CLIP From OpenAI Recognizes Images From Their Captions

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January 7, 2021

OpenAI’s Contrastive Language–Image Pre-training (CLIP) learns image representation from associated natural language. The intuition is to learn how to recognize a wide variety of visual concepts in images and associate them with their names. Thus, no preferential fine-tuning is required for many downstream tasks while benchmarking against the current state-of-the-art.

The researchers from OpenAI did not use curated labeled training data for CLIP. Instead, they obtained training data (400 million images and their captions) from the internet that are highly varied and highly noisy. Hence, it is a complete departure from the prevalent practice of using standard labeled datasets to train computer vision models that specialize in only one task.

The researchers used a simplified version of ConVIRT architecture. To make CLIP efficient, they adopted a contrastive objective for connecting text with images. The training objective was to predict a caption from 32,738 random ones, which is the correct one for a given image. After pre-training, natural language is used to reference learned visual concepts, enabling the model’s zero-shot transfer to downstream tasks.

Also Read: OpenAI Releases Robogym, A Framework To Train Robots In Simulated Environments

The pre-training distilled down to competitive performance on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. This result suggests that the zero-shot evaluation of task-agnostic models is much more representative of a model’s capability.

The most crucial part is the robustness of the neural net against adversarial data. Since the model is not directly optimized for the benchmarks, it learns much more rich representations that make it adversarially robust.

Even if the model seems to be versatile, it has the following limitations as reported by the researchers are:

It struggles with more abstract or systematic tasks such as counting the number of objects in an image and more complex tasks such as predicting the spatial distance
Zero-shot CLIP struggles with very fine-grained classification, such as telling the difference between car models, variants of aircraft, or flower species
CLIP poorly generalizes to images absent in its pre-training dataset
CLIP’s zero-shot classifiers can be sensitive to wording or phrasing and sometimes require trial and error “prompt engineering” to perform well.

To learn more about CLIP, have a look at the paper and the released code.

Computer Vision Has A New DeIT By Facebook

By

Pradyumna Sahoo

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January 5, 2021

Facebook AI introduces DeIT (Data-efficient Image Transformer), a Transformer-based approach to train computer vision models. Over the years, Transformer has led to several breakthroughs in NLP, but the use of Transformer for image processing has been a new advancement of late. The idea behind Transformer is to move away from the popularly used image processing techniques like Convolutional Neural Networks as the new technique delivers exceptional results while decreasing the need for data and computation.

On 3 December 2020, Google also released a Transformer-based image processing technique — Visual Transformer (ViT), achieving state-of-the-art results on image classification by obtaining superior accuracy on the ImageNet dataset. They used external data that amounts to 300 million training images, which is yet to be released. But within mere 20 days, researchers from Facebook published DeIT, which was trained on a single 8-GPU node in two to three days (53 hours of pre-training, and optionally 20 hours of fine-tuning) with no external data.

Researchers from FAIR built upon Visual Transformer (ViT) architecture from Google and used patch embeddings as input. But, they introduced a new transformer-specific knowledge distillation procedure based on a distillation token that brought down the training data requirement significantly compared to ViT.

Also Read: Top Image Processing Libraries In Python

It seems that Google Brain and FAIR researchers are trying to one-up each other. The ground details are below.

DeIT achieved competitive results against the state-of-the-art on ImageNet. When the pre-trained model was fine-tuned for fine-grained classification on several popular public benchmarks like CIFAR-10, CIFAR-100, Flowers, Stanford Cars and iNaturalist-18/19, it managed to secure the 2nd position in terms of classification accuracy in iNaturalist-18/19 with competitive scores in the rest of them.

Currently, the FAIR team has released three models with varying numbers of parameters:-

The reported tricks used to achieve such a feat are Knowledge distillation, Multi-head Self Attention layers (MSA) [with heads = 3,6,12] and certain standard image augmentation techniques like Auto-Augment and Random-Augment. They also used ADAMW optimizer and regularization techniques like Mixup and Cutmix to improve performance.

Check out the paper here and the released code here.

