#DataSets

[jcmarchi]

Impact and innovation of AI in energy use with James Chalmers

New Post has been published on https://thedigitalinsider.com/impact-and-innovation-of-ai-in-energy-use-with-james-chalmers/

Impact and innovation of AI in energy use with James Chalmers

In the very first episode of our monhtly Explainable AI podcas, hosts Paul Anthony Claxton and Rohan Hall sat down with James Chalmers, Chief Revenue Officer of Novo Power, to discuss one of the most pressing issues in AI today: energy consumption and its environmental impact.

Together, they explored how AI’s rapid expansion is placing significant demands on global power infrastructures and what leaders in the tech industry are doing to address this.

The conversation covered various important topics, from the unique power demands of generative AI models to potential solutions like neuromorphic computing and waste heat recapture. If you’re interested in how AI shapes business and global energy policies, this episode is a must-listen.

Why this conversation matters for the future of AI

The rise of AI, especially generative models, isn’t just advancing technology; it’s consuming power at an unprecedented rate. Understanding these impacts is crucial for AI enthusiasts who want to see AI development continue sustainably and ethically.

As James explains, AI’s current reliance on massive datasets and intensive computational power has given it the fastest-growing energy footprint of any technology in history. For those working in AI, understanding how to manage these demands can be a significant asset in building future-forward solutions.

Main takeaways

AI’s power consumption problem: Generative AI models, which require vast amounts of energy for training and generation, consume ten times more power than traditional search engines.

Waste heat utilization: Nearly all power in data centers is lost as waste heat. Solutions like those at Novo Power are exploring how to recycle this energy.

Neuromorphic computing: This emerging technology, inspired by human neural networks, promises more energy-efficient AI processing.

Shift to responsible use: AI can help businesses address inefficiencies, but organizations need to integrate AI where it truly supports business goals rather than simply following trends.

Educational imperative: For AI to reach its potential without causing environmental strain, a broader understanding of its capabilities, impacts, and sustainable use is essential.

Meet James Chalmers

James Chalmers is a seasoned executive and strategist with extensive international experience guiding ventures through fundraising, product development, commercialization, and growth.

As the Founder and Managing Partner at BaseCamp, he has reshaped traditional engagement models between startups, service providers, and investors, emphasizing a unique approach to creating long-term value through differentiation.

Rather than merely enhancing existing processes, James champions transformative strategies that set companies apart, strongly emphasizing sustainable development.

Numerous accolades validate his work, including recognition from Forbes and Inc. Magazine as a leader of one of the Fastest-Growing and Most Innovative Companies, as well as B Corporation’s Best for The World and MedTech World’s Best Consultancy Services.

He’s also a LinkedIn ‘Top Voice’ on Product Development, Entrepreneurship, and Sustainable Development, reflecting his ability to drive substantial and sustainable growth through innovation and sound business fundamentals.

At BaseCamp, James applies his executive expertise to provide hands-on advisory services in fundraising, product development, commercialization, and executive strategy.

His commitment extends beyond addressing immediate business challenges; he prioritizes building competency and capacity within each startup he advises. Focused on sustainability, his work is dedicated to supporting companies that address one or more of the United Nations’ 17 Sustainable Development Goals through AI, DeepTech, or Platform Technologies.

About the hosts:

Paul Anthony Claxton – Q1 Velocity Venture Capital | LinkedIn

www.paulclaxton.io – am a Managing General Partner at Q1 Velocity Venture Capital… · Experience: Q1 Velocity Venture Capital · Education: Harvard Extension School · Location: Beverly Hills · 500+ connections on LinkedIn. View Paul Anthony Claxton’s profile on LinkedIn, a professional community of 1 billion members.

Rohan Hall – Code Genie AI | LinkedIn

Are you ready to transform your business using the power of AI? With over 30 years of… · Experience: Code Genie AI · Location: Los Angeles Metropolitan Area · 500+ connections on LinkedIn. View Rohan Hall’s profile on LinkedIn, a professional community of 1 billion members.

