AI & ML in Logistics

May 9, 2022

Logistics Industry being transformed by Artificial Intelligence & Machine Learning. 

We witness fast technical advancements every day in the domains of big data, algorithmic development, cloud GPUs, networking, and processing power. These new technologies have improved the performance, accessibility, and cost-effectiveness of Artificial Intelligence & Machine Learning more than ever before.

Even the role of AI and ML in the logistics industry can not be neglected. Initially, the evolution of logistics was governed by automation, from the steam engine through the forklift to today's modern robotic pickers and packers.

It’s no surprise that organizations now have begun to recognize the potential of machine learning and the benefits of leveraging Artificial Intelligence to boost production and delivery.

How AI and ML are changing logistics?

With the present unplanned problems, leveraging evolving logistics technologies such as AI and ML is becoming increasingly important and when it comes to the advantages of AI and ML in logistics, there is never a lack of convincing reasons.

Many logistics organizations are using Artificial Intelligence to extract critical information such as invoicing amounts, account information, dates, addresses, and parties involved from a pool of unstructured invoice forms received by the company. These are the factors that prompted businesses to use Artificial Intelligence (AI) in order to provide better service.

Now, let's look at some of the advantages of artificial intelligence in logistics.

Optimized large distribution networks

Warehouse and manufacturing facilities are using AI-powered robots to solve the vast complexity of product flows. To observe their surroundings, recognize objects, navigate independently, and move things, these robots use strong deep neural networks for perception, segmentation, and position estimation. Market leaders are increasing throughput, per-order customization, and distinct client experiences to broaden their competitive edge.

The supply chain gains consciousness as a result of GPU-powered AI technologies. Operations become more efficient, process throughput accelerates, and warehouse robots provide end-to-end visibility, enhancing the accuracy of orders selected, packaged, and sent using sophisticated video analytics, robotics, automation, and management.


Better Demand Prediction

By gathering more accurate data, AI can perform detailed analytics, network analysis, and capacity estimation, while also incorporating more relevant external factors into its information extrapolation—better estimating upcoming demand and assisting logistics technology companies in embracing proactivity, giving them a competitive advantage in the transportation sector and reducing wasteful expenditures.

AI and ML can also help the organization to gather information from online chats and identify probable material shortages, access concerns, and supplier statuses. With the capacity to precisely forecast what's coming, logistics businesses can stay ahead of the demand curve.

Enhanced Customer Experience

Customers are interacted with by logistics businesses in a number of ways, the most important of which is package tracking and status updates. Customers may use chatbots powered by artificial intelligence that can handle a wide range of common customer service concerns without requiring human intervention. Customers benefit from faster response times and more helpful service, while the logistics firm saves money.

Another benefit of AI in logistics is personalized product suggestions based on consumers shopping history and personal preferences. As a result, consumers who receive more personalized service become more loyal to the company.

Efficient Planning and Resource Management

AI algorithms assist businesses in the use of predictive capacity matching. The use of such AI models may improve fleet performance visibility for logistic companies,  assist planners and logistics experts in strategically positioning their assets, and protect them against excessive risk by optimizing commodity transportation along with the distribution network in real-time.

Real-Time Route Optimization

AI-powered software can estimate demand by asset type and location up to 12 weeks in advance, allowing logistics businesses to better route trains and cars, maximize storage on ocean cargoes, and perhaps prevent bottlenecks at ports or typical manufacturing delays.

Many companies have begun to adopt self-driving delivery methods that use real-time shortest path algorithms to deliver things fast and without the need for human labor.

Warehouse Automation

Artificial Intelligence is revolutionizing warehouse activities such as data collection and analysis. AI is helping in more efficient inventory management, allowing businesses to enhance efficiency and income. AI is used in warehousing to estimate demand, adjust orders, and re-route items in transit. You may change your orders based on these forecasts and have in-demand items delivered to local warehouses as required. 

AI can even quickly connect with numerous warehouses to determine the optimal alternative for moving the cargo.

Sales and Marketing Optimization

Artificial Intelligence can help the sales and marketing team in lead scoring. AI systems may use data from hundreds of sales reps allowing them to focus on the correct prospects by identifying the elements that improve a prospect's chance to buy or make a purchase. AI can also help in increasing automation in tasks like email marketing, and other marketing efforts to increase sales.

Product Inspection

The application of computer vision technology in warehousing enables the recognition and organization of things. This sort of technology will be used in the future to perform quality control and reduce the need for human monitoring. AI identifies damaged goods and analyses the kind and depth of damage using computer vision technology, allowing you to prevent future harm and take action. AI systems rely on real-time data from sensors to do this.

Back-office Automation

The logistics sector relies heavily on back-office activities. Combining artificial intelligence (AI) with robotic process automation (RPA) has resulted in cognitive automation. AI, when combined with RPA, allows people to improve their performance by enhancing productivity and accuracy. Many back-office processes, including billing, email processing, scheduling, labor management, and other manual office operations, benefit greatly from AI. 


AI has several applications in the supply chain and logistics industries, as discussed above. Companies all across the globe are using Artificial Intelligence in their daily operations. AI and ML aid in the acceleration and simplification of a variety of critical procedures. It provides you with a competitive edge and ushers in a profitable and cost-effective transition.

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June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

Reference Links:,product%20on%20the%20right%20platform.

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June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.


The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.


In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.


Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:


Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 


As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online


As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 


Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.



Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 


An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.


In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.




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June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

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