You’re looking to integrate AI into your workflow. What software tools should you be using?

It depends upon the field in which we are integrating the AI and the level of automation to automate the tasks some other factors whether you are a beginner advanced or a student and the budget for buying the premium membership of AI tools as all the tools are not free. The most commonly used AI tools are : GCP AI and ML platform AWS AI and ML platform AZURE AI and ML platform GitHub Colpilot TensorFlow and Keras Pytorch ChatGPT - 3.5/4 HuggingFace

Integrating AI into our workflow depends on our specific needs and tasks. However, some commonly used software tools for AI integration include: 1. TensorFlow 2. PyTorch 3. Scikit-learn: A simple and efficient tool for data mining and data analysis, built on NumPy, SciPy, and matplotlib. 4. Keras: An open-source neural network library written in Python, capable of running on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. 5. OpenAI's GPT 6. Microsoft Azure ML 7. Amazon SageMaker 8. IBM Watson These tools cater to different aspects of AI development and deployment, so choosing the right ones depends on our project requirements, expertise, and budget.

My preferred programming language for data preprocessing is Python, mainly due to the great number of libraries available for this purpose. Inadequate data preprocessing can lead machine learning algorithms to learn and replicate erroneous patterns, compromising the accuracy of the model. Powerful libraries like Matplotlib and Seaborn play a crucial role in gaining insights into the data. You can highlight areas that require further investigation. For example, in a dataset related to turtle species, if the "weight" (in kg) column contains a couple of values in the thousands it becomes evident that the data needs some corrections, if you can verify the data source you might find out that those measurements were recorded in grams not kg.

Deep learning stands out as the ideal solution for integrating AI into workflows due to its capacity to automatically learn complex patterns in data. Frameworks like TensorFlow and PyTorch provide powerful tools for implementing and deploying deep learning models, enabling efficient automation in tasks such as image and speech recognition, natural language processing, and decision-making. The adaptability and capability to handle diverse data types make deep learning a compelling choice for achieving accurate and efficient AI integration into various workflows.

By incorporating Databricks for data processing tasks, you can accelerate the data preparation phase and streamline the transition to AI implementation. Its advanced features for data management, exploration, and visualization complement the capabilities of Pandas, NumPy, and SciPy, enabling a comprehensive approach to data preprocessing and analysis.

For training ML algorithms, you can use 1. Jupyter notebooks 2. GCP instances with GPUs for training unstructured data 3. Amazon sagemaker 4. Azure ML studio

Integrating AI into our workflow depends on the following factors: 1: Use Case 2: Workflow Usage Scope & Budget * Use Case The tasks to be automated in our workflow (Sentiment analysis, Text Generation, Translation, Summarization, Question Answering). Based on the use case we choose a model. * Workflow usage Scope & Budget Scope defines the boundaries of automation and its usage. Based on usage we select the deployment environment (CPU, GPU). Selecting any deployment environment budget is also an important factor. AI Tools for AI Workflow are: AWS AI and ML platform GCP AI and ML platform AZURE AI and ML platform RunPod ChatGPT - 3.5/4 Open Source LLM (PaLm, Flan T5, BERT etc.) Transformer TensorFlow and Keras Pytorch

AI can be secret weapon! But choosing the right tools is key. Think about your tasks, what kind of AI you need, and your tech skills. TensorFlow rocks for building custom models, while SpaCy is an NLP pro who understands language. Need to see things? OpenCV is your eyes, and YOLO spots objects in a flash. Cloud giants like Google AI and Amazon SageMaker are on standby, ready to power your creations. Once built, Docker keeps things tidy for deployment. Teamwork makes the dream work, so Git keeps versions in sync and GitHub lets everyone share. And don't forget to explain what your AI is doing with LIME! P.S. This is just a small sample, there are tons of amazing AI tools out there waiting to be discovered!

Machine learning demonstrates great potential applications for business, starting from the conventional valuation of properties to the intricate domain of stock trading. An area that has particularly piqued my curiosity is the incorporation of machine learning in cybersecurity. Consider harnessing the pattern recognition capabilities of ML to identify anomalous data requests within a company's network, for instance, instances where management is unlikely to solicit personal information from employees every four seconds. Such models can be implemented in TensorFlow or Pythorch, it is just a matter of the developers preference.

