Labelbox: Data Labeling Platform
Play • 47 min

Machine learning models require training data, and training data needs to be labeled. Raw images and text can be labeled using a training data platform like Labelbox. Labelbox is a system of labeling tools that enables a human workforce to create data that is ready to be consumed by machine learning training algorithms. The Labelbox team joins the show today to discuss training data and how to label it.

Sponsorship inquiries: sponsor@softwareengineeringdaily.com

The post Labelbox: Data Labeling Platform appeared first on Software Engineering Daily.

The Cloudcast
The Cloudcast
Cloudcast Media
Evolution of Commercial OSS
Joseph “JJ” Jacks (@asynchio, Founder/General Partner OSS Capital) talks about how Commercial OSS has evolved, coopetition with cloud providers, and what's next for Commercial OSS business models and communities.  *SHOW: *492 *SHOW SPONSOR LINKS:* * CloudZero - Cloud Cost Intelligence for Engineering Teams * BMC Wants to Know if your business is on its A-Game * BMC Autonomous Digital Enterprise * Datadog Security Monitoring Homepage - Modern Monitoring and Analytics * Try Datadog yourself by starting a free, 14-day trial today. Listeners of this podcast will also receive a free Datadog T-shirt. *CLOUD NEWS OF THE WEEK *- http://bit.ly/cloudcast-cnotw *CHECK OUT OUR NEW PODCAST - **"CLOUDCAST BASICS"* *SHOW NOTES:* * OSS Capital Partners and Advisors * Commercial Open-Source Software Company Index (COSSI) * OSS Capital to launch an ETF (with NASDAQ) of OSS Companies in Summer 2021 * Open Consensus - Data Driven Perspectives on Open Source Software * COSS Community / Open Core Summit  * The Kubernetes State of the Community (Eps.272) * Exploring the Business Side of Open Source Software (Eps.358) * Server Side Public License *Topic 1 *- Welcome to the show. For those that don’t already know you, tell us a little bit about your background, and some of the things you’re focused on today.  *Topic 2* - You’ve been tracking the commercialization of open-source projects for quite a while now. What big trends have you seen evolve over the last two decades (from Red Hat to MongoDB)  *Topic 3 *- Even in the face of new OSS-centric offerings from the cloud providers, we still continue to see companies getting funded. What is the sentiment in the VC-communities about what the new competitive landscape looks like? Are there new rules in the game?* * *Topic 4 *- We’ve recently seen MongoDB and Elastic changing their licensing model to SSPL. The stock of both companies continues to rise. Is what they are doing a short-term “fix” to a competitive threat, or a critical mistake? Does licensing need to evolve as a company matures?  *Topic 5* - Are there fundamental shifts in how OSS companies are created and eventually operationalized happening now?  *Topic 6* - Where do you see commercial OSS trending over the next 5 years, and what big changes need to happen to make those realities happen? *FEEDBACK?* * Email: show at thecloudcast dot net * Twitter: @thecloudcastnet
47 min
Google Cloud Platform Podcast
Google Cloud Platform Podcast
Google Cloud Platform
Cloud Spanner Revisited with Dilraj Kaur and Christoph Bussler
Mark Mirchandani and Stephanie Wong are back this week as we learn about all the new things happening with Google Cloud Spanner. Our guests this week, Dilraj Kaur and Christoph Bussler, describe Cloud Spanner as a fully managed relational database that boasts unlimited scaling and advanced consistency and availability. Unlimited scaling truly means unlimited, and Chris explains why Cloud Spanner offers this feature and how it’s making database design and development easier. Dilraj and Chris tell us all about the cool new features Spanner has developed, like generated columns and foreign keys, and how customer needs influenced these developments. Chris walks us through the process of using some of these new features, including how developers can monitor their database systems. Managed backups and multi-region configuration are additional recent additions to Cloud Spanner, and our guests explain how these are used by current enterprise clients. Dilraj and Chris explain the automatically managed features of Spanner versus the customer managed features and how people set up and manage