Thu, Aug 4, 9:00 AM PDT: Join us for the twenty-second Google Open Source Live event in our series; “Machine Learning Day”! Google Machine Learning experts will share updates on everything from To

The latest edition of our flagship learning series on everything in and about data analytics is sure to excite your minds, be prepared for the DataHour on Building your First Chatbot using Open Source Tools. The session will be hosted by Dr. Rachael Tatman- Staff Developer Advocate at Rasa, the world's leading conversational AI platform, that enables enterprises to revamp customer experience with cutting-edge open-source machine learning implementations. In this session, you will be led on an engaging journey of using the open-source platform Rasa, and the lecture will be helmed by an ex-Googler and an instructor at the University of Michigan, Dr. Rachael Tatman. The session is for both freshers and professionals alike who would like to design chatbots to improve the CX for their organisations or simply get hands-on experience with open source tools like Rasa. Chatbots have been around for some time.

Google Colab is a popular browser based environment for executing Python code on hosted Jupyter notebooks and training models for machine learning, including free access to GPUs! It is a great platform for data scientists and machine learning (ML) engineers for learning and quickly developing ML models in Python. Redis is an in-memory open source database that is increasingly being used in machine learning - from caching, messaging and fast data ingest, to semantic search and online feature stores. In fact, NoSQL databases - and specifically Redis - was named by Ben Weber, Director of Applied Data Science at Zynga as one of the 8 new tools he learned as a data scientist in 2020. Because of the increasing use of Redis for data science and machine learning, it is very handy to be able to run Redis directly from your Google Colab notebook!

There is only one Data Cloud. Snowflake's founders started from scratch and designed a data platform built for the cloud that is effective, affordable, and accessible to all data users. They engineered Snowflake to power the Data Cloud, where thousands of organizations unlock the value of their data with near-unlimited scale, concurrency, and performance. This is our vision: a world with endless insights to tackle the challenges and opportunities of today and reveal the possibilities of tomorrow. Snowfake's future success depends upon making our users: data scientists, app developers, and data engineers successful.

Amazon Web Services has partnered with education technology company DeepLearning.AI to offer a new specialization to help data professionals quickly master the essentials of machine learning and efficiently deploy data science projects at scale in the AWS cloud. The three-course Practical Data Science Specialization with Amazon SageMaker, AWS' fully managed machine learning (ML) service, is available through Coursera's education platform. The new, massive open online course (MOOC) addresses a critical factor to success with ML: growing the talent pool and helping more people become ML practitioners, according to Bratin Saha, vice president of machine learning services for AWS. "At Amazon, our goal is to train every developer we hire on machine learning," said Saha, who announced the new specialization during the opening keynote address for today's virtual AWS Machine Learning Summit. "In fact, machine learning courses are now mandatory for any engineer joining Amazon, and we want to make training accessible to even more developers."

Chris Fregly is a Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. He is also the founder of the Advanced Spark, TensorFlow, and KubeFlow Meetup Series based in San Francisco. Chris regularly speaks at AI and Machine Learning conferences across the world including the O'Reilly AI, Strata, and Velocity Conferences. Previously, Chris was Founder at PipelineAI where he worked with many AI-first startups and enterprises to continuously deploy ML/AI Pipelines using Apache Spark ML, Kubernetes, TensorFlow, Kubeflow, Amazon EKS, and Amazon SageMaker. He is also the author of the O'Reilly Online Training Series "High Performance TensorFlow in Production with GPUs" Antje Barth is a Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in Düsseldorf, Germany.

I can say I've been working across the machine learning pipeline in all the different roles… And as you mentioned, a lot of these roles are very [unintelligible 00:05:29.20] When people talk about data scientist, and data engineering roles in machine learning research, or machine learning engineering rather, they try to use these Venn diagrams… And I've found that it is not very descriptive. For example, if you're working on the data science side of the pipeline, you're focusing much more on the statistics, on developing novel algorithms or models that would help your business or your company to get [unintelligible 00:06:03.07] But then you will probably have/need some software engineering skills as well, to take that into a production format with the rest of your dev environment or your dev team… Whereas when you're working on the data engineering side of things, you're focusing much more on all the processes that are [unintelligible 00:06:23.24] And then the machine learning engineer role is basically the one that binds it all together.

Speaker 1) Kaz Sato / Staff Developer Advocate, Google Cloud, Google Inc. Kaz Sato is Staff Developer Advocate at Google Cloud. For machine learning and data analytics products, such as TensorFlow, Cloud AI and BigQuery, Kaz has been invited as a speaker at major events including Google Cloud Next, Google I/O, Strata, NVIDIA GTC and etc. He is also interested in hardwares and IoT, and has been hosting FPGA meetups since 2013. Speaker 2) Emma Haruka Iwao / Developer Advocate, Google Cloud, Google Inc. Bio: Emma is a developer advocate for Google Cloud Platform, focusing on application developers experience and high performance computing. She has been a C developer for more than 15 years and worked on embedded systems and the Chromium Project.

Join IBM November 1 at 21st Cloud Expo at the Santa Clara Convention Center in Santa Clara, CA, and learn how IBM Watson can bring cognitive services and AI to intelligent, unmanned systems. In this session we will build a chatbot powered by IBM Watson, connect it to third-party APIs, and share best practices of chatbots co-existing with humans. Cognitive analysis impacts today's systems with unparalleled ability that were previously available only to manned, back-end operations. Thanks to cloud processing, IBM Watson can bring cognitive services and AI to intelligent, unmanned systems. Imagine a robot vacuum that becomes your personal assistant that knows everything and can respond to your emotions and verbal commands!

Join IBM November 1 at 21st Cloud Expo at the Santa Clara Convention Center in Santa Clara, CA, and learn how IBM Watson can bring cognitive services and AI to intelligent, unmanned systems. In this session we will build a chatbot powered by IBM Watson, connect it to third-party APIs, and share best practices of chatbots co-existing with humans. Cognitive analysis impacts today's systems with unparalleled ability that were previously available only to manned, back-end operations. Thanks to cloud processing, IBM Watson can bring cognitive services and AI to intelligent, unmanned systems. Imagine a robot vacuum that becomes your personal assistant that knows everything and can respond to your emotions and verbal commands!