Artificial intelligence (AI) is defying rule-based programming with expansive machine learning opportunities. As an emerging [AI development company](<a href="https://artificialintelligence.oodles.io/"> machine learning development services </a>...
Artificial intelligence (AI) is defying rule-based programming with expansive machine learning opportunities. As an emerging [AI development company]( machine learning development services "AI development company"), Oodles AI is constantly exploring AI technologies to build powerful and business-oriented machine learning models.
In this article, we are exploring the distributed streaming platform, Apache Kafka, that is gaining momentum among global digital businesses. Developed as a big data messaging platform, Kafka is now fulfilling the machine learning requirements of businesses with dynamic data processing capabilities. We have highlighted the most effective real-time applications of machine learning with Apache Kafka to automate business operations significantly.
Understanding Kafka and its Significance
Apache Kafka is an open-source distributed event streaming platform that facilitates storing, streaming and processing of real-time data. It was initiated by Linkedin in 2011 as a messaging queue that eventually transformed into a mainstream evert-streaming platform.
Today, more than 30% of the Fortune 500 companies use Apache Kafka including Uber, Airbnb, LinkedIn, Spotify, PayPal, etc.
Machine learning (ML) and Kafka together enable businesses to develop scalable analytical models that use real-time data streams of records. For this, Kafka runs in a cluster (Kafka Cluster) and provides five major APIs namely, Producer, Consumer, Streams, Connect, and Admin API.
Below are some benefits of building machine learning models with Apache Kafka-
a) Kafka serves as a central nervous system for ML models and creates a simplified data pipeline for training and analytics purposes.
b) Kafka enables businesses to channelize large volumes of real-time data ormachine learning with Apache Kafkamachine learning with Apache Kafka batches.
c) Kafka’s ecosystem supports a high-performing and scalable platform to develop business-critical projects.
d) Kafka’s infrastructure comes with high-throughput, low-latency, and maximum fault-tolerant advantages that enable businesses to build persistent and reliable ML models.
Business Applications of Machine Learning with Apache Kafka
With Apache Kafka, businesses are now able to exercise advanced machine learning development services to process real-time data streams. Kafka’s light-weight stream processing library called Kafka Streams enables businesses to provide near real-time (NRT) recommendations in the following manner-
a) ML models using Kafka collect and process customer interactions, impressions, content popularity metrics, attribution, and metadata to analyze consumer interests.
b) The system then processes the collected data in a step-by-step manner to provide personalized and timely recommendations for customers. Kafka’s high-throughput and long durability makes for a reliable platform that can be used across video streaming, eCommerce, and other businesses.
stream processing netflix kafka
Here’s how Netflix uses Kafka along with Spark to run its NRT video recommendation system.
Here’s how Kafka is able to handle user activity tracking pipeline-
a) Site activities such as page views, searches, preferred content, and other user actions are published to central topics of any website.
b) Kafka’s Streams API can then store and distribute the published content across applications and systems to deliver to users in real-time.
c) Businesses with minimum hardware infrastructure can process high volumes of website activity with Kafka for real-time analytics and business intelligence.
How does Oodles AI practice Machine Learning with Apache Kafka?
Oodles’ artificial intelligence team has hands-on experience in building and deploying machine learning models for multiple-industry businesses. Our AI capabilities include personalized recommendation systems, predictive analytics, natural language processing systems, and computer vision development services.
Our team has a working knowledge of deploying Apache Kafka for the following business applications-
a) Personalized recommendations for eCommerce, video streaming, and new reporting businesses. We use comprehensive machine learning libraries such as TensorFlow and OpenCV and Kafka’s Stream library to power stream processing.
b) Big Data Analytics with scalable bandwidth to handle large datasets for data modeling, reporting, and visualization using technologies like Hadoop and Spark.
Reach out to our AI team to learn more about our artificial intelligence services.
Learn Apache Kafka in 5 minutes. Learn the principles of Apache Kafka and how it works through easy examples and diagrams! What is Kafka? Kafka Features. Kafka Components. Kafka architecture. Installing Kafka. Why Learn Apache Kafka?
Learn the principles of Apache Kafka and how it works through easy examples and diagrams!
Machine Learning (ML) is one of the fastest-growing technologies today. ML has a lot of frameworks to build a successful app, and so as a developer, you might be getting confused about using the right framework. Herein we have curated top 5...
Machine Learning (ML) is one of the fastest-growing technologies today. ML has a lot of frameworks to build a successful app, and so as a developer, you might be getting confused about using the right framework. Herein we have curated top 5 machine learning frameworks that are cutting edge technology in your hands.
Through the machine learning frameworks, mobile phones and tablets are getting powerful enough to run the software that can learn and react in real-time. It is a complex discipline. But the implementation of ML models is far less daunting and difficult than it used to be. Now, it automatically improves the performance with the pace of time, interactions, and experiences, and the most important acquisition of useful data pertaining to the tasks allocated.
As we know that ML is considered as a subset of Artificial Intelligence (AI). The scientific study of statistical models and algorithms help a computing system to accomplish designated tasks efficiently. Now, as a mobile app developer, when you are planning to choose machine learning frameworks you must keep the following things in mind.
The framework should be performance-oriented
The grasping and coding should be quick
It allows to distribute the computational process, the framework must have parallelization
It should consist of a facility to create models and provide a developer-friendly tool
Let’s learn about the top five machine learning frameworks to make the right choice for your next ML application development project. Before we dive deeper into these mentioned frameworks, know the different types of ML frameworks that are available on the web. Here are some ML frameworks:
Linear algebra tools
Now, let’s have an insight into ML frameworks that will help you in selecting the right framework for your ML application.