Like what you see? Then check out tonnes more.

From exclusive content by industry experts and an ever-increasing bank of real world use cases, to 80+ deep-dive summit presentations, our membership plans are packed with awesome AI resources.

Subscribe now

[debtservicecoverageratio]

DATASETS IN FINTECH STARTUP WORLD

Here are some real-world examples of fintech companies using datasets to improve their services:

1. Personalized Financial Planning:

Mint: Mint aggregates financial data from various sources like bank accounts, credit cards, and investments to provide users with a holistic view of their finances. It then uses this data to offer personalized budgets, track spending habits, and suggest ways to save money.

Personal Capital: Similar to Mint, Personal Capital analyzes user data to provide personalized financial advice, including investment recommendations and retirement planning.

2. Credit Scoring and Lending:

Upstart: Upstart uses alternative data sources like education and employment history, in addition to traditional credit scores, to assess creditworthiness and provide loans to individuals who may be overlooked by traditional lenders. This expands access to credit and often results in fairer lending practices.

Kiva: Kiva uses a dataset of loan applications and repayment history to assess the risk of lending to individuals in developing countries. This data-driven approach allows them to provide microloans to entrepreneurs who lack access to traditional banking systems.

3. Fraud Detection:

Stripe: Stripe uses machine learning algorithms to analyze transaction data and identify potentially fraudulent activity. This helps protect businesses from losses and ensures secure online payments.

Paypal: Paypal employs sophisticated fraud detection systems that analyze vast amounts of data to identify and prevent unauthorized transactions, protecting both buyers and sellers.

4. Investment Platforms:

Robinhood: Robinhood uses data to provide users with insights into stock performance, market trends, and personalized investment recommendations. This makes investing more accessible and helps users make informed decisions.

Betterment: Betterment uses algorithms and data analysis to create diversified investment portfolios tailored to individual risk tolerance and financial goals. This automated approach simplifies investing and helps users achieve their long-term financial objectives.

These are just a few examples of how fintech companies leverage datasets to improve their services and provide better value to their customers.

[jbfly46]

I bleed revolution. If your only anarchist actions are related to union organizing, then you’re not an anarchist, you’re a corporate puppet. Everything you do should work to subvert the current and future actions of the state and all of their tentacle corporate affiliations. If your only goal in life is to work under the orders of someone else, under someone’s else’s direction, with someone else’s instructions, then you’re not a human being. You’re chattel cattle at best. If a corporate pig tells or wants you to do something, then you should do the exact opposite, or else you’re just a pawn in a game of global corporate chess. Every one of your actions should be both a defensive and offensive maneuver. If you defend while you attack, you become one with your true purpose, which is to dismantle the state and all corporate authority. If you don’t think in a linear manner, then you’re not apart of their datasets, and they can’t predict your next move. You operate from outside of their datasets and what they think is your next move is never your next move. Then they start to doubt their own intelligence and all the false assumptions it’s based on, and the system starts to crumble. You use any means necessary, because that is your constitutional right, just as they use any means necessary to hold onto the power they stole from you. They stole your birthright, and it’s your legal duty as an American citizen to seek a redress of your grievances, using whatever it takes. Under no pretext.

[nikitricky]

Ever wondered what the datasets used to train AI look like? This video is a subset of ImageNet-1k (18k images) with some other metrics.

Read more on how I made it and see some extra visualizations.

Okay! I'll split this up by the elements in the video, but first I need to add some context about

The dataset

ImageNet-1k (aka ILSVRC 2012) is an image classification dataset - you have a set number of classes (in this case 1000) and each class has a set of images. This is the most popular version of ImageNet, which usually has 21000 classes.

ImageNet was made using nouns from WordNet, searched online. From 2010 to 2017 yearly competitions were held to determine the best image classification model. It has greatly benefitted computer vision, developing model architectures that you've likely used unknowingly. See the accuracy progression here.

