Tips

Tips

• AI inference vs. training: Key differences and tradeoffs

  AI inference and training are both critical phases of model development. Learn how to balance their demands to optimize performance, manage costs and scale models effectively.  Continue Reading

• How to run LLMs locally: Hardware, tools and best practices

  Local deployments of large language models offer advantages, including privacy, speed and customization -- but organizations need the right tools and infrastructure to succeed.  Continue Reading

• Reduce project expenses with AI cost optimization strategies

  AI projects don't have to break the bank. From open source tools to clever cloud strategies, learn how to keep your AI initiatives efficient and cost-effective.  Continue Reading

• 4 types of AI (artificial intelligence) explained

  Despite the rapid evolution and mind-boggling capabilities of AI tools, AI does not yet possess the general intelligence of humans, let alone ASI. Learn about four levels of AI.  Continue Reading

• Is getting an AI certification worth it? Factors to consider

  Some say AI certification is essential, while others worry it's a waste of time. Learn the benefits and downsides of AI certifications and how to find the right one for your needs.  Continue Reading

• How to choose the right LLM for your needs

  Selecting the best large language model for your use case requires balancing performance, cost and infrastructure considerations. Learn what to keep in mind when comparing LLMs.  Continue Reading

• How businesses can measure AI success with KPIs

  AI KPIs should include direct and indirect metrics. Learn about the standard and generative AI-specific KPIs that will help you gauge the success of your AI projects.  Continue Reading

• Types of AI algorithms and how they work

  AI algorithms can help businesses gain a competitive advantage. Learn the main types of AI algorithms, how they work, and why companies must thoroughly evaluate benefits and risks.  Continue Reading

• How to effectively manage AI projects in 12 steps

  AI is a high priority for companies but results often fall short of expectations. These 12 steps will help you successfully manage AI projects and deliver business value.  Continue Reading

• The future of AI: What to expect in the next 5 years

  AI's impact in the next five years? Human life will speed up, behaviors will change and industries will be transformed -- and that's what can be predicted with certainty.  Continue Reading

• How small businesses can take advantage of AI

  Small business owners see AI's potential for automation and transformation, yet concerns remain. This seven-step program will help AI implementations get off on the right track.  Continue Reading

• How to create a winning AI strategy for your business

  To deliver real business value, AI projects must be aligned with organizational goals. Here is a 10-step program for developing an effective AI strategy, plus sample templates.  Continue Reading

• Artificial intelligence vs. human intelligence: Differences explained

  Artificial intelligence is humanlike. There are differences, however, between natural and artificial intelligence. Here are three ways AI and human cognition diverge.  Continue Reading

• Microsoft Copilot vs. Google Gemini: How do they compare?

  Microsoft Copilot and Google Gemini are frontrunners in the generative AI productivity tools space. Explore how they compare in terms of key features, capabilities and pricing.  Continue Reading

• Compare 7 top AutoML tools for machine learning workflows

  From cloud-based platforms to open source options, compare the pros and cons of leading AutoML tools, which automate key machine learning tasks to accelerate workflows.  Continue Reading

• AI parameters: Explaining their role in AI model performance

  What is the correlation between the number of parameters and an AI model's performance? It's not as straightforward as the parameter-rich generative AI apps would have us believe.  Continue Reading

• The history of artificial intelligence: Complete AI timeline

  From the Turing test's introduction to ChatGPT's celebrated launch, AI's historical milestones have forever altered the lifestyles of consumers and operations of businesses.  Continue Reading

• How to manage proprietary enterprise data in AI deployments

  Explore strategies for managing sensitive data in enterprise AI deployments, from establishing clear data governance to securing tools and building a responsible AI culture.  Continue Reading

• Explore the role of training data in AI and machine learning

  AI and machine learning models use a variety of learning methods to process and analyze data -- but regardless of how a model is trained, data quality and relevance are crucial.  Continue Reading

• 24 advantages and disadvantages of AI

  Is AI good or bad? AI will benefit society, according to experts, but only with the correct guidelines in place and a solid understanding of what AI systems can and cannot do.  Continue Reading

• AI in banking: Benefits, risks, what's next

  AI technologies bring operational efficiency and customer benefits to banking. Learn how GenAI and other AI tools are transforming financial services and risks to watch out for.  Continue Reading

• 13 steps to achieve AI implementation in your business

  AI technologies can enable and support essential business functions. But organizations must have a solid foundation in place to bring value to their business strategy and planning.  Continue Reading

• What's the best programming language for machine learning?