OpenMined, In Collaboration With PyTorch, Introduces A Free Course Of “The Privacy AI Series”

By

Pradyumna Sahoo

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January 4, 2021

OpenMined has released a course to train next-generation machine learning enthusiasts and practitioners to process sensitive data without breaching privacy. OpenMined is well known as a community focussed on developing tools and frameworks for AI that can work with data that can not be pooled centrally for privacy concerns. This course is a part of their collaboration with PyTorch to offer four free courses (The Privacy AI Series) on machine learning with privacy-preserving techniques.

Currently, there are four courses planned to be offered — Our Privacy Opportunity, Foundations of Private Computation, Federated Learning Across Enterprises, and Federated Learning on Mobile.

At present, OpenMined has released the first course “Our Privacy Opportunity. The course is being offered free of cost along with a completion certificate. The best part is that you will be working on real-world projects while being mentored by world-class researchers with names ranging from Andrew Trask, PhD Researcher at the University of Oxford, Cynthia Dwork, author of Differential Privacy, Harvard, Ilya Mironov, author of Renyi Differential Privacy, FAIR, and more.

The course is aimed at dealing with current privacy infrastructures, their limitations, and building the foundations for upcoming courses on federated learning. As per the course design, it will take you around the privacy-transparency tradeoff and teach you about the principles of privacy. Moreover, the first course requires you to only invest a little over seven hours. At the end of the course, you will be able to come up with privacy product specifications on your own.

The course has been structured for beginners and hence, assumes no prerequisites. It begins by defining information flow, then puts lights on failures in terms of privacy and transparency in the information structure. After exposing the lacunas of current information flow designs, the course builds upon structured transparency and its impact.

Register for the first course of The Private AI Series here.

PURE EV Develops Next-Gen AI System To Automatically Resolve Defects In Lithium-Ion Batteries Of Electric Vehicles

By

Kenny Manuel

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January 4, 2021

IIT Hyderabad-incubated Startup PURE EV has developed an Artificial Intelligence-driven hardware that automates identification and repair of defects in Lithium-Ion Batteries of Electric Vehicles. This next-generation technology completely does away with the requirement for customers to visit Service Centers to address battery defects.

PURE EV Researchers have designed Artificial Neural Network (ANN)-based algorithms for the system called ‘BaTRics Faraday,’ which identify the defects in various series in the battery and also auto heals them to the best of the electro-chemistry potential of the cells. This process is fully automated by the hardware and no manual intervention is required all the way till complete capacity restitution.

Extensive field testing of ‘BaTRics Faraday’ has already been completed. This system can be used for all five Two-Wheelers models launched by PURE EV (Epluto 7g, Etrance Neo, Etrance, Egnite & Etron+). This system will be rolled out in the first quarter of 2021.

Elaborating about this system, Dr. Nishanth Dongari, Founder, PURE EV, and Associate Professor, Department of Mechanical and Aerospace Engineering, IIT Hyderabad, “Lithium batteries are the most critical component of electric vehicles. They contain multiple lithium cells welded together in series and parallel arrangements to meet the desired voltage and ampere-hour (Ah) capacity. In case of any defects coming to batteries in any of the cell series, it leads to significant downtime to the EV owners. Additionally, the usage behavior pattern and Indian environmental conditions put more load on the batteries. It is, indeed, a very difficult task for battery OEM to get the defects rectified through diagnosis and replacement of defective series of cells.”

Further, Dr. Nishanth Dongari said, “Hence the need of the hour is to develop an external intelligent hardware device which resolves battery defects through an external healing process. PURE EV has developed AI-driven hardware which carries out the diagnosis and resolution of defects in the battery through an externally-connected device to the battery. This saves precious man-hours otherwise spent in replacement of series of cells and significantly reduces the battery ‘Turn Around Time’ (TAT).”

Lithium batteries repair is a challenging task currently in the market as the supply chains are not well established and reputed OEMs (Original Equipment Manufacturers) are not yet operational in this emerging segment. Electric Vehicle Customers are reliant on EV OEMs who, in turn, are reliant on battery OEMs.

