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Artificial Intelligence - Machine Learning
From automating workflows to predicting customer churn, Oodles’ Machine Learning Development Services help you tackle data complexities of today’s hyperconnected business landscape with precision. Backed by industry-leading frameworks including TensorFlow, PyTorch, and more, our ML solutions adapt and scale as you grow, combining seamless integration and continuous model improvement to deliver measurable ROI across every stage of your AI transformation journey.
From automating workflows to predicting customer churn, Oodles’ Machine Learning Development Services help you tackle data complexities of today’s hyperconnected business landscape with precision. Backed by industry-leading frameworks including TensorFlow, PyTorch, and more, our ML solutions adapt and scale as you grow, combining seamless integration and continuous model improvement to deliver measurable ROI across every stage of your AI transformation journey.
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Wellsite AI
Wellsite™ serves oil and gas operators and service companies, streamlining oilfield operations through automation, application integration and collaboration across the well lifecycle. They required model training, user and role management and payment gateway integration. Oodles provided these services, leveraging machine learning, SaaS, and High Chart for intuitive data visualization to navigate industry complexities successfully.
Technologies Involved
postgresql
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Kafka
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ANGULAR
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Kaboodle
The Kaboodle project transforms search experiences with Generative AI, offering text/image generation and text-to-speech services. Kaboodle sought Oodles’ expertise to integrate AI tools like ChatGPT, Mistral, and DALL-E. Oodles delivered a Google-like search bar and user authentication, creating a seamless, intuitive experience that enhances productivity and user satisfaction.
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SharpScout
SharpScout, an advanced online tool, utilizes Generative AI to connect research, professionals, and services across various sectors. To enhance its platform, SharpScout needed improvements in cloud infrastructure and AI integration. Oodles delivered a robust solution by incorporating state-of-the-art Generative AI technologies, significantly boosting SharpScout’s functionalities and optimizing the user experience.
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Viral Nation
Viral Nation, an influencer marketing agency, sought an advanced platform to streamline influencer campaigns and talent management. They needed a system to analyze data from multiple social media networks. Oodles developed a comprehensive solution, leveraging cloud infrastructure optimization. This platform simplifies influencer marketing for agencies, brands, and influencers by unifying data collection and analysis to extract valuable insights.
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SnapWorks revolutionizes education by enhancing communication between classrooms, teachers, students, and parents, creating seamless collaboration. They sought to enhance this with advanced tech solutions. Oodles provided a comprehensive platform using Machine Learning, SaaS, Mobile, and Web Development, and AWS. The platform empowered teachers, kept parents informed via mobile, and ensured students had access to assignments and updates.
Technologies Involved
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Features and Initial Setup of Pinecone DB in Node Js
Features of Pinecone DBScalability and PerformancePinecone is built for scalability, enabling developers to handle large volumes of data without sacrificing performance. It supports efficient vector indexing and search operations, making it possible to retrieve relevant items from massive datasets in milliseconds.Machine Learning IntegrationPinecone is designed with machine learning applications in mind. It allows for easy storage and retrieval of high-dimensional vectors, which are typically generated by machine learning models. This feature is particularly useful for implementing features like semantic search, personalized recommendations, and anomaly detection.Simple APIPinecone's API is straightforward and intuitive, allowing developers to easily add vector search capabilities into their applications.Real-Time UpdatesThe database supports real-time updates, allowing for the insertion, deletion, and modification of vectors without significant performance degradation. This feature is crucial for applications that require up-to-date information, such as live recommendation systems.Initial Setup of Pinecone DB in Node.jsSetting up Pinecone DB in a Node.js environment involves several steps, from creating an account to installing the necessary SDK. Here's how you can get started:Step 1: Create a Pinecone AccountFirst, you need to sign up for a Pinecone account. Visit the Pinecone official website and follow the registration process. Once your account is set up, you'll be able to access your API key, which is necessary for authenticating your Node.js application with Pinecone.Step 2: Install Node.js and NPMMake sure Node.js and npm (Node Package Manager) are installed on your machine. If not download it from the official Node.js website or install nvm to download Node.js. Node.js 12.x or later is recommended for compatibility with the Pinecone client library.Step 3: Install the Pinecone Client LibraryTo interact with Pinecone from your Node.js application, you need to install the Pinecone client library from NPM:npm install pinecone-clientStep 4: Initialize Pinecone in Your ApplicationOnce the client library is installed, you can initialize Pinecone in your Node.js application. Here's a basic example:const pinecone = require('pinecone-client');
// Configure the Pinecone environment
const config = {
apiKey: 'YOUR_API_KEY', // Replace with your actual API key
environment: 'us-west1-gcp' // Specify the Pinecone environment
};
pinecone.initialize(config);
// Now you can use Pinecone to insert vectors, query the database, etc.Step 5: Create a Vector IndexBefore you can start inserting vectors, you need to create a vector index. This can be done through the Pinecone dashboard or programmatically using the Pinecone client library.// Example: Creating a vector index programmatically
