Frustrated with one-off articles and too much math? Take the Next Step and Get Tutorial-Based Playbooks that will Guide You to a Specific Result. Welcome to: the Machine Learning Mastery EBook Catalog.

Data trust is on every data team’s mind, but how do you create and maintain this elusive, precious asset? We surveyed 200 data teams with Wakefield Research to benchmark data product adoption rates and how data leaders can improve them.

The modern data stack was already bursting at the seams long before generative AI became the talk of the town due to steep increase in the number of data sources, data consumers, and use cases. As the world fixates on AI-driven data outcomes, there is an immense urgency to modernize the underlying data infrastructure to meet the data needs of the business in a manner that is trustworthy, reliable, timely and, of course, accurate. In addition, there is already a rallying cry for simplification and complexity reduction. To address these needs, it is imperative to reduce data stack complexity, which further helps increase reliability of the infrastructure components.

It’s yours, free.The success of any application or system depends on the architecture pattern you use. This report examines common software architecture patterns, explaining how each works, the pattern’s benefits and considerations, and the circumstances and conditions it was designed to address. If you’d like a quick guide to choosing the right pattern for your project, Software Architecture Patterns is extremely useful.

This ebook highlights the most common issues data engineering teams face trying to operationalize data for analytics, and offers practical tips and best practices on how to avoid them.

If you’re looking for a feature store or feature platform for machine learning and don’t know where to start, you’re in the right place! This guide will help you frame your vendor evaluation to ensure you choose the best solution for your organization’s needs.

Empower any data user at any skill level to harness the potential of Databricks and modernize your process for developing, deploying, and managing data pipelines.

In this new report, Mike Loukides dives into O’Reilly platform data to explore the technology and tools O’Reilly’s 2.8 million users are actively employing to turn big ideas into real-world products—and to predict what’s in store in the future.

In this eBook, you’ll learn how low-code for lakehouse can enable data engineers to: Visually build and tune data pipelines into well-engineered Spark or PySpark code; Directly store code in Git while leveraging testing and CI/CD best practices; Collaborate on multiple data pipelines within each level of data quality.

Get business teams started with a self-serve platform to build data products with speed, standards & quality on the Lakehouse.

Applying knowledge in the right context is the most powerful lever businesses can use to become agile, creative, and resilient. Knowledge graphs offer unparalleled automation and visibility into processes, products, and customers. They help you anticipate downstream effects, understand complex data relationships, and respond to dynamic markets. This short report will show you how.

This short but valuable report lays out both the principles of data mesh and the practical examples you need to implement a data mesh. The best part? It’s free.

Gurobi commissioned a Total Economic Impact (TEI) study from Forrester Consulting examining the potential return on investment (ROI) by deploying the Gurobi Optimizer. And the results are in . . .

In this guide, we will walk you through multiple strategies deployed by leading data teams that have successfully implemented data mesh such as Zalando, BlaBlaCar, and BairesDev.

This TDWI Best Practices Report examines where organizations are today in terms of responsible data and analytics and what work they still need to do. It also addresses organizational imperatives and technologies to help organizations become more responsible.

Grab this report for a better understanding of the opportunities knowledge graph can provide your organization as you explore your AI future.

In the second edition of our playbook, we’ll walk you through how to do just that with expert insights from data leaders at GitLab, The New York Times, Zalando, and other data-first companies. We’ll discuss the technologies, processes, and cultural requirements that influence this role, and share how some of the best data leaders are tackling organizational growth at scale.

The data anonymization guide introduces common legacy anonymization techniques and how they compare to synthetic data. The guide discusses the following data anonymization tools: data masking, pseudonymization, encryption, randomization, permutation, generalization.

The brightest data and analytics professionals do not want to work on legacy platforms, bound by legacy processes. They want to elevate their careers by delivering higher business impact, and they want all the modern tools and thinking required to do so. So, as you craft your 2023 plans for data and analytics, here are the top trends, predictions, and resolutions to keep you ahead of your competitors.

This ebook highlights the five core challenges that continue to prevent — or wholly derail — your efforts to scale AI, along with actionable how-tos for solving them.