In such a scenario any innovation that gives assurance to prospective customers stakeholders on the lower turnaround time will significantly boost their confidence level on emerging this technology. It is imperative for the electric vehicles to become mainstream that innovative solutions like ‘BaTRics Faraday’ come to market which ensures that the battery does not turn into an idling asset for EV owners

Highlighting the need for this technology, Mr. Rohit Vadera, Chief Executive Officer, PURE EV, said, “This intelligent device enhances the capability of PURE EV to turn around battery defects within a shorter time period. With the significant takeoff happening for EVs, PURE EV is building the necessary infrastructure and technical capabilities to become a pioneer in battery after-sales service for its esteemed customers.”

Mr. Rohit Vadera added, “PURE EV will be able to provide service in much lesser TAT and with the establishment of company-owned workshops pan India we intend to emerge as a reputed benchmark in the battery after-sales service standards.”

Currently, PURE EV has made this device operational based out of its factory. In future, PURE EV plans to establish company-owned high-end ‘Battery Diagnostics and Repair’ workshops at major demand centers across India. Such workshops will ensure a significantly lower TAT for battery repair across various geographies and the company deems this as one of the critical steps in the vision to become an established name across pan India in the EV space.

PURE EV has an in-house battery manufacturing facility and a research setup based out of IIT Hyderabad campus where the company’s dedicated R&D team works on core areas of battery thermal management system for development of long-range and high-performance Lithium batteries.

Google Cloud Is Offering Free Training On AI, Big Data, & More

By

Ratan Kumar

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December 31, 2020

Google Cloud is offering free training on in-demand skills like AI, Analytics, Kubernetes, and more through its Qwiklabs. On 9 December, Google Cloud extended the offer of no-cost training and allowed users to register by 31 December 2020 to avail of the offer.

Google Cloud has been helping people navigate through the difficult times due to the pandemic by allowing learning to get started with the technologies. Since the lockdown, cloud providers have witnessed a steep rise in the adoption of cloud computing.

Looking at the recent trends, in 2021 organizations would require professionals who are not only proficient in the latest technologies like machine learning, analytics, and more but also familiar with levering such technologies on the cloud to develop products. For any developers, cloud computing skills has moved from nice-to-have to must-have skill as remote working has become the new normal.

To enable learners to master the skills of the future, Google Cloud is offering free access to its Qwiklabs for 30 days. You will have to copy the offer code and then click on enroll to begin. However, you should follow the instruction that mandates the completion of 30 minutes tour of Qwiklabs and Google Cloud to get 30-day training. If you only signup on Qwiklabs and do not finish the tour, you will not get access to the free training.

Every lesson will come with a lab that will give you free access to the Google Cloud Platform to learn for free. Although there are timers to end the session to avoid endless computing, you can start the session again to continue to learn and complete the courses. You will also get badges on completing the courses to showcase your knowledge.

Learn from a wide range of courses like Big Data, Machine Learning, Infrastructure & DevOps, Website & App Development, and more and upskill to stay relevant in 2021 and beyond.

Register for free training from Google Cloud here.

Graphcore Raises $222 Million In Series E At A $2.77 Billion Valuation

By

Kenny Manuel

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December 30, 2020

Graphpcore, a UK-based AI chip producer, raises $222 million in Series E funding led by Ontario Teachers’ Pensions Plan Board, Fidelity International, and Schroders. Existing investors like Baillie Gifford and Draper Esprit also deepened their tie with Graphocre by participating in Series E funding. According to Graphocore, the investment will allow the company to further enhance its AI chips, software, and expand globally.

Founded in 2016, Graphcore is a pioneer in the developing Intelligence Processing Units (IPUs) that have outperformed Graphics Processing Units (GPUs). IPUs are optimized for processing AI-based workloads on the cloud.

Some of the early adopters of Graphocore’s IPU include Microsoft, Dell, Cirrascale, and more. For one, since November 2019, Microsoft has been offering access to Graphcore’s IPUs to selected users to innovate with high-speed processing of AI applications.

Graphocore has been evolving its processors and in July 2020 announced the second generation of GC200 chip. These chips are a part of its M200 IPU Machine that has four 7-nanometer GC200 chips. The latest GC200 chips have 59.4 billion transistors on a single 823 sq mm die, pushing the processing boundaries for projects involving neural networks.

