Scaling up from a single engineer working off of their laptop to a dedicated team is an exciting milestone. But with growth comes growing pains. As you scale up your machine learning (ML) team, it's essential to leverage cloud services and tools just like you do for the rest of your development teams. Discover how to set up a data lake and implement it into an ML experiment workflow, how to prepare an end-to-end workflow to easily share the workload, and other tips for scaling your startup.
56:48Your dev team is working on Saturdays, your next fundraising round is on the horizon, your Machine Learning Engineers are still labeling data, your GPU is heating up under your desk, and your mind is
Learn how leading companies use Amazon SageMaker to improve efficiency, boost productivity, and lower costs.
Learn how to extract structured data from documents with speed, flexibility, and accuracy using Amazon Textract.
27:52A quick crash course on AWS Machine Learning and how to apply it to your startup.
41:54In the session, we'll show how FinTech startups can build, train, and deploy a custom machine learning model that helps you assess credit worthiness at scale.
58:26On this episode, we speak with a fast-growing FinTech startup to hear how by using Amazon Sagemaker as the core of their platform they have been able to scale to over 3 million users worldwide whilst
34:01Don’t let the idea of integrating artificial intelligence (AI) or machine learning (ML) into your workflow intimidate you.
47:42To help learn from those who’ve done it before, we’ve gathered AI/ML founders from some of the world’s top startups to give a peek behind the scenes into the secrets of their own success.
36:56Learn how to maximize resource utilization to find performance bottlenecks, and how to reduce overall training and inference costs.
31:59Dive deep into demonstrating SageMaker’s advanced features that help you train and iterate on your ML models faster.
Join us as we introduce Amazon SageMaker Studio, the first full integrated development environment (IDE) for ML that makes it easy to build, train, tune, debug, deploy, and monitor ML models at scale.
AI's got a firm grasp on the moment. Companies all over the world are turning to deep learning to optimize business. Get up to speed with this crash course in the basic concepts of deep learning.
19:26Learn how Skydio is using AWS to enable rapid development of disconnected intelligent systems. AWS services like Amazon Kinesis and Amazon S3 enable Skydio’s high-throughput data ingestion.
16:21Cloud robotics seems to be the new buzzword in the area of automation, but what does it really mean and how can it benefit your organization?
14:40Engineering Manager Carl Sverre discusses how SingleStore leverages AWS services in the ML workflow to compile and run Amazon SageMaker models as database functions against real-time data.
35:12How do the world’s largest brands, like Lego, British Airways, and Allstate Insurance, find ways to continually improve their customer experiences? They use Decibel, an award-winning, machine learning
26:18Watch this session to review AWS AV solutions for the toolchain including data ingest, management, labeling, simulation, and model training.
20:32What’s the key to the future of diversity and inclusion in the fashion industry? According to Lalaland cofounder and CTO Ugnius Rimsa, it’s AI. Learn how Lalaland uses Amazon services like Amazon Cogn
7:49Learn how to incorporate personalization into your apps – no ML experience required! We’ll demonstrate this by using the example of an app that recommends courses for students.
7:33Learn how you can create an app with machine learning to predict the outcome of a sports game. This is a great project for someone new to machine learning.
