Amazon Web Services (AWS) offers a comprehensive suite of services for running data science workloads in the cloud at scale. These services include Amazon SageMaker, a pre-built suite of machine learning (ML) tools comprised of Textract, Polly, Lex, Comprehend, AWS Glue, Amazon EMR, and Amazon EC2.
In this 1-hour webinar, senior AWS instructor and consultant Michael Forrester discusses the benefits of transitioning data science workloads to AWS and walks through the steps involved in the migration process. By the end of this webinar, attendees will have a solid understanding of how to migrate their data science workloads to the AWS cloud.
In this 1-hour session, Michael will cover how to:
- Decide among the various options for developing, managing, and deploying data science workloads in AWS
- Transition on-premises notebooks to AWS
- Store data used in model development
- Obtain real-time and batch predictions
- General steps to earn the AWS Certified Machine Learning – Specialty certification
Audience
Decision makers and/or those in technical roles who want to understand the options and tools that AWS has related to moving Data Science Workloads to AWS.
Prerequisites
Basic understanding of ML/AI workloads, including sentiment analysis, probability forecasting, and a basic understanding of AWS and its core services.
Duration: 60 minutes
Cost: Free!
About the Presenter:
Michael Forrester is an Infrastructure Engineer with over 20 years of experience in the IT industry, specializing in all things DevOps. As an Amazon Authorized Instructor and trainer for almost a decade, he is deeply committed to cultivating DevOps expertise, aiming to optimize software delivery through efficiency, speed, and transparency. A big believer in people as the enabler, his extensive experience in DevOps also includes MLOps and AIOps, positioning him as a comprehensive authority in both traditional and emerging IT operations disciplines.
Get a first glimpse of Infrastructure as Code and Terraform. See a live demonstration that outlines the vital role Terraform plays in managing cloud resources. This is a precursor to our courses designed to extensively explore IaC and Terraform in conjunction with AWS and Azure.
Duration: 60 minutes
Cost: Free!
Cloud migrations present a unique set of problems that engineers may not have faced before. Moving data between data-centers has always been difficult, and the cloud represents a new iteration of the challenge. The possibilities offered by cloud-first software represents an opportunity to innovate but also brings a new set of tools that require mastery.
This session will present an AWS-centric view to moving data between sites and the challenges associated with it. It will cover requirements, tools, and evaluation criteria when planning your dataset migration to the cloud.
Topics in this half day class include different data movement scenarios, the tools available, and selection criteria. Specifically:
• Capture and transfer of different datasets: Event vs batch vs streaming Moving data from traditional databases and associated workloads into the cloud
• Moving log files and reports into the cloud
• Overview of AWS specific functions and services
This workshop is taught by Christopher Gambino, one of the founders of Calculated Systems. Prior to Calculated Systems, Chris lead project teams at Google and Hortonworks in deploying big data technologies such as Hadoop, Spark, BigQuery, and Kafka in AWS, GCP and Azure cloud environment Chris is a certified Google Cloud Architect and Data Engineer. Chris holds a bachelor’s degree in Biomedical Engineering and is a co-author of the big data ebook Apache NiFi for Dummies.
Target audience for Data Movement in AWS with Apache NiFi:
• Data Engineers, Engineering Managers, Architects, Data Scientists
• Technical professionals with data-oriented responsibilities (i.e. scientists needing more near real-time data, data engineers looking to build a new data pipeline)
In the wake of recent security breaches, that even fortune 500 companies have proven not to be immune to, we will take a look at different ways breaches can occur. Although we’ll focus on security in the cloud, since it introduces the concept of shared security responsibility between the provider and user, there will be valuable takeaways for anyone not yet in the cloud. We will also take a look at the lessons to be learned from the breaches, and the best practices for security that should be observed in a typical public-facing application deployment.
Join us as we discuss how open-source Apache Nifi can be used to easily consume cloud AWS Services. Featuring drag and drop interactions with many cloud capabilities it enables teams to quickly start handling their big data on the cloud. Both small agile and large enterprise teams can benefit from this easy to learn, rapid to implement approach to data processing.
• Strengths and Weaknesses of cloud technologies.
• Basics of open-source Apache Nifi.
• Practical example: Message Que > Enrichment > Database Entry.
Many enterprises are struggling with managing their cloud computing security posture and this can lead to exposing your enterprise to risks. Whether you’re an organization using AWS, GCP or Azure the one thing you have in common with other organizations is that the job of mitigating risks with Cloud Computing is never realized. “Playing defense in the cloud is much better than playing offense”
In this webinar, we will explore:
• “Top Ten Enterprise Cloud Risks” you need to avoid now.
• How to identify these Cloud Risks in your organization such as not understanding the “shared service model”, outdated security postures, poor development practices and understanding the current threat landscape.
• How your current culture, processes and even the current architecture can greatly impact your cloud computing security posture and your response to mitigating Cloud Risks.
• Best practices and techniques to identify vulnerabilities(Risks) and remedy those vulnerabilities(Risks) to minimize risk to your enterprise.
• Strategies to help you proactively identify and address high risk areasThis webinar will provide some ideas that can give your cloud oversight some key takeaways and action items to reduce your risk by playing offense in the cloud.
This half-day class introduces the audience to the specifics of designing and implementing cloud-native applications. In order to ensure smooth transition from on-premise application deployment to the cloud, both architects and developers need to learn new skills which depend on the choice of the target cloud platform. In this half-day class, we will review some of the aspects of going cloud- native where the cloud of choice is Pivotal Cloud Foundry, an Enterprise PaaS platform that runs on a variety of IaaS clouds, including AWS, Azure, and GCP.
Topics:
• Designing Applications for the Cloud
• Introduction to the Pivotal Cloud Foundry Platform
• Cloud Foundry CLI
• Pivotal Cloud Foundry Buildpacks
Cloud Computing can enable Agility when used properly. With an on-premise private cloud, you can combine the virtualized computing platform with your standardized technology stacks and practices. With a public cloud, you can access unlimited computing power on demand, when and how you need it, fully reaping Agile methodology benefits as well as achieving cost optimization. In this presentation, we will review the OpenStack private cloud platform as well as Amazon Web Services (AWS) as the public cloud platform. The use cases and fundamentals of public, private and hybrid clouds will be presented. Business and technology use cases for cloud around self-service, experimentation, and time-to-value will also be explored.
