Flyaps

Data problems are almost 3 times more likely to cause an AI project failure than anything else. Data can make or break your AI project. But how can you build a data pipeline that will help you tackle the data challenges? There are 3 options to choose from: 1️⃣ Building a custom data pipeline from scratch 2️⃣ Leveraging pre-built data pipelines 3️⃣ Using third-party services. Find out the pros and cons of each option below 👇

Writing prompts that yield accurate results takes patience. Our team has gathered the most essential practices for training LLMs to produce the desired outcomes faster and easier. Take a look at them.

Gartner predicts that companies will spend more on IT in 2024 because of the AI boom. If you wondered what are the best industries to adopt AI this year, this post is for you 👇

For ML projects you need MLOps. What is meant by this term? MLOps involves 3 aspects:  1️⃣ Automating and standardizing ML workflows 2️⃣ Implementing CI/CD pipelines for the ML model  3️⃣ Ensuring scalability, reliability, and performance of ML systems Discover how you can implement machine learning in operations in 6 steps 👇

McKinsey reports that Gen AI can help in 16 business scenarios, notably in customer operations, marketing and sales, and software engineering. There are at least four major benefits that Gen AI can bring:  Cost reduction Enhanced decision-making Improved customer service Optimized supply chain Here’s how Gen AI can improve your business processes 👇

How much will your AI project cost? 💸Spoilers, not an arm and a leg! Talking about AI implementation, the project cost boils down to four variable factors: 1️⃣ The type of AI 2️⃣ Project complexity 3️⃣ Dataset size 4️⃣ Algorithm accuracy and performance Here’s how these factors define the final project cost 👇

Prompt engineers make $280,000-$405,000 a year. Is writing prompts a stand-alone job already? Not really, if you ask us. From our perspective, it’s a must-have skill for any developer working with LLMs. At Flyaps, we build AI solutions and prompt engineering has become our daily routine. Here is a case study of how we used prompt engineering to build an AI chatbot that quickly retrieves information from a large knowledge base.

Are you still mixing Gen AI with LLMs? Let’s clear out the difference: Gen AI includes multiple technologies that generate different types of content – text, images, audio and so on. While LLMs (Large Language Models) are a subcategory of Gen AI technologies that focus on text generation. Swipe to see the examples of both 👇

At Flyaps, we developed AI solutions to help you unlock new efficiencies and growth – 3X faster than the usual AI product development cycle. We’ll take you through the entire process step-by-step, fitting the solution for your specific use case and ensuring it solves your unique business challenges. Here’s how you can adopt our pre-built AI solutions in 4 steps.

McKinsey's research uncovered 63 ways generative AI is being used across 16 different industries. What does this mean for you?  Well, it means there's an AI solution out there waiting to take your business to the next level 🔥 Think about it: businesses are already saving buckets of money with AI (Yell's report throws out a figure of up to $35,000). Still skeptical? Consider JPMorgan's AI, which processes tasks in seconds that once took 360,000 lawyer hours annually. Massive time savings and, while they haven't put a number on how much they save, you can guess. So, let's not waste any more time. Check out the gen AI use cases we've outlined below to find the perfect fit for your business needs. Here you can find more: https://lnkd.in/d6iw7g7M