  While no one language is perfect for AI and machine learning, considering factors like efficiency, readability and community support can help developers make the best choice.  Continue Reading

• Trustworthy AI explained with 12 principles and a framework

  To be considered trustworthy, AI systems should meet these 12 principles and employ a four-step framework to ensure the use of AI is ethical, lawful and robust.  Continue Reading

• What businesses should know about OpenAI's GPT-4o model

  Enterprise use cases are driving the need for faster AI response times, better data handling and cost optimization. OpenAI attempts to meet that need with GPT-4o.  Continue Reading

• How can AI drive revenue? Here are 10 approaches

  Artificial intelligence has captured the imagination of many a boardroom. Now, the emphasis has shifted to capturing revenue through AI-driven use cases.  Continue Reading

• AI model optimization: How to do it and why it matters

  Challenges like model drift and operational inefficiency can plague AI models. These model optimization strategies can help engineers improve performance and mitigate issues.  Continue Reading

• How to choose the best GPUs for AI projects

  GPUs are not all built the same. Factors like total core count, memory clock speed, hardware optimizations and cost can influence which GPU is right for a specific AI project.  Continue Reading

• How do big data and AI work together?

  Enterprises are leaning on big data to train AI algorithms and, in turn, are using AI to understand big data. The results are pushing business operations forward.  Continue Reading

• Simplify enterprise AI integration with a centralized AI hub

  For enterprises looking to scale their AI projects, centralized AI hubs and governance can simplify integration, streamline operations and ensure consistency.  Continue Reading

• How and why to run machine learning workloads on Kubernetes

  Running ML model development and deployment on Kubernetes is an absolute must in a world where decoupling workloads can optimize resources and cut costs.  Continue Reading

• How and why to create an AI bill of materials

  AIBOMs help developers and security teams by providing a transparent view of AI system components, improving supply chain security and compliance. Use this guide to get started.  Continue Reading

• 15 top applications of artificial intelligence in business

  The use of AI in business applications and operations is expanding. Learn about where enterprises are applying AI and the benefits AI applications are driving.  Continue Reading

• Tips to prevent machine learning scalability problems

  Addressing ML scalability challenges involves selecting the right models, planning resource usage and managing network connectivity to support expanding applications and data load.  Continue Reading

• How to ensure interpretability in machine learning models

  When building ML models, developers can use several techniques to make models easier for humans to interpret, leading to improved transparency, troubleshooting and user acceptance.  Continue Reading

• Microsoft 365 Copilot features and architecture explained

  Microsoft's new assistant adds generative AI to the workplace, using various features and architectural components for automated suggestions, content creation and data insights.  Continue Reading

• Generative AI ethics: 8 biggest concerns and risks

  As its adoption grows, generative AI is upending business models and forcing ethical issues like customer privacy, brand integrity and worker displacement to the forefront.  Continue Reading

• How to build the business case for AI initiatives

  Building a compelling business case for AI requires attention to business pain points, financial and risk considerations, and collaboration with the CFO.  Continue Reading

• Best practices for integrating AI and ESG strategies

  Integrating environmental, social and governance goals into AI models can help organizations achieve more ethical, responsible and sustainable business outcomes.  Continue Reading

• Top 10 generative AI courses and training resources

  These wide-ranging resources help users of all skill levels learn the ins and outs of generative AI, offer advice and training on tools, and help users keep up with trends.  Continue Reading

• Generative AI vs. predictive AI: Understanding the differences

  Generative AI and predictive AI vary in how they handle use cases and unstructured and structured data, respectively. Explore the benefits and limitations of each.  Continue Reading

• 10 popular libraries to use for machine learning projects

  Machine learning libraries expedite the development process by providing optimized algorithms, prebuilt models and other support. Learn about 10 widely used ML libraries.  Continue Reading

• Is your business ready for the EU AI Act?