Machine learning, a branch of AI, leverages data and algorithms to train models for autonomous tasks. Algorithms analyze massive datasets, enabling them to learn and make predictions without explicit programming. Supervised learning uses labeled data, unsupervised seeks patterns in unlabeled data, and reinforcement learning operates on reward/punishment feedback. Ultimately, machine learning aims to create models that generalize beyond training data, automating decisions and driving intelligent behavior.

There are three main steps while integrating DL model into your workflow. Lets assume we want to integrate image recognition model into workflow and for the same we want to train the model 1. Annotating image to identify items- apply image cleaning techniques. Applying augmentation if data size is limited. Using annotation tool to get bounding boxes around the items you want to detect. Storing coordinates in json or xml format 2. Training model- Identify suitable model for your problem. See if its pretrained weights are available. For fine tuning such models, get the data in format of the model. Fix the parameters such as epochs, batch size etc. Use suitable GPUs according to data size 3. Inference pipeline- deploy the inference service

I prioritize versatile tools like TensorFlow and PyTorch for deep learning. Scikit-learn and Keras serve well for machine learning tasks. Jupyter Notebooks enhance collaboration and prototyping. To manage models, MLflow and TensorBoard prove invaluable. Additionally, cloud platforms such as AWS, Azure, or Google Cloud offer scalable AI services. Tailoring tool choices to project requirements ensures a robust and efficient AI workflow.

Consider using TensorFlow and PyTorch. TensorFlow, developed by Google, is renowned for its flexibility and scalability in building and deploying machine learning models. PyTorch, known for its user-friendly interface and dynamic computational graph, is highly favored in the research community for rapid prototyping and experimentation in deep learning.

At the moment, there are various libraries for Deep Learning, e.g., PyTorch or TensorFlow. One should avoid their own implementation and choose a suitable framework carefully. Here are few criteria to think of: ? Is the framework well-equipped for the task at hand? ? Does it offer a suitable way for inference and/or optimisations? ? Is the framework updated on regular basis? ? Are there many contributors / users of the framework? ? In cases where modifications of the implementation may be necessary, the readability of the code should also be considered. It is advisable to first attempt to solve the problem quickly and optimise later, even if means switching the libraries and rewriting from scratch.

A subset of machine learning, utilizes artificial neural networks inspired by the brain's structure and function. These networks, with multiple interconnected layers, "learn" by processing data through each layer, progressively extracting increasingly complex features. Unlike traditional machine learning, deep learning excels at tasks like image recognition, natural language processing, and speech recognition, where data is inherently complex and high-dimensional.

NLP might be an integral part of your workflow if you have text or audio data. Let’s say you are an e-commerce company with data of what customers are buying and from whom they are buying, you can use this information to do predict many other things, like what they may buy next or what might be the sector the customer is interested in, or have a chatbot which can help answering customer queries in the platform. All the tools or libraries mentioned above can be used, but right now, we have transformers or LLM models, that can also be used to do all these things in a much better way than traditional algorithms. But the only thing to keep in mind is the cost. See what trade-off is possible and then come to conclusion on what to use.

To integrate NLP tools into your business workflow, consider using tools like Apache OpenNLP, Stanford CoreNLP, or TensorFlow’s Natural Language Toolkit (tf-nlp). These frameworks provide a robust foundation for various NLP tasks, enhancing your capabilities in text analysis, sentiment analysis, and language understanding. Additionally, explore cloud-based services like Google Cloud Natural Language API or Microsoft Azure Text Analytics for scalable and convenient solutions.

Right now a lot of businesses are looking to add Retrieval Augmented Generation (rag for short) to their arsenal. It allows for quick, intuitive searches for internal company information. This task is so important and complex, I would only entrust it to the world’s best AI, GPT-4, and nothing less. Even GPT-3.5 isn’t sufficient, and open-source or non-OpenAI competitorst are much worse.

