machine-learning Updates

Uncover our latest and greatest product updates

The Business Case for Machine Learning and Deep Learning

Machine LearningMachine Learning uses algorithms to understand data, learn from it, and then make future prediction or forecasts. Machine Learning is a step ahead of Business Intelligence; it uses a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of historical data and algorithms that give it the ability to learn how to perform the task. Machine Learning came directly from the minds of the evolved from the early days of Artificial Intelligence.At a high level Machine Learning is of 2 types: Supervised and Unsupervised Learning. Some examples of Machine Learning Algorithm are Linear Regression, Decision Tree, Clustering, Reinforcement Learning, Bayesian Networks and many more.The Business ChallengeLoan Distribution was a principal offering of the Bank. The major earnings of the Bank come from Loans disbursed and the interest earned. The Bank offered personal and company loans. The Bank wanted to reduce the credit risk and the defaults in loan repayment.Aziro (formerly MSys Technologies) Predictive Analytics ApproachWe proposed predictive analytics solution to measure the probability that a debtor will default. Our objective was to include the score of probability for defaulting as a key component in getting the loans. The score was used as a measure for credit risk of the potential loan customer.After analyzing the business problem, our focus was on two model types to measure the credit risk:Logistic RegressionDecision TreesA sample data of Model Scoring and Evaluation to decide which Model to select in Machine Learning Deep Learning“The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.”– Andrew Ng (source: Wired)Deep Learning has evolved from Artificial Neural Networks which was a form of Machine Learning. Neural Networks are inspired by our understanding of the biology of our brains – all those interconnections between the neurons. But, unlike a biological brain where any neuron can connect to any other neuron within a certain physical distance, these artificial neural networks have discrete layers, connections, and directions of data propagation.Deep Learning is used in Image Processing. For example, in image processing, an image is broken into a bunch of tiles that are inputted into the first layer of the neural network, which in turn passes the data to a second layer. The second layer of neurons does its task, and so on, until the final layer and the final output is produced.In earlier days Natural Language Processing(NLP) used statistical methods and Machine Learning. Now NLP is using neural network methods of Deep Learning.An example of Natural Language Processing(NLP) using Deep Learning is Chat Bots. Chat Bot uses NLP deep learning model to better understand the meaning and context of a message or email or a support ticket sent by customer. We expect to see even more innovative applications of NLP using deep learning in the near future, and expect the machines to bring better customer service as a result.Following is a CHAT BOT architecture using Natural Language Processing using Machine Learning; and the Diagram Below shows NLP using Deep Learning:

Aziro Marketing

fintech

Machine Learning- Why It Is the Future of Technology

Recently, an article by Forbes enlisted the top 10 technologies set to drive the technology industry in the year to come. “Machine learning” a relatively new concept is based on the theory of pattern recognition and computational learning in artificial intelligence. It is fast catching up and taking the tech industry by storm. Machine learning has been named as “the future” that will be the normal way to function very soon. It’s important to delve into what this technology is and how it can be of use to us. Very simply put, machine learning is nothing but the phenomenon of computers learning from experience; using algorithms that repeatedly absorb from data, machine learning enables computers to discover hidden insights without being clearly programmed to do so. Machine learning is a very practical application that reaps the true business benefits in any given set up which is saving on time and money. Tasks are now being handled by virtual assistant solutions that earlier required a person who managed activities with passwords, etc. This enables human efforts to be diverted to better and critical areas that can raise customer satisfaction and render the organization in a competitive space. Thinking logically, there are certain factors that contribute in the excellent functioning of machine learning – the technology. Massive availability of data and the exceptional power of computation render this technology incomparable. With the availability of humongous data and help of IoT in today’s world, many algorithms with patterns and combinations are devised to throw up intelligent inferences just like the human brain. This is facilitated with the advancement of computer hardware that is now able to perform way more complex computations in a matter of nano seconds with accurate results each time, every time. A human brain is intelligent but sometimes lacks the ability to retain such massive data. That is where machine learning takes over. Machine learning encompasses many complex learning models with zillions of parameters that analyze and interpret data in seconds. We see a plethora of applications bursting from this technology thus giving the world a genius AI and making life easy for the inhabitants of this planet. Machine learning is one technology that has the potential to dim the line between science and dream.