const indexName = 'example-index';
const dimension = 128; // Dimension of the vectors you'll be working with
pinecone.createIndex(indexName, dimension)
.then(() => console.log('Index created successfully'))
.catch(error => console.error('Error creating index:', error));Step 6: Inserting and Querying VectorsWith your vector index created, you're now ready to insert vectors into Pinecone and perform queries based on vector similarity.// Example: Inserting a vector
const vector = {/* your vector data here */};
pinecone.upsert(indexName, vector)
.then(() => console.log('Vector inserted successfully'))
.catch(error => console.error('Error inserting vector:', error));
// Example: Querying the index
const queryVector = {/* your query vector here */};
pinecone.query(indexName, queryVector, {topK: 5}) // Retrieve the top 5 similar vectors
.then(results => console.log('Query results:', results))
.catch(error => console.error('Error querying index:', error));ConclusionPinecone DB offers a powerful and scalable solution for integrating vector search into your Node.js applications. Its features are tailor-made for machine learning and similarity search applications, providing both performance and ease of use. By following the steps outlined in this guide, you can set up Pinecone in your Node.js environment and start leveraging its capabilities for your projects. Whether you're building a recommendation system, a semantic search engine, or any application that requires fast and efficient similarity search, Pinecone DB is a tool worth exploring.
Shubham Sharma
27 Feb 2024
How to integrate google analytics 4 with angular.
Google Analytics is a powerful tool that allows website owners to track their traffic and gather insights about their audience. Google Analytics 3 has been a popular tool for many years, but Google Analytics 4 is now available with enhanced features and an improved user experience. If you are currently using Google Analytics 3 and would like to migrate to Google Analytics 4.To integrate Google Analytics 4 with an Angular application, follow these steps:1. Create a new Google Analytics 4 property.You will need to create a new property in your Google Analytics account. Log in to your Google Analytics account and select “Admin” from the bottom left corner of the page. Under the “Property” column, click “Create Property” and select “Google Analytics 4.”Follow the setup wizard to get your measurement ID After creating your GA4 property, you will need to set up the tracking code on your website. The GA4 tracking code differs from the GA3 code. You can find the tracking code by clicking on “Data Streams” in the property column and selecting “Web” as the source. Follow the instructions provided to set up your tracking code. 2. Install the Google Analytics 4 gtag.js scriptIn your Angular project, open the index.html file and add the following code before the closing head tag:Replace GA_MEASUREMENT_ID with your measurement ID.3. Create a service for Google AnalyticsCreate a new file in your Angular project called google-analytics.service.tsAdd the following code:This service will allow you to send events to Google Analytics.4. Use the service to track eventsIn your Angular components or services, inject the GoogleAnalyticsService and call the event method to track events.This code will track a button click event with the category "UI" and label "my_button".That's it! With these steps, you should be able to integrate Google Analytics 4 with your Angular application and track events.
Area Of Work:
Machine Learning
Rajat Soni
30 Mar 2023
Image Augmentation using Keras
What is Image Augmentation?Image augmentation is a technique of modifying the existing data to create some new data for the model training process. It is the process of taking images that are already in a training dataset and manipulating them to create many altered versions of the same image. This increases the number of training images available, but it can also expose our classifier to a wider range of lighting and coloring conditions, strengthening it.When Should we use Image Augmentation?The input that a user will provide when working on image categorization projects might vary in numerous ways, including angles, zoom, and steadiness while clicking the photo. As a result, we should teach our model to take practically any input and understand it. In these situations when you are working with deep learning you'll find yourself in peculiar circumstances when there is not much data to train your model and gathering training data can be both frustrating and time-consuming. In times like this, image augmentation comes to rescue you.Advantages>>We can observe that the deep learning model's performance improves as the number of data increases.Every time, having a lot of data to feed the deep learning network is impossible. The issue with not having enough data is that the deep learning model may not be able to extract the patterns or functions from the data, which could affect how effectively it performs. This not only makes your model more resilient, but it also reduces overhead RAM! Let's take a closer look at how this class is useful for image augmentation.Different Image Augmentation TechniquesImage rotationImage shiftingImage flippingImage noisingImage blurringImage Brightnesszoom ImageImplementation## Installing Requirements (Keras Library)pip install keras## Importing Dependenciesfrom keras.preprocessing.image import ImageDataGenerator## Here we are applying the rules to generate data like its rotation range or if you want images to get flip or notgen = ImageDataGenerator(rotation_range=10, width_shift_range=0.1,height_shift_range=0.1, shear_range=0.15, zoom_range=0.1,channel_shift_range=10, horizontal_flip=False)## Expand the dimension of the image arrayimage = np.expand_dims(cv2.imread(image_path), 0)## Here we're applying the required image in the functionaug_iter = gen.flow(image)## We've created 10 images from one single image and getting them using the "next" keyword in a for loopaug_images = [next(aug_iter)[0].astype(np.uint8) for i in range(10)]## Now you can see and save every image using their indexplt.imshow(aug_images[5])
Area Of Work:
Machine Learning
,
Computer Vision
Manish Kumar
31 Jul 2022
An guide on how to generate payment link
What is the Payment link?