  The EU AI Act provides businesses with a rubric for AI compliance. Businesses must understand the landmark act to align their practices with upcoming AI regulations.  Continue Reading

• Learn how to create a machine learning pipeline

  Well-considered machine learning pipelines provide a structured approach to AI development in modern IT environments, ensuring uniformity, speed and business alignment.  Continue Reading

• What are the benefits of an MLOps framework?

  While maintaining machine learning models throughout their lifecycles can be challenging, implementing an MLOps framework can enhance collaboration, efficiency and model quality.  Continue Reading

• 7 machine learning challenges facing businesses

  Machine learning challenges cover the spectrum from ethical and cybersecurity issues to data quality and user acceptance concerns. Read on to learn about seven common obstacles.  Continue Reading

• Generative models: VAEs, GANs, diffusion, transformers, NeRFs

  Choosing the right GenAI model for the task requires understanding the techniques each uses and their specific talents. Learn about VAEs, GANs, diffusion, transformers and NerFs.  Continue Reading

• GitHub Copilot vs. ChatGPT: How do they compare?

  Copilot and ChatGPT are generative AI tools that can help coders be more productive. Learn about their strengths and weaknesses, as well as alternative coding assistants.  Continue Reading

• A short guide to managing generative AI hallucinations

  Generative AI hallucinations cause major problems in the enterprise. Mitigation strategies like retrieval-augmented generation, data validation and continuous monitoring can help.  Continue Reading

• How to build and organize a machine learning team

  Explore the process of building an ML team, including reasons to build one and descriptions of the core roles of project manager, data engineer, data scientist and ML engineer.  Continue Reading

• A guide to deploying AI in edge computing environments

  Deploying AI at the edge is increasingly popular due to processing speed and other benefits. Consider hosting requirements, latency budget and platform options to get started.  Continue Reading

• How to become a natural language processing engineer

  Explore the education, experience and skills needed to excel in the demanding yet rewarding field of NLP engineering, including expertise in linguistics, math and programming.  Continue Reading

• 8 machine learning benefits for businesses

  For business leaders, machine learning's predictive capabilities can forecast product demand, reduce equipment downtime and retain customers.  Continue Reading

• Choosing between a rule-based vs. machine learning system

  Deciding between a rule-based vs. machine learning system comes down to complexity and organizational needs. Compare the advantages, drawbacks and use cases for each AI approach.  Continue Reading

• Gemini vs. ChatGPT: What's the difference?

  ChatGPT took early lead among AI-generated chatbots before Google answered with Gemini, formerly Bard. While ChatGPT and Gemini perform similar tasks, there are differences.  Continue Reading

• How to craft a responsible generative AI strategy

  Generative AI's potential in the enterprise must be balanced with responsible use. Without a clear strategy in place, the technology's risks could outweigh its rewards.  Continue Reading

• Compare natural language processing vs. machine learning

  Both natural language processing and machine learning identify patterns in data. What sets them apart is NLP's language focus vs. ML's broader applicability to many AI processes.  Continue Reading

• The different types of machine learning explained

  Rigorous experimentation is key to building machine learning models. Learn about the main types of ML models and the many factors that go into training the right one for the task.  Continue Reading

• Compare 6 top MLOps platforms

  Choosing the right MLOps platform means considering features, pricing and ease of integration into your current machine learning environment. Evaluate six leading options.  Continue Reading

• Examining the future of AI and open source software

  As AI coding tools gain traction in the enterprise, it remains unclear whether AI-generated code violates open source software licenses -- but legal claims indicate possible risk.  Continue Reading