Aziro Marketing

internet-of-things iot machine-learning

MLOps on AWS: Streamlining Data Ingestion, Processing, and Deployment

In this blog post, we will explore a comprehensive architecture for setting up a complete MLOps pipeline on AWS with a special focus on the emerging field of Foundation Model Operations (FMOps) and Large Language Model Operations (LLMOps). We’ll cover everything from data ingestion into the data lake to preprocessing, model training, deployment, and the unique challenges of generative AI models.1. Data Ingestion into the Data Lake (Including Metadata Modeling)The first step in any MLOps pipeline is to bring raw data into a centralized data lake for further processing. In our architecture, the data originates from a relational database, which could be on-premise or in the cloud (AWS RDS for Oracle/Postgres/MySQL/etc). We use AWS Database Migration Service (DMS) to extract and replicate data from the source to Amazon S3, where the data lake resides.Key points:AWS DMS supports continuous replication, ensuring that new data in the relational database is mirrored into S3 in near real-time.S3 stores the data in its raw format, often partitioned by time or categories, ensuring optimal retrieval.AWS Glue Data Catalog is integrated to automatically catalog the ingested data, creating metadata models that describe its structure and relationships.The pipeline ensures scalability and flexibility by using a data lake architecture with proper metadata management. The Glue Data Catalog also plays a crucial role in enhancing data discoverability and governance.2. Data Pre-Processing in AWSOnce the data lands in the data lake, it undergoes preprocessing. This step involves cleaning, transforming, and enriching the raw data, making it suitable for machine learning.Key AWS services used for this:AWS Glue: A fully managed ETL service that helps transform raw data by applying necessary filters, aggregations, and transformations.AWS Lambda: For lightweight transformations or event-triggered processing.Amazon Athena: Allows data scientists and engineers to run SQL queries on the data in S3 for exploratory data analysis.For feature management, Amazon SageMaker Feature Store stores engineered features and provides consistent, reusable feature sets across different models and teams..3. MLOps Setup to Trigger Data Change, ML Model Change, or Model DriftAutomating the MLOps process is crucial for modern machine learning pipelines, ensuring that models stay relevant as new data or performance requirements change. In this architecture, MLOps is designed to trigger model retraining based on:New data availability in the data lake (triggered when data changes or is updated).Model changes when updates to the machine learning algorithm or training configurations are pushed.Model drift when the model’s performance degrades due to changing data distributions.Key services involved:Amazon SageMaker: SageMaker is the core machine learning platform that handles model training, tuning, and deployment. It can be triggered by new data arrivals or model performance degradation.Amazon SageMaker Model Monitor: This service monitors deployed models in production for model drift, data quality issues, or bias. When it detects deviations, it can trigger an automated model retraining process.AWS Lambda & Amazon EventBridge: These services trigger specific workflows based on events like new data in S3 or a drift detected by Model Monitor. Lambda functions or EventBridge rules can trigger a SageMaker training job, keeping the models up to date.By leveraging this automated MLOps setup, organizations can ensure their models are always performing optimally, responding to changes in the underlying data or business requirements.4. Deployment PipelineAfter the model is trained and validated, it’s time to deploy it for real-time inference. This architecture’s deployment process follows a Continuous Integration/Continuous Deployment (CI/CD) approach to ensure seamless, automated model deployments.The key components are:AWS CodePipeline: CodePipeline automates the build, test, and deployment phases. Once a model is trained and passes validation, the pipeline pushes it to a production environment.AWS CodeBuild: This service handles building the model package or any dependencies required for deployment. It integrates with CodePipeline to ensure everything is packaged correctly.Amazon SageMaker Endpoints: The trained model is deployed as an API endpoint in SageMaker, allowing other applications to consume it for real-time predictions. It also supports multi-model endpoints and A/B testing, making deploying and comparing multiple models easy.Amazon CloudWatch: CloudWatch monitors the deployment pipeline and the health of the deployed models. It provides insights into usage metrics, error rates, and resource consumption, ensuring that the model continues to meet the required performance standards.AWS IAM, KMS, and Secrets Manager: These security tools ensure that only authorized users and applications can access the model endpoints and that sensitive data, such as API keys or database credentials, is securely managed.This CI/CD pipeline ensures that any new model or retraining job is deployed automatically, reducing manual intervention and ensuring that the latest, best-performing model is always in production.5. FMOps and LLMOps: Extending MLOps for Generative AIAs generative AI models like large language models (LLMs) gain prominence, traditional MLOps practices must be extended. Here’s how FMOps and LLMOps differ:Data Preparation and LabelingFor foundation models, billions of labeled or unlabeled data points are needed.Text-to-image models require manual labeling of pairs, which Amazon SageMaker Ground Truth Plus can facilitate.For LLMs, vast amounts of unlabeled text data must be prepared and formatted consistently.Model Selection and EvaluationFMOps introduce new considerations for model selection, including proprietary vs. open-source models, commercial licensing, parameter count, context window size, and fine-tuning capabilities.Evaluation metrics extend beyond traditional accuracy measures to include factors like coherence, relevance, and creativity of generated content.Fine-Tuning and DeploymentFMOps often involve fine-tuning pre-trained models rather than training from scratch.Two main fine-tuning mechanisms are deep fine-tuning (recalculating all weights) and parameter-efficient fine-tuning (PEFT), such as LoRA.Deployment considerations include multi-model endpoints to serve multiple fine-tuned versions efficiently.Prompt Engineering and TestingFMOps introduces new roles like prompt engineers and testers.A prompt catalog is maintained to store and version control prompts, similar to a feature store in traditional ML.Extensive testing of prompts and model outputs is crucial for ensuring the quality and safety of generative AI applications.Monitoring and GovernanceIn addition to traditional model drift, FMOps require monitoring for issues like toxicity, bias, and hallucination in model outputs.Data privacy concerns are amplified, especially when fine-tuning proprietary models with sensitive data.Reference ArchitectureConclusionThe integration of FMOps and LLMOps into the MLOps pipeline represents a significant evolution in how we approach AI model development and deployment. While the core principles of MLOps remain relevant, the unique characteristics of foundation models and LLMs necessitate new tools, processes, and roles.As organizations increasingly adopt generative AI technologies, it’s crucial to adapt MLOps practices to address the specific challenges posed by these models. This includes rethinking data preparation, model selection, evaluation metrics, deployment strategies, and monitoring techniques.AWS provides a comprehensive suite of tools that can be leveraged to build robust MLOps pipelines capable of handling both traditional ML models and cutting-edge generative AI models. By embracing these advanced MLOps practices, organizations can ensure they’re well-positioned to harness the power of AI while maintaining the necessary control, efficiency, and governance.

