Are you ready for machine learning? Do you have a plan? In this article, Atakan Cetinsoy from BigML goes over six things that every organization needs to be aware of when they’re devising their own machine learning strategy.
Prepare to be dazzled by what this small team of academics managed to achieve. Fast.ai is an organization that aspires to make machine learning accessible to everyone. Their most crowning achievement? Well, not much. Just outperforming the AI giant of Google!
How can developers learn to utilize machine learning in their DevOps practice? In this article, Prasanthi Korada goes over some basic approaches that can help developers apply cutting edge tech like machine learning to their everyday work.
There are a number of issues that arise when data scientists and ML researchers meet DevOps to try to deploy, audit, and maintain state-of-the-art AI models in a production and commercial environment. Peter Zhokhov and Arshak Navruzyan discuss a new open source software tool called Studio.ML, which offers a number of solutions to this problem.
With artificial intelligence and machine learning becoming increasingly relevant for modern enterprises, many companies might be feeling the pressure to invest in an AI strategy, before fully understanding what they are aiming to achieve. In this session, Sumanas Sarma and Rob Hinds explain how you can go from theory to production in adopting machine learning solutions.
Amazon has been using predictive models for decades. Now, they want to put machine learning in the hands of every developer with their AWS AI division. In this article, Cyrus Vahid, an AI specialist from AWS, explains some basic models for deep learning and goes over how Amazon has a service for every use case.
TensorFlow 1.9 is here! So what does this latest update mean for the popular machine learning project? For starters, there’s an improved tf.keras beginner’s guide. For everyone else, there’s eager execution, improved GRU and LSTM implementation, and gradient boosted trees estimators.
Who doesn’t wish we could snap our fingers and have the tough, labor-intensive part of coding be finished? In this article, Daniel Kroening imagines a future where AI codes for us, and we get to do all the fun parts. The self-coding future may not be very far away. Fact or fiction?
Machine learning inevitably adds black boxes to automated systems and there is clearly an ethical debate about the acceptability of appropriating ML for a number of uses. The risks can be mitigated with five straightforward principles.
The future of digital technology is here. 2017 saw incredible progression for things like data science, artificial intelligence, and machine learning. Where will they go in 2018? In this article, Maria Thomas explores the future of data science and how well it can be combined with predictive analysis.
Every year, Stack Overflow surveys the state of the developer community. What trends, tools, and technologies did they find? Julia Silge, a data scientist at Stack Overflow, dives deep into the data to show the most loved technologies of 2018.
It’s that time of year again! The 2018 Packt Skill Up Report is out and loud! With 8000 respondents and 6 focus tracks, it gives a detailed picture of the tools and trends that define how software developers work today. In this article, we take a closer look at web development: What technologies are currently number one and what does the future hold?
Wherever you are, you can livestream the opening keynote for the Machine Learning Conference and join Xander Steenbrugge as he discusses “Cracking open the black box of neural networks”.
An in-memory computing platform with continuous learning capabilities enables a range of real-time decision making use cases. What might some of these cases be and how will they affect the future of machine learning and deep learning?