To help developers make AI applications on IPUs, Graphcore has open-sourced PopLibs libraries to simplify the development process. Today, Graphcore’s IPUs currently support TensorFlow and PyTorch to enable developers to leverage ML-based products’ neural networks.

An Ultimate Guide To Data Science Career Path In 2021

By

Arihant Jain

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December 29, 2020

Data science career path strategy keeps evolving due to the varying demand for organizations. Over the years, aspirants with minimal knowledge could land a data position because there was a dearth of talents. However, today millions of people are learning data science, leading to enough aspirants for job openings. Unlike yesteryears, you might not get a job offer if you struggle to differentiate among other applicants in 2021. To ensure you learn and get data science jobs, you have to devise an effective data science learning path in 2021.

Here is a 21-step data science career guide for 2021:-

Develop Problem Solving Aptitude: More often than not, aspirants try to learn data science because of the hype. As a result, they ignore the skill of developing rigor for solving business problems. You need to have the curiosity to find challenges in day-to-day lives and a passion for solving problems. Either there are shortcomings with the way products and services are delivered or issues that are ignored altogether. When data is everywhere, you should find ways to leverage data science techniques and mitigate pain points for businesses in the digital age.
Learn Structure Thinking Framework: Structured thinking is the art of applying a framework to an unstructured problem to simplify the process by understanding intricacies at the macro level. Beginners often try to fit machine learning techniques into problems from the first go because they lack the ability to think structurally for any situation. Instead, it would help if you mindmap how a problem can be solved from the beginning till the end. This does not mean that you will have a perfect framework, but an overall approach to solving problems will streamline the entire process.
Understand the Basics Of Data Science: When you have a problem-solving aptitude and structured thinking ability, you need to acquire skills to solve problems. For this, you should read several blogs and talk to data science practitioners to understand the scope of machine learning, data, and more. Some problems cannot be solved with machine learning techniques. Consequently, you will know where you can apply data science practices and where you cannot.
Explore Different Domains: This is one of the crucial stages in your data science career path. You cannot master a lot of domains in one go as every sector has its share of challenges that may require a completely different approach. It is recommended to figure out how machine learning is used in other domains; this will give you a heads-up regarding standard practices in specific sectors like BFSI, retail, and more. Besides, if you are passionate about a particular domain, you can even effectively strategize your data science career path from the very beginning.
Learn Programming Language: Unfortunately, most of the aspirants start with learning a programming language. Non-technical aspects like the right attitude, critical thinking, structural thinking, and storytelling are equally important. Make sure that you go through the above four steps before learning a programming language. You can either learn Python or R programming languages to start. But, do not fall for questions like Python vs. R programming for data science. Further, get started with IDE or use Jupyter Notebook with Anaconda to isolate environments.
Master Statistics: Since statistics and mathematics are the cores of machine learning, begin with descriptive statistics, and gradually move ahead to master inferential statistics. Most of the time, beginners obtain an overall idea of inferential statistics and rely on libraries to carry out statistical analysis. Although this can help complete the task at hand, a weak foundation can limit your ability to think and explore data.
Grasp Mathematics: Mastering mathematics is vital for a data scientist as one can come up with their own methodologies instead of depending on existing libraries. However, to begin, you should know about logarithm, exponential, linear algebra, and more. As you progress, learn calculus and other optimization techniques.
Attend Meetups/Conferences: Engaging with like-minded people can keep you motivated during your learning curve and improve your storytelling skills. Meetups offer a completely different learning experience than the regular online videos, as you can get real-time suggestions or help for your specific challenges from others. Besides, you can also get inspiration by following top data scientists trying to solve strenuous problems with data science.