ResNet

Residual Network (or ResNet) is an architecture for image recognition made in 2015, trying to fix "vanishing/exploding gradients" (read the paper here). It managed to achieve an accuracy of 96.43% (that's 96 thousand times better than randomly guessing!), winning first place back in 2015. I'll be using a smaller version of this model (ResNet-50), boasting an accuracy of 95%.

The scatter plot

If you look at the video long enough, you'll realize that similar images (eg. dogs, types of food) will be closer together than unrelated ones. This is achieved using two things: image embeddings and dimensionality reduction.

Image embeddings

In short, image embeddings are points in an n-dimensional space (read this post for more info on higher dimensions), in this case, made from chopping off the last layer from ResNet-50, producing a point in 1024-dimensional space.

The benefit of doing all of that than just comparing pixels between two images is that the model (specifically made for classification) only looks for features that would make the classification easier (preserving semantic information). For instance - you have 3 images of dogs, two of them are the same breed, but the first one looks more similar to the other one (eg. matching background). If you compare the pixels, the first and third images would be closer, but if you use embeddings the first and second ones would be closer because of the matching breeds.

Dimensionality reduction

Now we have all these image embeddings that are grouped by semantic (meaning) similarity and we want to visualize them. But how? You can't possibly display a 1024-dimensional scatter plot to someone and for them to understand it. That's where dimensionality reduction comes into play. In this case, we're reducing 1024 dimensions to 2 using an algorithm called t-SNE. Now the scatter plot will be something we mere mortals can comprehend.

Extra visualizations

Here's the scatter plot in HD:

This idea actually comes from an older project where I did this on a smaller dataset (about 8k images). The results were quite promising! You can see how each of the 8 classes is neatly separated, plus how differences in the subject's angle, surroundings, and color.

Find the full-resolution image here

Similar images

I just compared every point to every other point (in the 2d space, It would be too computationally expensive otherwise) and got the 6 closest points to that. You can see when the model incorrectly classifies something if the related images are not similar to the one presented (eg. there's an image of a payphone but all of the similar images are bridges).

Pixel rarity

This one was pretty simple, I used a script to count the occurrences of pixel colors. Again, this idea comes from an older project, where I counted the entirety of the dataset, so I just used that.

Extra visualization

Here are all the colors that appeared in the image, sorted by popularity, left to right, up to down

Some final stuff

MP means Megapixel (one million pixels) - a 1000x1000 image is one megapixel big (it has one million pixels)

That's all, thanks for reading. Feel free to ask questions and I'll try my best to respond to them.

[edujournalblogs]

Data Cleaning in Data Science

Data cleaning is an integral part of data preprocessing viz., removing or correcting inaccurate information within a data set. This could mean missing data, spelling mistakes, and duplicates to name a few issues. Inaccurate information can lead to issues during analysis phase if not properly addressed at the earlier stages.

Data Cleaning vs Data Wrangling : Data cleaning focuses on fixing inaccuracies within your data set. Data wrangling, on the other hand, is concerned with converting the data’s format into one that can be accepted and processed by a machine learning model.

Data Cleaning steps to follow :

Remove irrelevant data

Resolve any duplicates issues

Correct structural errors if any

Deal with missing fields in the dataset

Zone in on any data outliers and remove them

Validate your data

At EduJournal, we understand the importance of gaining practical skills and industry-relevant knowledge to succeed in the field of data analytics / data science. Our certified program in data science and data analytics is designed to equip freshers / experienced with the necessary expertise and hands-on experience experience so they are well equiped for the job.

URL : http://www.edujournal.com

[bluefeatheredvelociraptor]

This reminded me of the time I was doing social service for my bachelor's degree.

I'm a biologist. Back then (2007-2008ish, I guess? Don't remember, it's been a while lol) I joined the Ornithology Lab hoping to start my bachelor's thesis early (I did NOT but that's another story lmao). Part of my social service job involved transcribing lots (and I mean LOTS, there were journals dating back to the 80s) of field journals from past students into Excel spreadsheets and then entering the curated info into a special database designed by the Mexican environmental commission (CONABIO) for it to be accessible to other researchers and to add to the national biodiversity repository.

