Está tratando de mejorar las capacidades de IA de su empresa. ¿Cómo puedes encontrar la plataforma adecuada?

It is a multi-dimensional decision making to select the right platform. It is about selecting the 1. Right GenAI platform 2. Right Landing zone platform and 3. Right data platform. These 3 platforms have to function in tandem to deliver the expected improvement for businesses. On the low level following are the decision points: Regulatory compliance, Time to market, CapEx vs OpEx, Latency and throughput, Cost, Ease of Governance, Scalability, Data proximity, Security Managed vs Unmanaged, Automation, Availability, Resiliency, Hosting, Virtualization, Sustainability, Hardware, Software, Ease of Collaboration, Inference task, Upgrades, Technical debt, Technical skills, Open-source vs Closed-source and Accuracy - combined.

Here are 5 key steps to defining your goals: 1. Define your objectives and goals for AI implementation. 2. Identify the main problems or opportunities you want to address with AI. 3. Determine the expected outcomes and benefits for your business. 4. Establish metrics for measuring the success of your AI projects. 5. Use these defined goals to narrow down your options and select suitable AI platforms.

Establish clear and specific goals for implementing AI within your business. Whether it's optimizing operations, improving customer engagement, or enhancing data analytics, articulate measurable objectives. Defining goals helps in aligning AI capabilities with your business strategy, making it easier to evaluate potential platforms based on their ability to meet these specific objectives.

As ever, start by clearly defining your specific needs and goals. Research different AI platforms, focusing on those aligning with your business objectives - keep your current and future goals as your north star. Consider ease of integration with your existing systems, time to learn, market resource cost, and the level, and responsiveness, of technical support offered. It's also wise to look into the platform's scalability, ensuring it can grow with your business. And look at the providers strategic roadmap to ensure you are both going in the same direction. Additionally, seek recommendations from industry peers and consult AI experts. Finally, explore trial versions to test suitability before committing to a financial contract.

Start by clearly outlining what you aim to achieve with AI in your business. Whether it's enhancing customer experience, automating processes, or gaining insights from data, having clear objectives will guide you in choosing a platform that best suits your needs.

Evaluate the resources you have available, including your team's technical expertise, budget, and existing infrastructure. This assessment will help you determine which AI platform aligns with your resource capabilities and how much investment in training or additional infrastructure might be needed.

Evaluate the resources available within your organization, including budget, expertise, and infrastructure. Determine the level of technical expertise among your team members and identify any gaps that may require external support. Consider the scalability of the AI platform in relation to your business growth. Understanding your resource landscape ensures that the chosen platform aligns with your capabilities and supports effective implementation.

When enhancing your business' AI capabilities, identifying the optimal platform is crucial. From a legal perspective, ensuring data compliance and privacy is paramount. Selecting a platform with robust security measures and compliance features is non-negotiable. On the AI front, consider the skills within your team. A platform with user-friendly interfaces and comprehensive documentation can facilitate smoother integration. Legal-wise, having contracts that clearly define data ownership and usage rights is advisable. Budget considerations are vital. Evaluate whether a platform offers a scalable pricing model aligned with your business growth. Hope this helps.

Assessing existing business resources and evaluating them to see what areas AI can improve is an essential step. It helps businesses evaluate how ready they are for AI improvement, taking into consideration existing infrastructure, size or quality of data they have, existing standards, and skill level of their employees. It also makes it easy to identify areas that would really need AI improvement.

This touches on some of my point in prompt 1, you need to know where you are at the beginning of your AI project and the reality of how long it will take to get there based on resources. Now, you can obviously add them as you need and thats often the most efficient. But keep in mind the timeline of these types of things and that AI is moving faster than almost any other industry in history. So be prepared to pivot on your resource needs as needed. At the same time, pivoting from one platform to another is not easy and can be incredibly difficult. Try to choose the right one from the start because if you do, it can grow as you do and you will not ever have to entertain switching platforms because you are missing valuable tools or otherwise.

Top 10 considerations when comparing platforms: 1. Consider the types and quality of models offered or supported. 2. Check if the platform covers the domains and tasks of interest. 3. Evaluate if state-of-the-art algorithms and frameworks are used. 4. Assess the ease and speed of model development and deployment. 5. Determine the time it takes to create, train, test, and deploy a model. 6. Consider the amount of coding or configuration required. 7. Evaluate the performance and reliability of the platform's models. 8. Take into account data security and privacy. 9. Ensure compliance with relevant regulations. 10. Consider how the platform monitors and updates models over time.