• Beyond algorithms: The rise of data-centric AI

  Prioritizing data curation, preparation and engineering -- rather than tweaking model architecture -- could significantly improve AI systems' reliability and trustworthiness.  Continue Reading

• How to build an MLOps pipeline

  Machine learning initiatives involve multiple complex workflows and tasks. A standardized pipeline can streamline this process and maximize the benefits of an MLOps approach.  Continue Reading

• How to identify and manage AI model drift

  The training data and algorithms used to build AI models have a shelf life. Detecting and correcting model drift ensures that these systems stay accurate, relevant and useful.  Continue Reading

• How to train an LLM on your own data

  Retraining or fine-tuning an LLM on organization-specific data offers many benefits. Learn how to start enhancing your LLM's performance for specialized business use cases.  Continue Reading

• Embedding models for semantic search: A guide

  Embedding models in semantic search are changing how we interact with information by going beyond keyword matching to capture meaning and relationships in text and other data.  Continue Reading

• How to measure the ROI of enterprise AI initiatives

  Interest in AI tools and systems has skyrocketed across industries. To ensure their endeavors are worthwhile, businesses are increasingly emphasizing return on investment.  Continue Reading

• Compare proprietary vs. open source for enterprise AI

  Unsure whether to choose proprietary or open source AI for your enterprise deployment? Compare the pros and cons of both software models, including how each can benefit businesses.  Continue Reading

• Evaluate whether your organization needs a chief AI officer

  As more businesses start developing comprehensive AI strategies, the new role of chief AI officer, or CAIO, might become the next addition to your organization's executive suite.  Continue Reading

• Tips for planning a machine learning architecture

  When planning a machine learning architecture, organizations must consider factors such as performance, cost and scalability. Review necessary components and best practices.  Continue Reading

• How to build an enterprise generative AI tech stack

  Generative AI tech stacks consist of key components like LLMs, vector databases and fine-tuning tools. The right tech stack can help enterprises maximize their generative AI ROI.  Continue Reading

• How to get started with machine learning

  Machine learning roles are rapidly evolving and require a diverse range of skills. Looking to join the field? Start by exploring job responsibilities and required experience.  Continue Reading

• How organizations should handle AI in the workplace

  When implementing AI in the workplace, organizations must build a comprehensive strategy aligned with business values. Failing to do so could lead to the emergence of shadow AI.  Continue Reading

• Clean data is the foundation of machine learning

  Clean data is crucial to achieving accurate, consistent and thorough machine learning models. With the right prep techniques, teams can improve data quality and model outcomes.  Continue Reading

• Compare enterprise generative AI deployment options

  To pick the best generative AI deployment model for your organization, examine how cloud and on-premises approaches fit into your security, cost, infrastructure and network needs.  Continue Reading

• Explore mitigation strategies for 10 LLM vulnerabilities

  As large language models enter more enterprise environments, it's essential for organizations to understand the associated security risks and how to mitigate them.  Continue Reading

• Compare large language models vs. generative AI

  While large language models like ChatGPT grab headlines, the generative AI landscape is far more diverse, spanning models that are changing how we create images, audio and video.  Continue Reading

• GPT-3.5 vs. GPT-4: Biggest differences to consider

  GPT-3.5 or GPT-4? With multiple OpenAI language models to choose from, picking the right option for your organization's needs comes down to the details.  Continue Reading

• Prompt engineering tips for ChatGPT and other LLMs

  Master the art of prompt engineering -- from basic best practices to advanced strategies -- with practical tips to get more precise, relevant output from large language models.  Continue Reading

• AI and compliance: Which rules exist today, and what's next?

