dealing with privacy issues, yet has the potential to change the life of every one of us for the better. Whatever your domain is, you can chart your path to AI with the right mindset shared in this session.
We start with data practices in the cloud while keeping existing on-premises systems operating. Learn how to replicate data for advanced analytics combining structured and unstructured data from various silos and systems across the company.
We continue with inspiration from Amazon on thinking about business problems and working backward to build ML systems based on the enterprise data. You can’t become Amazon in a week, but you can learn from their 20 years of experience.
In the future, many interfaces with online services will be using voice and natural language dialog. With Siri, OK Google, and Alexa, huge investments of the giant tech companies, this future is not far off anymore. In this session, you can see where Alexa is going and how you can get started with delighting your customers, managers, and employees.
Amazon.com was not born in the cloud, and it had and still has many on-premises databases and systems. The company must consider how to plan careful migration to the cloud, which systems to move first, how to make sure that there is no downtime, and how to train thousands of engineers to build and operate these systems in the cloud. These are critical lesson to learn to be successful in your transformation to the cloud.
Connecting the massive physical world in Amazon.com fulfilment centers to the cloud was not a simple task, and many of the lessons they learned in the process are relevant to every company that is operating logistics, physical buildings, agents in the field, and so on.
Both cloud and advanced analytics are new disciplines to many large companies. Architecting the new analytics systems in the cloud based on the experience of other large companies and AWS tools is important to the success of these new systems and capabilities.
Another important shift in business analytics is the move from batch processing and reporting to real-time event processing. Many use cases of fraud prevention or personal recommendations are more effective when running in real time on current events and data. In this session, the real-life use cases of Sony PlayStation are presented and the integration of event streaming tools are discussed.
Innovation is one of the concepts that is most misunderstood in large companies, but at the same time, it is critical for the growth and survival of these companies and their people. In this session, the chief architect of AWS, who joined Amazon after architecting the Netflix cloud platform, presents how to plan and execute innovative projects and services.
Building advanced analytics in the cloud doesn’t have to be done with new tools such as Amazon Athena or Amazon Kinesis. It can also be based on more familiar tools such as SAP. In this session, you can bridge the gap to the cloud by deploying your planned SAP HANA to the cloud for speed, cost, and performance.
The main concept to understand when starting to use the cloud is how to integrate many services based on API calls into a reliable, scalable, and flexible architecture. In this session, you learn about the mindset that you must have when building modern systems in the cloud.
Failure is one of the hardest concepts to accept in the enterprise world, but at the same time, the cloud is based on the “everything fails” concept. Knowing how to embrace failure and plan for it is one of the most important tools that you can learn in this session, presented by one of the most experienced engineers at AWS.
Machine learning is not data scientists building models. Machine learning is a mindset for an organization, and Amazon SageMaker can be a tool in the core of it. In this session, you can learn how to integrate your data; try, train, and tune models from built-in or AWS Marketplace algorithms or build-your-own algorithms; and learn how to use the models in production for customers and the business.
Part of the cloud evolution is the shift from VMs to containers. It takes time to convert legacy systems and benefit from the flexibility and agility of containers, but new systems based on ML provide a good opportunity to get started with Docker and Kubernetes.
Enterprises are built on workflows, and they often use a messaging bus to integrate systems and implement these workflows. In the (near) future, some of these workflows will be automated with AI running in the cloud. In this session, you learn how to architect and build a modern version of a messaging bus that can integrate with serverless and intelligent functions in the cloud.
The last concept that is confusing many experienced CIOs and IT managers is DevOps. Come learn how to break the silos of R&D, QA, and operations for better agility and reliability. In this session, you can get a glimpse into the future of your cloud operations center and the tools that your DevOps team will be using.
re:Invent is a great opportunity to stop the daily rat race we all live in and look strategically to the future of our businesses, our people, and ourselves. The future will be powered by AI, will improve people’s lives, and will be built on the most powerful capability of humans to change and evolve and on the ability of companies to transform by dreaming, designing, building, using, and operating innovative services. After all, the best way to predict the future is to build it.
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