--Model Types and Quality: We scrutinize the range of models offered, ensuring they align with our project's domain. It's vital that these platforms support state-of-the-art algorithms and frameworks that can tackle the specific tasks we're interested in. --Development and Deployment Process: Time efficiency is key. We evaluate how long it takes to go from model creation to deployment, including the amount of coding or configuration needed. --Performance and Scalability: Assessing the accuracy, robustness, and scalability of the models is crucial. We need a platform that not only delivers high-performance results but can also scale with the growing demands of our project.

Thoroughly assess the features and functionalities offered by various AI platforms. Consider factors such as machine learning capabilities, data integration, scalability, and compatibility with your existing systems. Evaluate user interfaces, ease of integration, and the platform's ability to address your specific business needs. A detailed comparison ensures that the chosen AI platform aligns with your requirements and provides the necessary tools to achieve your defined goals.

After setting clearly defined goals and assessing existing business resources, it is important for businesses to compare which AI features or functionalities currently exist that best meet their identified areas of improvement. Model performance, functionality, speed, scalability, frameworks, ethics, privacy, and compliance are all areas to make good comparisons.

When comparing features and functionalities of AI platforms, focus on their capability to meet your specific business needs. Look at the range of AI technologies offered, like machine learning, natural language processing, and automation. Evaluate the ease of integration with your existing systems and data compatibility. Assess the scalability of the platform to ensure it can grow with your business. Consider user-friendliness and the level of technical expertise required. Check for customisation options and support services provided. Analyse performance metrics and security features to ensure robustness and data protection. Comparing these aspects will help you choose the most suitable AI platform.

Customer Experiences: Look for customer reviews, testimonials, and case studies that provide insights into the experiences of businesses similar to yours. Assess both strengths and weaknesses of the platform from the perspective of real users. Seek References and Demos: Ask the platform providers for references or demos. This allows you to see the platform in action, understand its capabilities, and assess how well it addresses your specific business problems or needs.

Research and analyse customer reviews and feedback for each AI platform under consideration. Pay attention to reviews from businesses with similar needs and challenges. Assess user experiences, customer support, and the platform's overall reliability. Real-world feedback provides valuable insights into the practical performance and user satisfaction, helping you make an informed decision about the suitability of each platform for your business.

Research, research, research. But do so quickly and efficiently. The research is a portion left by many in AI because they don't want to do the painstaking work it usually entails. But there are plenty of tools out there that can greatly assist in this process. Reading reviews is not enough, not even close. Use them as part of the process but they cannot be the sole part of the process. See if you can get interviews with platform customers to get their thoughts and experience with the AI platform you are considering. Two, maybe 3, is all you need. Then, verify some things quickly with your research, decide, get to work.

Seek Reviews and Testimonials: Look for detailed reviews and testimonials from other users. They often highlight the platform's strengths, weaknesses, and real-world performance. Analyze Case Studies: Review case studies to understand how the platform has been applied in similar scenarios or industries. This provides insights into its practical utility and effectiveness. Check Ratings: Ratings can give a quick overview of user satisfaction and the platform's overall reputation. Request References or Demos: Ask the provider for references or demos. Direct Inquiries: Reach out to current or past users, if possible, for firsthand accounts of their experiences.

Considering customer feedback is essential when selecting an AI platform. Look for detailed reviews and testimonials that reveal both the platform's strengths and potential shortcomings. Case studies offer valuable context on its real-world applications. Don't hesitate to request references or live demonstrations to gain a firsthand understanding of the platform's capabilities and how they match your specific business requirements. This can significantly enhance your decision-making process.

Define and Prioritize Criteria: - Clearly define criteria for evaluating AI platforms, prioritizing factors aligned with your business goals and use cases. Conduct Iterative Testing: - Implement a Proof of Concept (POC) with a subset of platforms, testing aspects such as model development, integration, performance, scalability, user-friendliness, and support. Refine and Iterate: - Analyze results, refine evaluation criteria based on insights, and iterate on the testing process to gradually narrow down the options, involving stakeholders and considering budget constraints.