  The AI regulatory landscape is still racing to catch up with the fast pace of industry and technological developments, but a few key themes are starting to emerge for businesses.  Continue Reading

• Improve AI security by red teaming large language models

  Cyberattacks such as prompt injection pose significant security risks to LLMs, but implementing red teaming strategies can test models' resistance to various cyberthreats.  Continue Reading

• The importance and limitations of open source AI models

  Despite rising interest in more transparent, accessible AI, a scarcity of public training data and compute infrastructure presents significant hurdles for open source AI projects.  Continue Reading

• The role of trusted data in building reliable, effective AI

  Without quality data, creating and managing AI systems is an uphill battle. Methods such as zero-copy integration and primary key consistency can ensure trusted data for better AI.  Continue Reading

• 8 top generative AI tool categories for 2024

  Need a generative AI-specific tool for your organization's development project? Explore the major categories these tools fall into and their capabilities.  Continue Reading

• Generative AI vs. machine learning: How are they different?

  Generative AI differs from simpler forms of machine learning in several ways, but both can enhance efficiency, personalize customer experiences and drive revenue growth.  Continue Reading

• How AI is advancing assistive technology

  Recent advances in generative AI could revolutionize assistive technology. For people relying on assistive tools, AI-powered devices could usher in a new era of accessibility.  Continue Reading

• Compare 3 top AI coding tools

  AI-powered coding tools GitHub Copilot, Amazon CodeWhisperer and Tabnine take an innovative approach to software development -- but don't count the human developer out just yet.  Continue Reading

• Explore the impact of data science in business workflows

  Data science and machine learning are reshaping business workflows and customer experiences, ushering in an era of highly tailored services and predictive strategies.  Continue Reading

• How an AI governance framework can strengthen security

  Learn how AI governance frameworks promote security and compliance in enterprise AI deployments with essential components such as risk analysis, access control and incident response.  Continue Reading

• How to become an MLOps engineer

  Explore the key responsibilities and skills needed for a career in MLOps, which focuses on managing ML workflows throughout the model lifecycle.  Continue Reading

• A guide to ChatGPT Enterprise use cases and implementation

  ChatGPT Enterprise promises powerful generative AI capabilities for business use cases, but successful implementation requires careful planning for security, costs and integration.  Continue Reading

• AI vs. machine learning vs. deep learning: Key differences

  The terms artificial intelligence, machine learning and deep learning are often used interchangeably, but they aren't the same. Understand the differences and how they're used.  Continue Reading

• What are the risks and limitations of generative AI?

  As enterprise adoption grows, it's crucial for organizations to build frameworks that address generative AI's limitations and risks, such as model drift, hallucinations and bias.  Continue Reading

• Adversarial machine learning: Threats and countermeasures

  As machine learning becomes widespread, threat actors are developing clever attacks to manipulate and exploit ML applications. Review potential threats and how to combat them.  Continue Reading

• Successful generative AI examples and tools worth noting

  Industries are using generative AI in various ways to generate new content. Learn about successful examples of this technology and notable tools in use.  Continue Reading

• The data privacy risks of third-party enterprise AI services

  Using off-the-shelf enterprise AI can both increase productivity and expose internal data to third parties. Learn best practices for assessing and mitigating data privacy risk.  Continue Reading

• How to source AI infrastructure components

  Rent, buy or repurpose AI infrastructure? The right choice depends on an organization's planned AI projects, budget, data privacy needs and technical personnel resources.  Continue Reading

• 10 realistic business use cases for ChatGPT

  Many business use cases for ChatGPT are emerging, but organizations must decide which best fit their specific needs. Consider 10 pragmatic example applications.  Continue Reading

• Explore 14 real-world use cases for adaptive AI

  Adaptive AI's ability to alter its code in response to changing circumstances is useful in dynamic, complex environments. Discover potential business use cases for this technology.  Continue Reading

• 10 prompt engineering tips and best practices

  Asking the right questions is key to using generative AI effectively. Learn 10 tips for writing clear, useful prompts, including mistakes to avoid and advice for image generation.  Continue Reading

• Evaluate model options for enterprise AI use cases

  To successfully implement AI initiatives, enterprises must understand which AI models will best fit their business use cases. Unpack common forms of AI and best practices.  Continue Reading