Master Python Libraries: After getting familiar with Python’s fundamentals, learn the most common libraries like Pandas, Numpy, Matplotlib, Seaborn, Skit-learn, and more. You would require these libraries for almost every project you will work on. Mastering these libraries will save you a few Google searches to speed up your tasks.
Learn Exploratory Data Analysis (EDA): Exploratory data analysis is the first step in data analytics, where data is assessed to discover patterns, spot outliers, evaluate the spread, and more. A proper exploratory data analysis can help structure the entire process of the project. It will also play a crucial role in assessing your Python and SQL skills; you can choose from Titanic, Netflix movie recommendations, and house price prediction datasets to practice exploratory data analysis.
Data Visualization & Storytelling: Being proficient in data visualization helps understand the data and allows practitioners to tell compelling stories. Since visualization summarises the entire data to communicate immediately, learning to plot can provide an edge over others.
Supervised Machine Learning: As you advance in your data science career path, supervised machine learning is where your machine learning journey begins. Start with simple methods like classification and regression. You will also come across various terminologies like overfitting, underfitting, bias-variance tradeoff, and more. Other standard techniques include linear regression, logistic regression, ridge regression, lasso regression, decision tree, KNN, and Naive Bayes.
Advanced Supervised Algorithms: After supervised machine learning, you can focus on advanced algorithms like the random forest, XGBoost, Catboost, GBM, SVM, and others. These techniques in several use cases help in further optimizing your algorithm to get superior results.
Unsupervised Algorithms: Unlike supervised learning, there are no corresponding values for the input you provide. Unsupervised learning includes clustering and association to unearth patterns that, in most cases, the human cannot. Some of the popular algorithms are K-Means, Hierarchical clustering, DBSCAN, PCA, LDA, and more.
Advance Hyperparameters Tuning Methods And Model Performance: While the above algorithms can help you obtain fairly optimal results, effective hyperparameter tuning can be the game-changer for your machine learning models. Learn techniques like Grid search, random search, Bayesian, and understand different model performance metrics for classification and regression.
Recommendation Engines & TimeSeries Forecasting: Personalizations has become the differentiating factor for many organizations to capture the market. As a result, expertise in recommendation engines becomes crucial for you to learn. Besides, time-series forecasting is another commonly used technique to understand the occurrence of events and predict outcomes. Consequently, you should know SVD and work on recommendations engine projects.
Participate In Competitions: The best way to remember most of what you learned is by practicing in Hackathons and Kaggle competitions. Besides, you can start teaching others by writing blogs and creating Youtube videos. Creating content and participating in contests puts your focus on acquiring in-depth knowledge about several machine learning topics. At this stage, you can also apply for internships to learn while working on real-world projects at data-driven companies.
Neural Networks: Neural Networks can be a very vast concept depending on the use cases. However, you can learn techniques like Artificial Neural Networks and master frameworks like TensorFlow or PyTorch.
Basics Of NLP: As per various studies, 70 to 80 percent of data in organizations are unstructured. This makes natural language processing a crucial technique to bring value from unstructured data. Essential methods involved in this are tokenization, stemming, and lemmatization.
Basics Of Computer Vision: Computer Vision has gained traction due to the numerous use cases. But bias in computer vision is limiting the adoption of the technology. This, in contrast, opens up the opportunity to blaze the trail and develop reliable computer vision-based products. Some of the crucial techniques to learn are CNN and transfer learning.
Apply For Jobs: Eventually, you can apply for data science jobs to work with experts and advance your data science career path in organizations. Jobs can sometimes be correlated to your visibility in the industry. Therefore, you should increase your visibility by publishing blogs, networking in conferences, and being active on LinkedIn.