This part of the process is usually the most fun, and also the most frustrating if things don't go according to plan. While the prompt suggests a free trial, those have become few and far between and be sure to read the T's & C's before committing to the ones still out there. For me, pilots are the best because you can choose your audience as well as have proper expectations set for what you're hoping to achieve from your pilots. I want them to tell me everything they don't like, what features they want, what will make their job easier and better in regards to the tool, and more. That feedback is priceless and I emphasize as much when onboarding them. Of course I want to hear what they like also but the pain points are far more valuable.

Before committing to a long-term contract, conduct pilot tests or trial periods with short-term subscriptions for the selected AI platforms. Implement the platform in a controlled environment to assess its performance, user-friendliness, and alignment with your goals. Gather feedback from your team and iterate on the implementation based on insights gained during the testing phase. This iterative approach ensures that the chosen AI platform seamlessly integrates into your business processes and delivers the expected results before full-scale adoption.

This is top of my list. The number of tools has proliferated, as has the number of LLMs. The only way to establish which is best for your business is to try a few and assess performance. However, testing has to be systematic. We are seeing a number of businesses that should know better make basic IT deployment errors that cause brand embarrassment. It's not necessary. Dev Ops discipline is required.

As someone who's witnessed countless project triumphs and stumbles, let me tell you: testing and iterating is the ultimate weapon. You need to experience the platform firsthand to feel its strengths and weaknesses under your data and goals. Here's what you need to do: Free trials and sandbox environments: Experiment with features, train models on your data, and see how the platform handles your specific challenges. Pilot projects: Pick a small, manageable project that aligns with your overall goals and dive in with the chosen platform. You'll understand its integration, performance, and impact on your workflows. Embrace the iterative loop: Testing isn't a one-time thing. As you learn and gather data, refine your approach and strategy.

A critical thing you need to consider is whether the tool you are going to use requires prompt engineering skills. Prompt engineering is both a skill and art of communicating with AI, and I would argue one of the most important skills to learn these days. I would go as far as saying that not having at least the basic skills of prompt engineering in the next few years will become the same as not being able to use a phone today. Having prompt engineering skills, allows you to have a much broader set of tools you can use to be competitive in this new world, and these tools are much more flexible and powerful.

Further, it is important to consider the long-term viability of the vendor/SaaS company offering the tools. As you delve into the process of selecting an AI platform, it's crucial to thoroughly assess the vendor's background, reputation, and long-term viability. Investigate the vendor's track record in delivering consistent updates, addressing customer concerns, and adapting to technological advancements. A reliable vendor with a clear roadmap for the platform's evolution instils confidence in its commitment to supporting your business's AI journey over the long term. Evaluate the vendor's stability, support infrastructure, and responsiveness to ensure sustainable continuity as your business scales.

Improving business' AI capabilities starts with identifying the right AI platform that aligns with your specific needs and goals. Assess type of AI applications you intend to develop, such as natural language processing, image recognition, or predictive analytics. Research, compare platforms like IBM Watson, Google Cloud AI, Microsoft Azure AI, and Amazon Web Services for their features, scalability, and support. Consider ease of integration with your existing systems and level of technical expertise required. It's also crucial to evaluate the cost-effectiveness and the quality of customer support provided. Attending webinars, reading case studies, and seeking recommendations from industry peers can also aid in making an informed decision.

Start by diving deep into AI-centered communities. They are the heart and soul of AI development and an excellent resource for real-time insights. Follow thought leaders in the field who are unassuming, yet are constantly experimenting with AI. Be selective of those with a substantial audience however, as there is a substiantial amount of so-called "experts". How can someone be an expert in a space with 1000 new companies and 4000 new research papers every month. The path to finding the perfect AI platform is not a solitary journey. Find those guides that will meet you where you are presently in your AI journey, and explore together.

a. Scalability: Ensure that the chosen AI platform can scale with your business as it grows. Scalability is crucial for long-term success. b. Integration: Check if the platform easily integrates with your existing systems and software. Seamless integration can save time and resources. c. Customization: Determine whether the platform allows for customization to meet your unique business needs. A one-size-fits-all solution may not be sufficient. e. Support and Training: Assess the level of support and training offered by the platform provider. Adequate training and support are essential for