Don’t Miss Out on These 5 Machine Learning Frameworks of 2019
TensorFlow is an open-source software library for data-based programming across multiple tasks. The framework is based on computational graphs which is essentially a network of codes. Each node represents a mathematical operation that runs some function as simple or as complex as multivariate analysis. This framework is said to be best among all the ML libraries as it supports regressions, classifications, and neural networks like complicated tasks and algorithms.
machine learning frameworks
This machine learning library demands additional efforts while learning TensorFlow Python framework. Your job becomes easy in the n-dimensional array of the framework when you have grasped the Python frameworks and libraries.
The benefits of this framework are flexibility. TensorFlow allows non-automatic migration to newer versions. It runs on the GPU, CPU, servers, desktops, and mobile devices. It provides auto differentiation and performance. There are a few goliaths like Airbus, Twitter, IBM, who have innovatively used the TensorFlow frameworks.
#2 FireBase ML Kit
Firebase machine learning framework is a library that allows effortless, minimal code, with highly accurate, pre-trained deep models. We at Space-O Technologies use this machine learning technology for image classification and object detection. The Firebase framework offers models both locally and on the Google Cloud.
machine learning frameworks
This is one of our ML tutorials to make you understand the Firebase frameworks. First of all, we collected photos of empty glass, half watered glass, full watered glass, and targeted into the machine learning algorithms. This helped the machine to search and analyze according to the nature, behavior, and patterns of the object placed in front of it.
The first photo that we targeted through machine learning algorithms was to recognize an empty glass. Thus, the app did its analysis and search for the correct answer, we provided it with certain empty glass images prior to the experiment.
The other photo that we targeted was a half water glass. The core of the machine learning app is to assemble data and to manage it as per its analysis. It was able to recognize the image accurately because of the little bits and pieces of the glass given to it beforehand.
The last one is a full glass recognition image.
Note: For correct recognition, there has to be 1 label that carries at least 100 images of a particular object.
#3 CAFFE (Convolutional Architecture for Fast Feature Embedding)
CAFFE framework is the fastest way to apply deep neural networks. It is the best machine learning framework known for its model-Zoo a pre-trained ML model that is capable of performing a great variety of tasks. Image classification, machine vision, recommender system are some of the tasks performed easily through this ML library.
machine learning frameworks
This framework is majorly written in CPP. It can run on multiple hardware and can switch between CPU and GPU with the use of a single flag. It has systematically organized the structure of Mat lab and python interface.
Now, if you have to make a machine learning app development, then it is mainly used in academic research projects and to design startups prototypes. It is the aptest machine learning technology for research experiments and industry deployment. At a time this framework can manage 60 million pictures every day with a solitary Nvidia K40 GPU.
#4 Apache Spark
The Apache Spark machine learning is a cluster-computing framework written in different languages like Java, Scala, R, and Python. Spark’s machine learning library, MLlib is considered as foundational for the Spark’s success. Building MLlib on top of Spark makes it possible to tackle the distinct needs of a single tool instead of many disjointed ones.
machine learning frameworks
The advantages of such ML library lower learning curves, less complex development and production environments, which ultimately results in a shorter time to deliver high-performing models. The key benefit of MLlib is that it allows data scientists to solve multiple data problems in addition to their machine learning problems.
It can easily solve graph computations (via GraphX), streaming (real-time calculations), and real-time interactive query processing with Spark SQL and DataFrames. The data professionals can focus on solving the data problems instead of learning and maintaining a different tool for each scenario.
Scikit-learn is said to be one of the greatest feats of Python community. This machine learning framework efficiently handles data mining and supports multiple practical tasks. It is built on foundations like SciPy, Numpy, and matplotlib. This framework is known for supervised & unsupervised learning algorithms as well as cross-validation. The Scikit learn is largely written in Python with some core algorithms in Cython to achieve performance.
machine learning frameworks
The machine learning framework can work on multiple tasks without compromising on speed. There are some remarkable machine learning apps using this framework like Spotify, Evernote, AWeber, Inria.
With the help of machine learning to build iOS apps, Android apps powered by ML have become quite an easy process. With this emerging technology trend varieties of available data, computational processing has become cheaper and more powerful, and affordable data storage. So being an app developer or having an idea for machine learning apps should definitely dive into the niche.
Still have any query or confusion regarding ML frameworks, machine learning app development guide, the difference between Artificial Intelligence and machine learning, ML algorithms from scratch, how this technology is helpful for your business? Just fill our contact us form. Our sales representatives will get back to you shortly and resolve your queries. The consultation is absolutely free of cost.
Author Bio: This blog is written with the help of Jigar Mistry, who has over 13 years of experience in the web and mobile app development industry. He has guided to develop over 200 mobile apps and has special expertise in different mobile app categories like Uber like apps, Health and Fitness apps, On-Demand apps and Machine Learning apps. So, we took his help to write this complete guide on machine learning technology and machine app development areas.
This Apache Kafka Tutorial - Kafka Tutorial for Beginners will help you understand what is Apache Kafka & its features. It covers different components of Apache Kafka & it’s architecture. You'll learn: What is Kafka? Kafka Features, Kafka Components, Kafka architecture, Installing Kafka, Working with Single Node Single Broker Cluster
This Apache Kafka Tutorial - Kafka Tutorial for Beginners will help you understand what is Apache Kafka & its features. It covers different components of Apache Kafka & it’s architecture. So, the topics which we will be discussing in this Apache Kafka Tutorial are:
Why Learn Apache Kafka?
Kafka training helps you gain expertise in Kafka Architecture, Installation, Configuration, Performance Tuning, Kafka Client APIs like Producer, Consumer and Stream APIs, Kafka Administration, Kafka Connect API and Kafka Integration with Hadoop, Storm and Spark using Twitter Streaming use case.