Amazon Web Services User Group Edinburgh Welcome to AWS User Group Edinburgh #22 Webinar - Nimbo.sh and Machine Learning, No Expertise Required Presented by Miguel Jaques Nimbo.sh and John Walker, CirrusHQ Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh Proceedings 18.30 Open 18.35 - 19:20 Nimbo.sh Presentation 19:20 - 20:15 Machine Learning, No expertise Required 20.15 - 20.30 Q&A 20.35 Close Live chat available Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh John Walker ● Professional Services Team Lead @ CirrusHQ ● APN Ambassador ● Twitter : @zz9 ● 5x AWS Certified ● I also write about AWS at ● Technology Enthusiast https://blog.johnwalker.tech/ Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh 3 Amazon Web Services User Group Edinburgh Miguel Jaques ● PhD in Data Science @ University of Edinburgh ● Co-founder @ Nimbo ● Twitter : @migJaques ● Tech Lead @ Springbok.ai Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh 4 Amazon Web Services User Group Edinburgh Nimbo Dead-simple Machine learning on AWS Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh What is Nimbo? ● Nimbo is a simple command-line tool that allows you to run code on AWS as if you were running it locally. ● It abstracts away the complexity of AWS, allowing you to build, iterate, and deliver machine learning models faster than ever. Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh What does that even mean? Let's say you usually train a neural network on your computer like this: With Nimbo you can run this script on a GPU-enabled remote machine on AWS with: Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh Why did we build it? ● I did some part-time work for an company taking on ML projects for larger clients. ● I needed to have a neural network fine-tuned within a week in order to show results to the client. ● Manager suggested I use the company’s AWS infrastructure, but just getting up to speed on it would take a week (I ended up using a different service). ● I thought: “There should be a way to automate all of the annoying parts of AWS in order to focus on training the models instead of dealing with permissions, instance keys, security groups, environments, storage, etc.” Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh What’s the problem with AWS? ● AWS is a very flexible and powerful tool, but it takes many hours to understand and utilize it effectively. ● Just the EC2 API has hundreds of endpoints, with each endpoint having dozens of parameters. ● You then have to add the S3 SDK, the IAM SDK, the pricing SDK, etc. Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh What’s the problem with Amazon Web Services User Group Edinburgh ● In Nimbo, all we have is this: AWS ? Every command is intuitive and useful, with at most 2 arguments Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh What’s the problem with AWS? ● Other AWS solutions like SageMaker are cool if you want to use the web UI, but otherwise the CLI user experience isn’t very nice. ● We wanted a tool that took care of all the complexity of AWS, mimicking the experience of developing code and experiments on your own computer. ● Nimbo drastically simplifies your AWS workflow by taking care of instance, environment, data, and user management - no changes to your codebase needed. Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh How does it work? ● There is a nimbo-config.yml file that looks something like this: Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh How does it work? The command nimbo run "python -u train-mnist.py --epochs=10 --lr=3e-4" will: ● launch your instance (on-demand or spot instance) ● setup your environment (according to conda_env) ● sync your code to the instance ● pull your datasets from S3 into the instance ● run the job ● when the job is done: ○ save the results and logs back to S3 ○ delete the instance (or not) ● You can then do nimbo pull results or nimbo pull logs to get the job's results or nimbo-logs onto your computer. Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh How does it work? Nimbo also provides many useful commands to simplify IAM, key, security group, cost and data management. E.g.: ● nimbo spending <quantity> days/months ● nimbo ls-prices ● nimbo push datasets/results/logs ● nimbo pull datasets/results/logs ● nimbo ls-active ● nimbo ssh <instance_id> ● nimbo rm-instance <instance_id> ● nimbo launch-and-setup ● nimbo admin-setup Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh Enough talking, let’s do a demo! Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh Benefits (for the engineers and managers) ● Nimbo is the fastest way to prototype on AWS, allowing you to iterate faster. ● Nimbo has minimal setup, meaning you can be up and running in minutes. ● Nimbo simplifies onboarding, meaning a new engineer will be ready to start running jobs on your AWS infrastructure in minutes, without even having to know AWS! Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh Machine Learning on AWS No Expertise Required Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh Overview - Machine Learning Experience v No Experience Amazon provide a wide range of AI and Machine Learning solutions. These are typically split into 3 areas, AI Services, ML Services and Frameworks and Infrastructure. AI Services are AWS Services that can be used by builders with typically no Machine Learning experience. ML Services are AWS Services for performing Machine Learning on AWS. Frameworks and Infrastructure covers other supported Machine Learning use cases, such as directly using EC2 Instances and Container services for Machine Learning. Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh Overview - Machine Learning Experience v No Experience For the remainder of this presentation we will be focusing on the services that typically require no Machine Learning experience. These services are designed to be used by end users with no ML experience but still tap into the power and expertise of AWS. For example, the AWS Personalize service is based on the recommendation system used for Amazon.com where Amazon has gained real world experience in what works and what doesn’t. Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh Overview - Machine Learning Experience v No Experience The previous images were taken from the Data Science on AWS book https://www.amazon.co.uk/dp/B092 1MXC9S/ AWS and Deeplearning.ai AI have also released a new Data Science Specialisation https://www.coursera.org/specializa tions/practical-data-science Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh Health - Amazon HealthLake Amazon Healthlake is a specialised Data Lake product currently in preview for storing and analyzing patient data in a privacy focused way Features: ● Transform and understand patient data from sources such as prescriptions, procedure documents ● Identify Trends in data, and make predictions ● Transform data into industry standard data formats (such as FHIR - https://digital.nhs.uk/services/fhir-uk-core) Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh Health - Amazon Transcribe Medical Amazon Transcribe Medical is a specialism of the Transcribe Service that is use to convert Medical Speech, such as patient phone calls into transcripts Features: ● Transcribe medical related speech ● Serverless process, no data is stored as part of the process, you control the input and output ● Transcription expertise in primary care and specialisations such as neurology Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh Health - Amazon Comprehend Medical Amazon Comprehend Medical is a specialism of the Comprehend Service that is use to understand medical information in input documents, such as doctors notes Features: ● Understand and identify medical data and link to medical definitions ● Identify Patient Health Information and adhere to HIPAA and GDPR standards ● Identify relationships between information in the source documents Machine Learning on AWS – Presented by CirrusHQ and Nimbo.sh Amazon Web Services User Group Edinburgh Industrial - AWS Panorama + Appliance AWS Panorama is a combination of local appliance and SDK that can be used to do Computer Vision (CV) tasks locally, such as detecting quality control issues and site safety. The Panorama Appliance is a hardware device that can connect to existing cameras and be used for Features: ● Panorama Appliance (Coming Soon) for Computer Vision locally Computer Vision ● Panorama SDK for third parties to create cameras that run CV locally ● Predictions are done locally with high accuracy and low latency, with limited or no internet access needed Machine Learning on AWS – Presented