Aziro Marketing

Revolutionizing Industries: The Power of Image Recognition in 2023 and beyond

IntroductionThe field of image recognition has been at the forefront of the exponential growth in technology that we’ve seen in recent years. At a compound annual growth rate (CAGR) of 17.4% from 2020 to 2025, the worldwide image recognition market is forecast to be worth $38.2 billion in 2025. This state-of-the-art AI technology has quickly spread across a wide range of industries, reshaping practices, boosting output, and providing better service to end users. In this blog post, we’ll look at how image recognition already creates waves across various sectors and transforms the business world.What is Image Recognition?Image recognition is a subfield of computer vision that includes instructing computers to read and comprehend images. It’s a method for teaching computers to “see” and understand visual content like people do. Machine learning algorithms that examine visual data for patterns, shapes, and characteristics do this.Identifying objects in images is the focus of the computer vision subfield known as image recognition. It’s a fast-expanding industry with several potential uses, including autonomous vehicles and medical diagnosis.An image is initially dissected into its component pixels by image recognition algorithms. After collecting this data, the system will examine the patterns inside the pixels to see whether they match any recognized items. The term “feature extraction” is commonly used to describe this process.The system can perform object classification after it has recognized its characteristics. This is accomplished by comparing the object’s attributes to those of other items in a database.A wide range of items can be taught to image recognition systems. Training the algorithm with more data will improve its accuracy.Source:Great LearningHow does Image Recognition work?Typically, one of two methods is used by image recognition systems:1. Traditional methods:These ways of identifying things use details that were made by hand. People make hand-crafted features to fit the task at hand.2. Machine learning:These approaches use machine learning algorithms to learn the features essential to the job naturally. Machine learning methods are becoming more and more popular because they can learn to recognize objects more correctly than traditional approaches.Now, let’s look at how image recognition is revolutionizing the various industries:1. Health care: a precise diagnosis can save livesNew diagnostic tools and therapeutics are being created with image recognition, which is predicted to grow the medical imaging market to $320.8 billion by 2025. Image recognition is starting to change the way treatment is done. It helps doctors diagnose diseases with an accuracy that can’t be beaten. AI-driven picture recognition is saving lives by giving early and accurate assessments. For example, it can find tumors in X-rays and abnormalities in pathology slides.2. Retail: Changing the way people shopIn the retail world, picture recognition makes in-store and online shopping much more personal. Smart shelves can tell when a product is running low and restock it; visual search makes it easy for customers to find what they want. With virtual try-on, you don’t have to guess when you buy clothes online, which makes customers happier.3. Self-driving cars will make the road saferImage recognition is a crucial technology being utilized in the development of self-driving automobiles, which is predicted to grow to a market size of $86.6 billion by 2025. Image recognition is the key to self-driving cars for the auto business. The eyes of these cars are cameras and sensors that use image recognition to see where they are going, find barriers, and make sure the trip is safe. Picture recognition is the way to go as we move towards self-driving cars.4. Agriculture: Transforming the way crops are managedImage recognition powered by AI is making crop control better in agriculture. Drones with cameras take pictures of farms, which can be used to find diseases and pests in real-time. This makes it possible to make exact changes, cutting down on harmful chemicals and increasing food yields.5. Security: Making safety betterImage recognition technology helps security systems all over the world. The worldwide security market was worth USD 119.75 billion in 2022, and it is anticipated to expand at a CAGR of 8.0% from 2023 to 2030. The proliferation of security systems may be attributed to the growth in criminal activity, terrorism, fraudulent schemes worldwide, and stricter regulatory regulations. Face recognition, finding objects, and finding unusual things make places safer. This technology keeps us safe from airports to houses by letting us know who is around and who might be a threat.6. E-commerce: A Revolution in the Way We ShopVisual shopping is changing the way people shop online. Consumers can find goods by taking pictures of them thanks to image recognition, which drives visual search. Product tagging makes online shopping more accessible, and virtual try-ons for clothes and items improve the user’s experience.7. Content Moderation: Making the Internet a Safe PlaceImage recognition is increasingly used to moderate material on social media apps and websites. This technology instantly finds and eliminates dangerous or inappropriate material, making the Internet safer for people of all ages.8. Protecting the environment: helping with conservation effortsImage recognition helps keep the world healthy. It helps keep track of the number of animals, find criminal trapping, and measure deforestation. AI-powered systems that can discover reusable materials also make it easier to get responsibly rid of trash.9. Accessibility: Making everyone feel welcomeImage recognition is one of the most essential parts of making the digital world easier to use. It turns the words in pictures into speech, so people who can’t see can still get information. Object recognition apps help with everyday jobs by figuring out what things are in real-time.10. Problems and ethical things to think aboutAs picture recognition is increasingly used, problems with bias, privacy, and data protection must be solved. For AI to reach its full potential, it is crucial to ensure its methods are fair and safe.ConclusionImage recognition is more than just a technology tool in 2023 and beyond. It’s a driving force of progress, transforming whole sectors while raising productivity and bettering people’s lives. We must prioritize ethical issues and data protection as we embrace the ever-expanding capabilities of image recognition to guarantee that these developments will be used for society’s greater good. We may look forward to a future where image recognition continues to give us agency, ushering in more intelligent, secure, and individually tailored interactions in various fields.Aziro (formerly MSys Technologies): Facilitating Your Organization’s Digital Evolution Here at Aziro (formerly MSys Technologies), we firmly believe in the game-changing potential of tools like image recognition. Our digital services are made to help companies of all sizes and in all industries take advantage of cutting-edge technologies like image recognition. Our team of professionals is here to assist you with all aspects of digital transformation, from designing user-friendly interfaces to expanding your data resources.We’re here to help your company become more responsive to market changes, data-driven, and capable of producing intelligent, scalable solutions. Our extensive digital offerings include everything you need, including mobility, analytics, the Internet of Things, artificial intelligence/machine learning, and big data.Are you prepared to speed up your transition into the technological future? Contact us at marketing@aziro.com right away so we can begin discussing the opportunities that await us.

