Machine Learning (ML) powers an increasing number of the applications and services that we use daily. For organizations who are beginning to leverage datasets to generate business insights -- the next step after you've developed and trained your model is deploying the model to use in a production scenario. That could mean integration directly within an application or website, or it may mean making the model available as a service. As ML continues to mature the emphasis starts to shift from development towards deployment. You need to transition from developing models to real world production scenarios that are concerned with issues of inference performance, scaling, load balancing, training time, reproducibility and visibility.
You have worked for weeks on building your machine learning system and the performance is not something you are satisfied with. You think of multiple ways to improve your algorithm's performance, viz, collect more data, add more hidden units, add more layers, change the network architecture, change the basic algorithm etc. But which one of these will give the best improvement on your system? You can either try them all, invest a lot of time and find out what works for you. You can use the following tips from Ng's experience.
Personalized learning, which tailors educational content to the unique needs of individual students, has become a huge component of K–12 education. A growing number of college educators are embracing the trend, taking advantage of data analytics and artificial intelligence to deliver just-right, just-in-time learning to their students. Data-driven insights are becoming integral to business and financial decision-making by institutional leaders, and educators are quickly finding ways to leverage analytics to increase student retention. Applying data analytics to adaptive learning programs is proving to be another smart application. In adaptive learning, educators collect data on various aspects of student performance -- from engagement with course content to exam performance -- and tailor material to each student's knowledge level and ideal learning style.
Machine learning (ML) powers an increasing number of the applications and services that we use daily. For organizations who are beginning to leverage datasets to generate business insights, the next step after you've developed and trained your model is deploying the model to use in a production scenario. That could mean integration directly within an application or website, or it may mean making the model available as a service. As ML continues to mature, the emphasis starts to shift from development towards deployment, you need to transition from developing models to real-world production scenarios that are concerned with issues of inference performance, scaling, load balancing, training time, reproducibility, and visibility. In previous posts, we've explored the ability to save and load trained models with TensorFlow that allow them to be served for inference.
In our webinar "Optimizing Machine Learning with TensorFlow" we gave an overview of some of the impressive optimizations Intel has made to TensorFlow when using their hardware. You can find a link to the archived video here. During the webinar, Mohammad Ashraf Bhuiyan, Senior Software Engineer in Intel's Artificial Intelligence Group, and myself spoke about some of the common use cases that require optimization as well as benchmarks demonstrating order-of-magnitude speed improvements when running on Intel hardware. TensorFlow, Google's library for machine learning (ML), has become the most popular machine learning library in a fast-growing ecosystem. This library has over 77k stars on GitHub and is widely used in a growing number of business critical applications.
These new applications require a new way of thinking about the development process. Traditional application development has been enhanced by the idea of DevOps, which forces operational considerations into development time, execution, and process. In this tutorial, we outline a "cognitive DevOps" process that refines and adapts the best parts of DevOps for new cognitive applications. Specifically, we cover applying DevOps to the training process of cognitive systems including training data, modeling, and performance evaluation. A cognitive or artificial intelligence (AI) system fundamentally exhibits capabilities such as understanding, reasoning, and learning from data.
One of the most amazing things about Python's scikit-learn library is that is has a 4-step modeling pattern that makes it easy to code a machine learning classifier. While this tutorial uses a classifier called Logistic Regression, the coding process in this tutorial applies to other classifiers in sklearn (Decision Tree, K-Nearest Neighbors etc). In this tutorial, we use Logistic Regression to predict digit labels based on images. The image above shows a bunch of training digits (observations) from the MNIST dataset whose category membership is known (labels 0–9). After training a model with logistic regression, it can be used to predict an image label (labels 0–9) given an image.
Enhancing a model performance can be challenging at times. I'm sure, a lot of you would agree with me if you've found yourself stuck in a similar situation. You try all the strategies and algorithms that you've learnt. Yet, you fail at improving the accuracy of your model. You feel helpless and stuck.
Nearly every industry today is swimming in data, and the floodgates are not closing any time soon. Expert projections suggest a 4,300% increase in annual data production that will create 35 zettabytes by 2020. As the acceleration of data analytics continues, more businesses are realizing the necessity for an efficiency of increased automation across their organizations. In fact, nearly three-quarters of business leaders and employees believe at least some part of their job could be automated. Yet, there's also an ongoing debate around the linear computational ability of machines, which inherently lacks business logic.
HPE announced new purpose-built platforms and services capabilities to help companies simplify the adoption of Artificial Intelligence, with an initial focus on a key subset of AI known as deep learning. Inspired by the human brain, deep learning is typically implemented for challenging tasks such as image and facial recognition, image classification and voice recognition. To take advantage of deep learning, enterprises need a high performance compute infrastructure to build and train learning models that can manage large volumes of data to recognize patterns in audio, images, videos, text and sensor data. Many organizations lack several integral requirements to implement deep learning, including expertise and resources; sophisticated and tailored hardware and software infrastructure; and the integration capabilities required to assimilate different pieces of hardware and software to scale AI systems. Based on the HPE Apollo 6500 system in collaboration with Bright Computing to enable rapid deep learning application development, this solution includes pre-configured deep learning software frameworks, libraries, automated software updates and cluster management optimized for deep learning and supports NVIDIA Tesla V100 GPUs.