Hugging Face is a highly recommended tool. Hugging Face offers a wide range of pre-trained models like BERT, GPT, and T5, which are pivotal in various NLP tasks such as text classification, question-answering, and language generation. It's known for its user-friendly interface and comprehensive library (transformers), making it easier for developers to implement complex NLP models with minimal code. Additionally, Hugging Face provides an active community and extensive documentation, which are great resources for both beginners and experienced NLP practitioners.

NLP, bridging the gap between humans and computers, empowers machines to comprehend and handle human language (written and spoken). It draws on computer science, linguistics, and statistics to tackle tasks like translation, sentiment analysis, and speech recognition. By analyzing grammar, vocabulary, and context, NLP unlocks meaning, intent, and relationships within language, paving the way for natural interactions with machines and diverse applications like chatbots and automated analysis.

In my experience, if your data contains images or videos, you need to consider integrating Computer vision algorithms into your workflow. First, understand the problem you are trying to solve, let’s say you are an automotive company and want to do depth estimation for autonomous driving experience. Here, we will be having data for driving cars. Check the data quality, use OpenCV or PIL to do some analysis. We can check what are the traditional computer vision algorithms that can be used for depth estimation, or if the model performance is low then go for neutral networks. We can make use of tensor flow or PyTorch to implement convolutions neural networks and then evaluate the models. Always estimate the value addition for your business.

There are excellent generic purpose Python libraries for Computer Vision. - OpenCV is a foundational library including various kinds of algorithms for Image Processing, some of them also using AI. - PIL / Pillow offers a great support for various kinds of images I/O. - scikit-image implements various algorithms for Image Processing There are then more specialized libraries. - HuggingFace Diffusers allows to interact with state of the art AI models for Image Generation using Stable Diffusion. - YOLO is the best and fastest available library to implement AI models for Object Detection, Image Segmentation, Instance Segmentation, Object Tracking etc.

To integrate Computer Vision AI into your workflow, consider using tools like OpenCV for real-time image processing, TensorFlow or PyTorch for advanced machine learning tasks, and cloud-based services like Google Cloud Vision or AWS Rekognition for pre-trained models and image analysis. The choice of tool depends on your project's needs, the technical expertise of your team, and available resources.

In Computer Vision , it depends on the problem you are working on , but generally I - personally - prefer to. first , think about the problem and try to brain storm some solutions second , search in literature about similar problems to what researchers reach to and what they have used to save the time of development. this is a very general point but it's very important.

One promising application for computer vision is agrotech. Employing a drone for aerial imaging, both in normal and thermal, opens opportunities for enhancing agricultural practices. Using Scikit-image for image preprocessing, and subsequently employing a pixel segmentation architecture in TensorFlow, allows the identification of areas in crops that require special attention. Take, for instance, crops such as corn, which exhibit distinct thermal properties and colours when afflicted by diseases. Through a pixel segmentation model, these variations can be accurately identified, effectively highlighting patches of crops indicative of disease. This information can be used for targeted interventions in the crop.

In my opinion, integrating AI tools like ThoughtSpot Sage, ChatGPT, Google Bard and Duet, Jasper, ChatSpot.ai by HubSpot, theGist, Fireflies.ai, Lavender.ai, Motion, and Frase.io into our business model is crucial. Their diverse capabilities, ranging from natural language processing for data-driven decisions to task automation, significantly boost productivity, efficiency, and innovation across our operations.

If I am looking to integrate AI to my workflow in shall inquire about the domain, the tasks i want to automate, The challenges I want to productionize, My level of expertise and budget?

It's surprising that many organizations haven't yet integrated GitHub Copilot into their development process. This tool is more than just an autocomplete; it's like a coding partner that guides you through laborious coding tasks . Developers often lose hours to repetitive tasks like setting up boilerplate code and writing tests, which diverts attention from more significant problems. Copilot frees up valuable time, enhancing not just personal efficiency but also speeding up the overall project delivery. This is especially beneficial for small teams or individual developers who aim to get more done with less especially on a tight budget.

Hi @linkedin, I would prefer not to comment on any of the subfields present in this article. I keep on receiving selections for AI/ML which is something I would keep my focus on. But not in CS as that is not something I have specialized in. Suppose there are electronics, instrumentation, AI, ML, and DSP articles. I am still considering it. Not something I don't know of.