A payment link is a URL or QR code that takes customers to stand-alone web content created in very little time. A customer who clicks on a link is redirected to the checkout page where they will complete the transaction. you'll find a singular link for every good or service you're selling. One payment link can facilitate a transaction for one intended recipient, like an invoice would, up to an infinite number of shoppers.
Here we are using a Square_up payment gateway to create a payment link with an example of a request and response.
To Payment link, we have to call four APIs.
1- Create Customer API
2- Create Order API
3- Create Invoice API
4 - Publish Invoice API
Let's start with Api's request & response
1- Create Customer API - First of all we have to create a customer, for this, we need an idempotency key(this is auto-generated ) and token id(this is generated into the square_up account)
Request Parameter -
Response of Customer API.
2- Create Order API - In the order API we will create the payment amount as per the above customer purchase request for this we need a customer id (this is generated in the above API response) and the location id (this is generated into the square_up account)
Request parameter -
Response of order API -
3- Create Invoice API - in this API we will create an invoice number for this we need a token id, customer id, location id, and order id. (this all id's will generate in the above api's)
Request parameter -
Response of invoice api -
4- Publish invoice API- here we will find the payment link just by call the public invoice api.
Request
Response parameter -
Finally we will get the payment link - https://squareupsandbox.com/pay-invoice/inv:0-ChBzdE0qKxCgsM0OpIYN7T1IELMK
Area Of Work:
Machine Learning
Sonu Kumar
29 Jul 2022
A Brief Introduction to LSTM Model
What is LSTM?
LSTM (Long short-term memory) LSTM is an artificial recurrent Neural Network (RNN) capable of learning order dependence in sequence prediction problems. LSTM has feedback connections in contrast to standard feed-forward neural networks.
What is RNN?
RNN called a Recurrent Neural Network, is a class of Neural Networks. It is the first algorithm that remembers its input, due to internal memory, in other words, RNN uses sequential data and it is a generalization of feed-forward neural networks that has internal memory.
Why do we use LSTM?
Recurrent neural networks RNNs have a long-term dependency issue that LSTM networks were created expressly to address (due to the vanishing gradient problem). LSTMs, in contrast to more conventional feedforward neural networks, feature feedback connections. With this attribute, LSTMs may process whole data sequences, such as time series, without having to treat each data point separately. Instead, they can leverage the information from earlier data points in the sequence to help analyze later data points. Because of this, LSTMs excel at processing data sequences like text, audio, and general time series.
How LSTM Works?
LSTMs utilize several "gates" that regulate how data in a sequence enters, is stored in, and leaves the network. An output gate, an input gate, and a forget gate are the three gates that make up a standard LSTM.
Consider an LSTM cell
as visualized in the following
diagram imagine moving from
left to right.
Forget Gate
The first step is the forget gate. In this step, we will determine which pieces of the cell state—the network's long-term memory—are relevant in light of both the prior hidden state and the fresh incoming data.
Input Gate
The input gate and a new memory network are involved in the following step. This step aims to determine what new information should be added to the network's long-term memory (cell state), given the previous hidden state and new input data.
Note: The input gate and the new memory network are both neural networks in and of themselves, and they both accept identical inputs—the old hidden state and the new input data.
Output gate
The output gate decides the new hidden state. We'll employ three factors—the recently updated cell state, the previous hidden state, and the fresh input data—to make this determination.
Area Of Work:
Machine Learning
Manish Kumar
12 Jun 2022
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