This article was published as a part of the Data Science Blogathon. Amazon Sagemaker is arguably the most powerful, feature-rich, and fully managed machine learning service developed by Amazon. From creating your own labeled datasets to deploying and monitoring the models on production, Sagemaker is equipped to do everything. It can also provide an integrated Jupyter notebook instance for easy access to your data for exploration and analysis, so you don't have to fiddle around with server configuration. Sagemaker supports bring-your-own-algorithms and frameworks, which offer flexible distributed training options that adjust to your specific workflows.

Amazon SageMaker Autopilot automatically builds, trains, and tunes the best machine learning (ML) models based on your data, while allowing you to maintain full control and visibility. We have recently announced support for time series data in Autopilot. You can use Autopilot to tackle regression and classification tasks on time series data, or sequence data in general. Time series data is a special type of sequence data where data points are collected at even time intervals. Manually preparing the data, selecting the right ML model, and optimizing its parameters is a complex task, even for an expert practitioner.

Our purpose in this article is to show you how to use the fully managed AI and machine learning services from Amazon so you won't have to manage your own infrastructure for your AI and machine learning pipelines. With Amazon SageMaker Autopilot and Amazon Comprehend, we'll explore two services that deliver automated machine learning, both designed for users who want to build powerful predictive models from their datasets quickly. It is very easy and cost-effective to establish a baseline model performance with both SageMaker Autopilot and Comprehend.

Qlik has deepened its work with Amazon Web Services with the release of an advanced analytics connector for Amazon SageMaker and integration with Amazon SageMaker Autopilot. These integrations increase the breadth of advanced analytics capabilities already available in Qlik Cloud, providing seamless integration to Amazon's advanced machine learning capabilities all via Qlik's Active Intelligence Platform. "Advanced machine learning capabilities to drive more predictive and prescriptive analytics is an important part of our vision for Active Intelligence, where businesses can seize every business moment," says Qlik chief product officer, James Fisher. "Now users of Amazon SageMaker can take advantage of their offerings directly in our SaaS platform, opening up even more opportunities," he says. "This capability supplements what is available today via Qlik AutoML, and illustrates our ongoing commitment to being a fully open, independent platform for data."

Bangalore – Qlik deepened its work with Amazon Web Services (AWS) today with the release of an advanced analytics connector for Amazon SageMaker and integration with Amazon SageMaker Autopilot. These integrations increase the breadth of advanced analytics capabilities already available in Qlik Cloud, providing seamless integration to Amazon's advanced machine learning capabilities all via Qlik's Active Intelligence Platform . "Advanced machine learning capabilities to drive more predictive and prescriptive analytics is an important part of our vision for Active Intelligence, where businesses can seize every business moment. Now users of Amazon SageMaker can take advantage of their offerings directly in our SaaS platform, opening up even more opportunities," said James Fisher, Chief Product Officer at Qlik. "This capability supplements what is available today via Qlik AutoML, and illustrates our ongoing commitment to being a fully open, independent platform for data." The new Amazon SageMaker connector is part of Qlik's Advanced Analytics Integration strategy, offering native, engine-level integrations built directly into Qlik's cloud analytics. It enables direct data exchange between Qlik's Analytics Engine and Amazon SageMaker to deliver a set of predictive data and updated calculations in real time as the user interacts with the data.

Amazon SageMaker Autopilot automatically trains and tunes the best machine learning (ML) models for classification or regression problems while allowing you to maintain full control and visibility. This not only allows data analysts, developers, and data scientists to train, tune, and deploy models with little to no code, but you can also review a generated notebook that outlines all the steps that Autopilot took to generate the model. In some cases, you might also want to customize pipelines generated by Autopilot with your own custom components. This post shows you how to create and use models with Autopilot in a couple of clicks, then outlines how to adapt the SageMaker Autopilot generated code with your own feature selectors and custom transformers to add domain-specific features. We also use the dry run capability of Autopilot, in which Autopilot only generates code for data preprocessors, algorithms, and algorithm parameter settings.

Code is only a small component of any machine learning solution. The goal of managed machine learning services is to centralize these components into a single packaged solution. But not all managed machine learning services are fully comparable. Tools like AWS Sagemaker help you manage the complexity inherent in any machine learning solution, but still expect you to have engineers on your team who can build and understand the code. These tools focus more on the compute layer.

Today, we're extremely happy to launch Amazon SageMaker Autopilot to automatically create the best classification and regression machine learning models, while allowing full control and visibility. In 1959, Arthur Samuel defined machine learning as the ability for computers to learn without being explicitly programmed. In practice, this means finding an algorithm than can extract patterns from an existing data set, and use these patterns to build a predictive model that will generalize well to new data. Since then, lots of machine learning algorithms have been invented, giving scientists and engineers plenty of options to choose from, and helping them build amazing applications. However, this abundance of algorithms also creates a difficulty: which one should you pick?

AWS launched a host of new tools and capabilities for Amazon SageMaker, AWS' cloud platform for creating and deploying machine learning models; drawing the most notice was Amazon SageMaker Studio, a web-based integrated development platform (IDE) . In addition to SageMaker Studio, the IDE for platform for building, using, and monitoring machine learning models, the other new AWS products aim to make it easier for non-expert developers to create models and to make them more explainable. During a keynote presentation at the AWS re:Invent 2019 conference here Tuesday, AWS CEO Andy Jassy described five other new SageMaker tools: Experiments, Model Monitor, Autopilot, Notebooks, and Debugger. "SageMaker Studio along with SageMaker Experiments, SageMaker Model Monitor, SageMaker Autopilot, and Sagemaker Debugger collectively add lots more lifecycle capabilities for the full ML (machine learning) lifecycle and to support teams," said Mike Gualtieri, an analyst at Forrester. SageMaker Studio, Jassy claimed, is a "fully-integrated development environment for machine learning." The new platform pulls together all of SageMaker's capabilities, along with code, notebooks, and datasets, into one environment.