database projects. We hear examples of companies using Cloud Spanner and how it has improved their businesses. Dilraj Kaur Dilraj Kaur is an Enterprise Customer Engineer with specialization in Data Management. She has been with Google for about 2.5 years and is based in Atlanta. Christoph Bussler As a Solutions Architect Chris is focusing on databases, data migration and data integration in enterprise customer settings. See his professional work and background on his website. Cool things of the week * New to Google Cloud? Here are a few free trainings to help you get started blog * Start your skills challenge today site * Service Directory is generally available: Simplify your service inventory blog Interview * Google Cloud Spanner site * GCP Podcast Episode 62: Cloud Spanner with Deepti Srivastava podcast * Using the Cloud Spanner Emulator docs * Cloud Spanner Ecosystem site * Cloud Spanner Qwiklabs site * Google Cloud Platform Community On Slack site * Creating and managing generated columns docs * WITH Clause docs * Foreign Keys docs * Numeric Data Type docs * Information schema docs * Overview of introspection tools docs * Backup and Restore docs * Multi-region configurations docs * ShareChat: Building a scalable data-driven social network for non-English speakers globally site * Blockchain.com: Streamlining infrastructure for the world’s most dynamic financial market site * What is Cloud Spanner? video What’s something cool you’re working on? Mark has been working on budgeting blog posts, including Protect your Google Cloud spending with budgets. Stephanie is working on her data center animation series
41 min
Python Bytes
Python Bytes
Michael Kennedy and Brian Okken
#222 Autocomplete with type annotations for AWS and boto3
Sponsored by Linode! pythonbytes.fm/linode Special guest: Greg Herrera YouTube live stream for viewers: Watch on YouTube Michael #1: boto type annotations * via Michael Lerner * boto3's services are created at runtime * IDEs aren't able to index its code in order to provide code completion or infer the type of these services or of the objects created by them. * Type systems cannot verify them * Even if it was able to do so, clients and service resources are created using a service agnostic factory method and are only identified by a string argument of that method. * boto3_type_annotations defines stand in classes for the clients, service resources, paginators, and waiters provided by boto3's services. Example with “bare” boto3: Example with annotated boto3: Brian #2: How to have your code reviewer appreciate you * By Michael Lynch * Suggested by Miłosz Bednarzak * Actual title “How to Make Your Code Reviewer Fall in Love with You” * but 🤮 * even has the words “your reviewer will literally fall in love with you.” * literally → figuratively, please * Topic is important though, here are some good tips: * Review your own code first * “Don’t just check for mistakes — imagine reading the code for the first time. What might confuse you?” * Write a clear change list description * “A good change list description explains what the change achieves, at a high level, and why you’re making this change.” * Narrowly scope changes * Separate functional and non-functional changes * This is tough, even for me, but important. * Need to fix something, and the formatting is a nightmare and you feel you must blacken it. Do those things in two separate merge requests. * Break up large change lists * A ton to write about. Maybe it deserves 2-3 merges instead of 1. * Respond graciously to critiques * It can feel like a personal attack, but hopefully it’s not. * Responding defensively will only make things works. Greg #3: REPODASH - Quality Metrics for Github repositories * by Laurence Molloy * Do you maintain a project codebase on Github? * Would you like to be able to show the maturity of your project at a glance? * Walk through the metrics available * Use-case Michael #4: Extra, extra, extra, extra, hear all about it * Python 3 Float Security Bug * Building Python 3 from source now :-/ It’s still Python 3.8.5 on Ubuntu with the kernel patch just today! (Linux 5.4.0-66 / Ubuntu 20.04.2) * Finally, I’m Dockering on my M1 mac via: * docker context create remotedocker --docker "host=ssh://user@server" * docker context use remotedocker * docker