You know you need to make data useful and readily accessible for business users. You know you need to make your data work for you with agility, flexibility, and scale. But how do you start? Start with Cloud Analytics Automation for Dummies. This eBook is the perfect starting point for building a solid foundation for future data and analytics success.

Planning for network automation can be daunting, and mistakes can be both time-consuming and expensive. But new trends in network automation, such as cloud computing, enable you to provide not just single services but entire infrastructure and software-defined networks on demand in real time and can lead to real efficiencies. This invaluable report shows you how to successfully lay the groundwork, assess organizational readiness, and plan for the adoption of network automation.

Data quality is vital when creating reliable algorithms. For companies looking for a solution that equals the quality of their in-house team, it can seem as though outsourcing is an impossible option. Have no fear. The "Outsourcers' Guide to Quality" will help you predict the level of quality you can expect from a data labeling provider.

Time and time again, we hear how organizations are increasingly investing in AI, but struggling to generate (and maintain) actual business impact from their analytics and AI projects. How can they sift through what’s actually worth the resources versus what’s purely hype? The Gartner Hype Cycle for Data Science and Machine Learning highlights what data science and ML concepts have high or transformational benefits, their business impacts, key drivers, obstacles to implementation, and more.

Are you curious about how your job title, gender, state, age, or education impact your salary? Want to determine whether a particular cloud certification is worth going for? Wonder how many of your peers are working remotely—and what that means for salaries? Or do you just want to see how your skills and salary compare to others in your field?

Contrary to popular belief, self-service analytics done right consists of much more than just pulling numbers from dashboards. In order to actually drive value between the data team and the data self-servers out in the business, teams need to align on the intricacies of the end-to-end self-service program from the start. That way, they ensure the initiative is actually meaningful and value-generating for the business.

We have gathered 30 brilliant data leaders to provide their thoughts on key developments and trends for 2022, as well as share their predictions for 2023.

Trying to accommodate multiple datatypes or workloads can create data fragmentation that spills over into application development, IT operations, data security, system scalability, and availability. This report explains cloud native application development techniques for working with both structured and unstructured data so you can run transactional and analytical workloads on a single, unified data platform.

In this eBook, dive into amazing examples of robotics and AI automation in retail warehouses, brick-and-mortar stores, and parcel delivery. Learn important insight about the people, processes, and tools you’ll need to bring high-quality retail AI solutions to life.

Artificial intelligence operations (AIOps) allows infrastructure and ops teams to reduce operational workloads, support rapid DevOps initiatives, and improve incident management cycles. This O’Reilly report reviews AIOps components and provides an engineering framework for enterprise-wide scalable and sustainable AI solutions.

Addressing the customer experience consistently ranks among the top initiatives for financial leaders across the globe. Data teams must deliver access to data while managing a complex, sprawling data footprint that consists of on-premises and cloud data lakes and data warehouses, organizational silos, and legacy platforms that were never designed to store today’s data volumes or meet modern query performance requirements.

Introducing Python, second edition, takes you step-by-step through one of the world’s most popular programming languages. And today you can get chapter 2, covering data types, values, variables and names, free.

Across nearly every industry, organizations of all sizes are experiencing growth in the volume, variety, and velocity of data. At the same time, there is greater demand and need to derive actionable insights from that data.

What happens when you combine one of the oldest professions with one of the newest innovations? This ebook explores how AI and new precision agriculture technology, backed by high-quality data, help today’s farmers overcome agricultural challenges, lower costs, and scale their operations.

Each chapter contains practical advice on designing and executing winning Data & Analytics strategies with AI & BI across your organization.

Dataiku, a 2x Leader (2020 and 2021) in the Magic QuadrantTM for Data Science and ML Platforms, first coined the term Everyday AI in 2021. To us, Everyday AI is all about making the use of data almost pedestrian — AI that is so ingrained and intertwined with the workings of the day-to-day that it’s just part of the business.

Thousands of organizations across nearly every business and industry use analytic process automation (APA) to accelerate data-driven business outcomes. This report uses real-world examples to examine the power of APA. You'll learn how to use APA to tackle complex problems, increase productivity, and improve efficiency.