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Migrating existing applications into containers provides better manageability and greater portability. This e-book outlines specific, technical recommendations and guidelines for container migration, ranging from image build procedures to production best practices. Also included are technical checklists for architecture, security, and performance.
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With the growing adoption of cloud models, many organizations are seeking ways to move to cloud-native development. New applications can be developed entirely using cloud models and services, exploiting microservices, autonomous development teams, agile and continuous deployment, and containerized and orchestrated cloud deployments. Unfortunately, completely rewriting all legacy applications is seldom feasible due to the required time and cost. This guide covers the "lift-and-shift" modernization model, a first step to cloud-native development.
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Your organization’s current—and future—digital transformation is based on your culture and technology choices. It is an evolutionary process, and the stages of change and the final result look different for each organization. This e-book examines the stages of "digital Darwinism" and helps you determine how your organization should evolve to effectively control your digital transformation—taking your technology "elephant" and teaching it to be agile, process-driven, and adaptive.
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It can be difficult to bring new solutions to your boss. Budget, security, and the task of maintaining existing systems are factors, and management is just trying to keep things up and running. What if you could highlight the benefits of how change would make life easier for you and your manager? Read this checklist to learn 5 key points to bring to your boss about developing applications and microservices on containers to increase your rate of innovation and competitiveness.
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A successful data-driven organization has to provide the right tools for its data analysts, developers, and business end-users. Increasingly, this means leveraging open-source software. But for all their benefits, popular open-source data programs also come with potentially time-consuming drawbacks. This paper will discuss how to handle these drawbacks, and get the right data tools to the right people, by leveraging popular open source tools with IBM® SPSS® Modeler software.
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Data science and AI simplified for you. The maturity of AI and machine learning (ML) use has reached a point where businesses of all sizes are using the technology as key strategic technology enablers. As an analytic and data science professional, you’re in control. You can empower people in centers of excellence (COE) and line of business (LOB) to C-level executives with scalable insights with trust and transparency. Data science competency is critical for your business to increase predictability, optimize operations and govern use of AI. To succeed, leaders of your organization must harness the power of machine and human intelligence—data, talent and tools—while extracting actionable insights faster than ever. However, you have to bring your AI and ML experimentation into production to drive results while tackling your data and talent challenges. The question is, how will you innovate and move your business forward while preparing your data and analytics practice to harness the power of AI?
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ESG recently completed testing of IBM Decision Optimization for Watson Studio, which is designed to enable organizations to accelerate the value they can extract from AI more easily. Testing examined how IBM Watson Studio with Decision Optimization collects data, organizes an analytics foundation, and analyzes insights at scale—with a focus on the ease of operationalizing AI and data science to improve trust, simplify compliance, and speed monetization.
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Data science and machine learning provides the basis for business growth, cost and risk reduction and even new business model creation. Implementing predictive analytics does present some challenges, however. The process can be complex, and it can be difcult to find data scientists and analysts with a mix of the right skillsets. A drag and drop, visual data science tool, exemplified by IBM SPSS Modeler, enables rapid creation of machine learning models while making it easy to collaborate with data science and analytics teams as a whole. In this paper, members of IT Central Station who use IBM SPSS Modeler share their experiences and ofer insights and recommended best practices for data science and machine learning.
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This guide will help your business perform the following actions: 1) Navigate the modern predictive analytics landscape; 2) Identify opportunities to grow and enhance your use of AI; 3) Empower both data science teams and business stakeholders to deliver value fast.
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Accelerate data science and AI project delivery with IBM Watson Studio Premium for Cloud Pak for Data.
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In our 24-criteria evaluation of multimodal predictive analytics and machine learning (PAML) providers, we identified the 13 most significant ones — Dataiku, Datawatch, FICO, IBM, KNIME, MathWorks, Microsoft, RapidMiner, Salford Systems (Minitab), SAP, SAS, TIBCO Software, and World Programming — and researched, analyzed, and scored them. This report shows how each provider measures up and helps enterprise application development and delivery (AD&D) leaders make the best choice.
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A successful data-driven organization has to provide the right tools for its data analysts, developers, and business endusers. Increasingly, this means leveraging open-source software. But for all their benefits, popular open-source data programs also come with potentially time-consuming drawbacks. This paper will discuss how to handle these drawbacks, and get the right data tools to the right people, by leveraging popular open source tools with IBM® SPSS® Modeler software.