run -it ubuntu:latest bash now works as usual but remotely! * Why I keep complaining about merge thing on dependabot. Why!?! ;) * Anthony Shaw wrote a bot to help alleviate this a bit. More on that later. Brian #5: testcontainers-python * Suggested by Josh Peak * Why mock a database? Spin up a live one in a docker container. * “Python port for testcontainers-java that allows using docker containers for functional and integration testing. Testcontainers-python provides capabilities to spin up docker containers (such as a database, Selenium web browser, or any other container) for testing.” import sqlalchemy from testcontainers.mysql import MySqlContainer with MySqlContainer('mysql:5.7.32') as mysql: engine = sqlalchemy.create_engine(mysql.get_connection_url()) version, = engine.execute("select version()").fetchone() print(version) # 5.7.32 * The snippet above will spin up a MySql database in a container. The get_connection_url() convenience method returns a sqlalchemy compatible url we use to connect to the database and retrieve the database version. Greg #6: The Python Ecosystem is relentlessly improving price-performance every day * Python is reaching top-of-mind for more and more business decision-makers because their technology teams are delivering solutions to the business with unprecedented price-performance. * The business impact keeps getting better and better. * What seems like heavy adoption throughout the economy is still a relatively small-inroad compared to what we’ll see in the future. It’s like water rapidly collecting behind a weak dam. * It’s an exciting time to be in the Python world! Extras: Brian: * Firefox 86 enhances cookie protection * sites can save cookies. but can’t share between sites. * Firefox maintains separate cookie storage for each site. * Momentary exceptions allowed for some non-tracking cross-site cookie uses, such as popular third party login providers. Joke: 56 Funny Code Comments That People Actually Wrote: These are actually in a code base somewhere (a sampling): /* * Dear Maintainer * * Once you are done trying to ‘optimize’ this routine, * and you have realized what a terrible mistake that was, * please increment the following counter as a warning * to the next guy. * * total_hours_wasted_here = 73 */ // sometimes I believe compiler ignores all my comments // drunk, fix later // Magic. Do not touch. /*** Always returns true ***/ public boolean isAvailable() { return false; }
38 min
AWS TechChat
AWS TechChat
Shane Baldacchino
Episode 81 - re:Invent 2020 - AI/ML Special
In this episode of AWS TechChat, we close out our four parts of AWS re:Invent 2020 series with an AI/ML special. We cover Amazon Sagemaker, Amazon Kendra, Amazon Elastic MapReduce (EMR), Amazon QuickSight, and some brand new services. We talk about AWS HealthLake and how it makes sense of health data. AWS customers can use Kendra’s Google Drive connector to ingest and manage content from Google Docs and Google Slides. We introduce AWS Panorama which will help improve your operations with computer vision at the edge. We continue with a raft of new Amazon SageMaker updates: • Amazon SageMaker Feature Store - A fully managed repository for machine learning features • Amazon SageMaker Clarify - Bias Detection and Explainability • Amazon SageMaker Debugger - Optimize ML models with real-time monitoring of training metrics and system resources • Amazon SageMaker Model Monitor - Detect drift in model quality, model bias, and feature importance • Amazon SageMaker Pipelines - First purpose-built CI/CD service for machine learning • Amazon SageMaker Jumpstart - Simplifies Access to Pre-built Models and Machine Learning Solutions Before wrapping out, we share two more AI/ML updates - Amazon EMR Studio is the integrated development environment (IDE) for applications written in R, Python, Scala, PySpark, and Jupyter notebooks now gives you the option to deploy on Amazon Elastic Kubernetes Service (EKS). Amazon QuickSight allows you to ask Natural Language Query (NLQ) about your data and get answers in seconds. Speakers: Shane Baldacchino - Edge Specialist Solutions Architect, ANZ, AWS Shai Perednik - Solutions Architect, AWS Pallavi Nargund - Solutions Architect, AWS
1 hr 4 min
More episodes
Search
Clear search
Close search
Google apps
Main menu