In this video, deeplizard discusses GPU support for TensorFlow and the integrated Keras API and how to get your code running with a GPU.
Databricks just livestreamed this tech talk earlier today.
Developers and data scientists around the world have developed tens of thousands of open source projects to help track, understand, and address the spread of COVID-19. Given the sheer volume, finding a project to contribute to can prove challenging. To make this easier, we built a recommendation system to highlight projects based off of inputted programming languages and keywords.
This talk will go through the full cycle of implementing this system: gathering data, building/tracking models, deploying the model, and creating a UI to utilize the model.
Using data for machine learning and analytics can potentially expose private data.
How can we leverage data while ensuring that private information remains private?
In this video, learn how differential privacy can be used to preserve privacy and get a demo on how you can use newly released open source system, WhiteNoise, to put DP into your applications.
In the second part of this two-part video segment, Rohit Nayak dives deeper into the new capabilities for Azure SQL Database for Private Endpoints.
Be sure to watch the first episode to understand what Private Endpoint for Azure SQL Database.
- [01:09] Set-up
- [02:00] Demo: How to connect from Public IP Address to Private Endpoint
- [08:36] Connecting Client VM to SQL Database with a Private Endpoint
- [10:45] How to check your deployment succeeded
- [14:08] Now that you created a Private IP Address, can you login to the IP Address itself?
- [15:50] Double-checking on another machine
- [16:30] 3 key takeaways
Developing a new drug is an expensive and time consuming process.
Here’s an interesting blog post about how deep learning can speed up the process and lower the costs.
The cost of developing a new drug and bringing it to market is anywhere between $1.3 billion USD to nearly $2.9 billion USD depending on who you ask. Many of the low-hanging fruit are already picked, and the changing health landscape of modern society compounds the challenge. Our aging society is now more heavily affected by heart disease, dementia, and cancer than our progenitors were.
Databricks just posted part 3 of a 3 part online technical workshop series on Managing the Complete Machine Learning Lifecycle with MLflow. If you’re interested in learning about machine learning and MLflow, this workshop series is for you!
This workshop is an introduction to MLflow. Machine Learning (ML) development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models.
To solve these challenges, MLflow, an open source project, simplifies the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, encapsulate models that can be used with many existing tools, and central repository to share models, accelerating the ML lifecycle for organizations of any size.
The following is a guest post by Ashley Halsey.
While artificial intelligence (AI) has taken off dramatically over the last few years and can now pretty much be found in any industry or niche of business from scientific applications to causal smartphone apps that can do practically anything you care to name.
However, how has it affected one of the most important aspects of business; we are, of course, talking about driving and managing customers. How is AI affecting the modern-day customer experience, and what do you need to know about it?
Today, I’m going to share everything you need to know.
Is AI Actually Being Used?
Mark Swarly, a tech blogger at Writinity and LastMinuteWriting, shares; “While there is an obvious increase in the use and implementation of AI among businesses, you may be interested to know that more than 45% of businesses throughout the world (retailers) have introduced it already, which is huge. The same study discovered that around 85% of all customer interactions would be handled by machines and AI by the end of this year.”
This, in turn, means that the global economy as we know it today will double by 2035 as a direct result of what AI is bringing to the table. While we are still in the early stages of the technology and seeing what it can offer, it’s already changing the game of business as we know it forever, and there’s no denying it’s here to stay.
Chatbots, Chatbots, Chatbots
I have to start with the most important change that AI has bought to the mainstream customer experience, and that’s chatbots. You will have seen them when you go onto a website and see the little chatbot in the corner pop up. Businesses on Facebook use them, websites use them, and you can find them in all industries.
The thing about the modern-day customer experience is the fact that anybody could try to access and connect with your business at any time of day, 24/7, and it’s important for your business to recognise this.
In the old days, if someone wanted to ask a question or find out more about your business, they’d have to wait till someone was online, phone up, or send an email, probably during office hours. However, this demand for instant communication is evident, which is why chatbots have changed the game.
“Chatbots are automatic, can handle endless customers at the same time, and provide key information which is customised to basically whatever you want it to say. Every good business in 2020 and beyond will need chatbots,” suggests Amanda Holder, an IT expert at Draft Beyond and Researchpapersuk.
The age of voice search is well and truly underway, and this has been mainly due to the introduction of voice and personal assistants. Whether you’re referring to the apps on our phones, such as Siri and Google Assistant, or physical devices that include models such as Google Home and Amazon Alexa, these are still, in a way, chatbots, but they are also so much more.
These assistants are much more human, come with their own personalities and can provide different answers to the same topic. Since they are AI, they can also learn to provide a better service. In the context of business, this is allowing people to access information and products quicker, contact businesses faster, and overall speeding up and optimising the entire customer experience.
Everything Faster, Better Predictions
AI is capable of handling insane amounts of data and processing it very quickly; far quicker than any human being, or even a group of human beings would be able to. When fed large amounts of data or gathering information on a user over a longer period of time, the AI can create a personalised service for the customer that gives them a better experience than it was possible to ever before.
The more personalised an experience is, and the more tailored it is to suit the needs of the individual, thus improving their customer experience. They’ll be much more inclined to see the businesses and products they’ll actually be interested in, and therefore much more likely to make a sale and to interact.