Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular, image, and text. Starting today, the SageMaker LightGBM algorithm offers distributed training using the Dask framework for both tabular classification and regression tasks. The supported data format can be either CSV or Parquet.

Lately, I've been meeting a lot of people who are interested in making a career shift into data science. One of the first things they always ask me is, "what does a typical day look like?". I've seen a lot of articles that give an overview of the skills and tools Data Scientists use, but I don't see very many that provide real examples of daily tasks. While every day is different, these tasks represent a typical day for me as a Senior Data Scientist at a large financial institution. I typically start my work day around 8:30 am after I roll out of bed at 8:20.

Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular, image, and text. This post is the second in a series on the new built-in algorithms in SageMaker. In the first post, we showed how SageMaker provides a built-in algorithm for image classification.

Dr. Vivek Madan is an Applied Scientist with the Amazon SageMaker JumpStart team. He got his PhD from University of Illinois at Urbana-Champaign and was a Post Doctoral Researcher at Georgia Tech. He is an active researcher in machine learning and algorithm design and has published papers in EMNLP, ICLR, COLT, FOCS and SODA conferences. João Moura is an AI/ML Specialist Solutions Architect at Amazon Web Services. He is mostly focused on NLP use-cases and helping customers optimize deep learning model training and deployment. He is also an active proponent of low-code ML solutions and ML-specialized hardware. Dr. Ashish Khetan is a Senior Applied Scientist with Amazon SageMaker built-in algorithms and helps develop machine learning algorithms. He got his PhD from University of Illinois Urbana Champaign. He is an active researcher in machine learning and statistical inference and has published many papers in NeurIPS, ICML, ICLR, JMLR, ACL, and EMNLP conferences.

Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular, image, and text. Starting today, SageMaker provides a new built-in algorithm for image classification: Image Classification – TensorFlow. It is a supervised learning algorithm that supports transfer learning for many pre-trained models available in TensorFlow Hub.

With the ability to revolutionize everything from self-driving cars to robotic surgeons, artificial intelligence is on the cutting edge of tech innovation. Two of the most widely recognized AI services are Microsoft's Azure Machine Learning and IBM's Watson. Both boast impressive functionality, but which one should you choose for your business? Azure Machine Learning is a cloud-based service that allows data scientists or developers to train, build and deploy ML models. It has a rich set of tools that makes it easy to create predictive analytics solutions. This service can be used to build predictive models using a variety of ML algorithms, including regression, classification and clustering.

When you start looking at ML outside of your local notebook or environment, you start getting into the world of Cloud Computing. Providers such as AWS, Azure, and GCP are offering an incredible suite of ML services in their respective Clouds that can help you take ML to a production grade scale. What's even more incredible is ML is slowly being democratized for all programmers. As ML has expanded a lot of the theory and knowledge behind the algorithms have been abstracted out into AutoML services that enable developers with no ML experience to launch applications powered by cutting edge AI. These Auto-AI services cover a variety of different ML fields such as NLP, Computer Vision, Time-Series Forecasting, and more.

Amazon SageMaker is a fully-managed, modular machine learning (ML) service that enables developers and data scientists to easily build, train, and deploy models at any scale. With a choice of using built-in algorithms, bringing your own, or choosing from algorithms available in AWS Marketplace, it's never been easier and faster to get ML models from experimentation to scale-out production. One of the key benefits of Amazon SageMaker is that it frees you of any infrastructure management, no matter the scale you're working at. For instance, instead of having to set up and manage complex training clusters, you simply tell Amazon SageMaker which Amazon Elastic Compute Cloud (EC2) instance type to use, and how many you need: the appropriate instances are then created on-demand, configured, and terminated automatically once the training job is complete. As customers have quickly understood, this means that they will never pay for idle training instances, a simple way to keep costs under control.

Image classification and object detection in images are hot topics these days, thanks to a combination of improvements in algorithms, datasets, frameworks, and hardware. These improvements democratized the technology and gave us the ingredients for creating our own solution for image classification. The state-of-the-art technologies for image classification and object detection are based on deep learning (DL). DL is a subarea of machine learning (ML) that is focused on algorithms for handling neural networks (NN) with many layers, or deep neural networks. ML, in turn, is a subarea of artificial intelligence (AI), a computer-science discipline.