DeepMind’s MuZero Marks A New Breakthrough In Reinforcement Learning

By

Kenny Manuel

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December 28, 2020

DeepMind’s MuZero, an AI program that can play Chess, Go, Shogi, and Atari, gained superhuman performance to outperform existing AI agents like DQN, R2D2, and Agent57, on Atari while matching the performance of AlphaZero on Go, Chess, and Shogi. DeepMind with MuZero could do all of this even without training it with the rules of Go, Chess, Shogi, and Atari.

Although MuZero was introduced in a preliminary paper in 2019, this breakthrough was obtained by combining AlphaZero’s superior lookahead tree search. But what makes MuZero different from other approaches is that it does not try to model the entire environment for effective planning.

“For many years, researchers have sought methods that can both learn a model that explains their environment, and can then use that model to plan the best course of action. Until now, most approaches have struggled to plan effectively in domains, such as Atari, where the rules or dynamics are typically unknown and complex,” mentions DeepMind in a blog post.

Also Read: Free 12-Week Long Artificial Intelligence Course By IIT Delhi

In strenuous environments, AI models have failed to deliver optimal results because machine learning struggles to generalize. As a workaround, researchers adopt techniques like lookahead search or model-based planning. However, both approaches have several limitations when it comes to complex environments. While lookahead search only delivers exceptional results when the rules are correctly defined (Chess and Go) or provided with accurate simulators, model-based planning cannot be used to understand the entire complex environments like Atari.

Consequently, DeepMind with MuZero uses an approach where they model only some parts of the environment, which are crucial for AI to make decisions. This eliminates the need for modeling the entire environment in reinforcement learning. For instance, as humans, we do not understand the environment’s intricacies, but we can predict the weather conditions and make decisions accordingly. Adopting a human-like approach for decision-making by AI makes DeepMind’s MuZero a significant breakthrough in the general-purpose algorithm.

DeepMind’s MuZero considers three elements of environments — value, policy, and reward — for effective planning. While value tells how good is the current position, the policy helps in evaluating the best action. The reward assesses the effectiveness of the last action.

MuZero marks a new beginning in AI that can open up further opportunities in the domain to democratize machine learning in complex and dynamic environments.

Free 12-Week Long Artificial Intelligence Course By IIT Delhi Starts On 18 January 2021

By

Ratan Kumar

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December 27, 2020

free artificial intelligence course by IIT Delhi

IIT Delhi launches a free 12-week long artificial intelligence course primarily targeting undergraduate students. This course will be taught by Prof. Mausam, who was named 25th most influential scholar in AI for 2019 by ArnetMiner.

Hosted on National Program on Technology Enhanced Learning (NPTEL), an e-learning platform by the Government of India, the course is ideal for beginners to get started with artificial intelligence.

The course follows a book — third edition of Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig. Although the course will only cover around 50 percent of the book, it will touch upon a wide range of artificial intelligence techniques.

Also Read: Free 12-Week-Long Cryptography Course By IIIT Bangalore

Some of the key concepts in the free artificial intelligence course by IIT Delhi include adversarial search, bayesian networks, decision theory, Markov decision processes, reinforcement learning, and neural networks.

The course starts on 18 January 2021, but the enrollments will be accepted till 25 January 2021. After the completion of the course on 9 April 2021, you can opt for an examination to get the certificate. However, the e-certification is not free; you will have to pay Rs 1000 to enroll for the examination.

Since the certification is optional, you can complete the entire course for free. Enroll for the free artificial intelligence course by IIT Delhi here.

Honeywell Acquires Sparta Systems To Offer Superior AI-Based Life Sciences Solutions

By

Kenny Manuel

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December 24, 2020

Honeywell acquires Sparta Systems, an AI-based SaaS platform provider for the life science industry, for $1.3 billion. Sparta was a privately owned company based out of New Jersey, with offices across Europe and Asia. Founded in 1994, Sparta has over 400 customers, including 42 of the top 50 pharma companies and 33 of the top 50 medical device companies.

According to Honeywell, the all-cash acquisition of Sparta will provide the world’s leading drug manufacturers and biomedical firms with advanced automation and process control technologies for over 30 years.

“Sparta’s TrackWise Digital® and QualityWise.aiSM are a welcome addition to Honeywell’s enterprise performance management software, Honeywell Forge, and will further enhance the link between quality and production data for life sciences manufacturers,” said Que Dallara, president and chief executive officer of Honeywell Connected Enterprise.

Also Read: Cognizant Acquires Inawisdom To Enhance Its AI & ML Capabilities

“Our combined offerings will make it easier for customers to gain critical insights from manufacturing and quality data that can improve their manufacturing processes while ensuring product quality, patient safety, and supply chain continuity.”

With Sparta, Honeywell will double down to offer superior AI-based life sciences solutions for the healthcare industry. Due to COVID-19, the need for effective solutions that automate the process in life sciences has increased rapidly, leading to more demand for digital products.

Honeywell is a pioneer in the automation industry and has been offering next-generation solutions to a wide range of companies. By integrating Sparta’s capabilities into Honeywell Forge platform and Experian Process Knowledge System, the company can assist highly regulated organizations in ensuring quality, compliance, documentation, training and supplies.