Aziro Marketing

EXPLORE ALL TAGS

Ansible Test Automation

application containerization

applications

Application Security

application testing

artificial intelligence

asynchronous replication

business intelligence

Cloud Cost Optimization

cloud devops

Cloud Infrastructure

Cloud Interoperability

Cloud Native Solution

Cloud Storage Security

Codeless Automation

Cognitive analytics

Configuration Management

container world conference

continuous-delivery

continuous deployment

continuous integration

data backup and recovery

Descriptive analytics

Descriptive analytics tools

Digital Transformation

dockercon 2019 san francisco

end-to-end-test-automation

High Performance Computing

icinga for monitoring

Image Recognition 2024

kubernetesday bangalore

Low-Code No-Code Platforms

mobile-application-testing

mobile-automation-testing

Predictive analytics tools

prescriptive analysis

Rapid Application Development

raspberry pi

RDMA

real time analytics

realtime analytics platforms

Real-time data analytics

Recovery

Recovery as a service

recovery as service

rsa

rsa 2019

rsa 2019 san francisco

Selenium Test Automation

selenium testng

serverless

Serverless Computing

Site Reliability Engineering

software defined storage

software-testing

software testing trends

software testing trends 2019

storage virtualization

support

Synchronous Replication

testing automation tools

thought leadership articles

trends

tutorials

ui automation testing

ui testing

ui testing automation

vCenter Operations Manager

vmworld 2019 san francisco

VMworld 2019 US

vROM

Web Automation Testing

web test automation

WFH

LET'S ENGINEER

Your Next Product Breakthrough

Book a Free 30-minute Meeting with our technology experts.

Aziro has been a true engineering partner in our digital transformation journey. Their AI-native approach and deep technical expertise helped us modernize our infrastructure and accelerate product delivery without compromising quality. The collaboration has been seamless, efficient, and outcome-driven.

CTO

Fortune 500 company