Oh, boy.

The spelling in plenty of those journals was TERRIBLE. And I'm not referring to the questionable spelling of scientific names (which can be truly difficult to memorize and write). I'm talking about the spelling of things like the alpha codes we ornithologists use to abbreviate either the scientific names or the standardized common names in English (HOW DO YOU MISSPELL FOUR / SIX LETTERS???), site identifiers, descriptions, field observations, etc. Heck, there were times when even the names of the observers were spelled differently ON THE SAME PAGE written BY THE SAME PERSON. Had at least one instance where a student regularly spelled his own name wrong and the head of the Laboratory didn't remember which spelling was the correct one, so we had to settle with the most common spelling of that student's name.

Considering all this information was gathered by fellow biology students during field practices (who in all likelihood were making these identifications with the aid of guidebooks and the professors' guidance), one would expect them to be able to write with certain grammatical consistency, as was to be expected of their academic level. But nope.

And yes, I know people can be dyslexic (or have other undiagnosed learning disabilities) and struggle with reading and writing, but some of those journals were written by people who were somewhat bordering on functional illiteracy, which I find truly baffling of people studying for a higher education degree.

Curating all that info was tortuous but I managed. And in the end I completed the mandatory 480 hours (and more!) of the social service necessary for graduation. Good grief, though. Reading OPs post gave me serious war flashbacks 😂

Working on a dataset of roadkill reports. state agency personnel CANNOT spell

[sapientsapiens]

Finalized on a dataset for the 1st Capstone project at #mlzoomcamp led by Alexey Grigorev @DataTalksClub .

[carolxia]

Salary dataset

(1) 250 celebrity endorsement earnings data (2)200 luxury advertisement videos (3) 9 Financial data of luxury companies

[apondoffinlessfishes]

Will there be an algorithm to airbrush away our worst features? Must we buy this privatization of culture? Does the postmodern critique of the museum, the call for tearing down its walls, do anything but free art for the shopping mall? I’ll take Bilbao, thanks.

Saunders, W. S. (ed.) (2017). Commodification and Spectacle in Architecture: A Harvard Design Magazine Reader. Harvard Design Magazine.

[titleknown]

...I will say @mortalityplays, even as someone who's generally positive towards AI art/image synthesis, thank you for approaching it from this data privacy view instead of the copyright argument which, as I've talked about before, is a very bad framework.

Like... legit, it disturbs me how much people are moving into copyright maximalism when a much more helpful way to think of it would be from the data privacy angle you're describing, and I wish more people would rally around trying to take action with that instead of trying to make our bad copyright system even worse.

Because, as friend of the blog @tangibletechnomancy said, when you look into it AI art is one of the least concerning things they're doing with that data, and if this motivated folks to push back against that, well, that's probably a good thing.

ngl it's driving me a little bit fucking insane that the whole conversation about image scraping for AI has settled on copyright and legality as a primary concern, and not consent. my shit should not be used without my consent. I will give it away for free, but I want to be asked.

I don't want to be included in studies without my knowledge or consent. I don't want my face captured for the training of facial recognition models without my knowledge or consent. I don't want my voice captured for the training of speech recognition models without my consent. I don't want my demographic or interest profile captured without my consent. I don't want my art harvested for visual model training without my consent. It's not about 'theft' (fake idea) or 'ownership' (fake idea) or 'inherent value' (fake idea). It's about my ability to opt out from being used as a data point. I object to being a commodity by default.

[jcmarchi]

Breaking the Scaling Code: How AI Models Are Redefining the Rules

New Post has been published on https://thedigitalinsider.com/breaking-the-scaling-code-how-ai-models-are-redefining-the-rules/

Breaking the Scaling Code: How AI Models Are Redefining the Rules

Artificial intelligence has taken remarkable strides in recent years. Models that once struggled with basic tasks now excel at solving math problems, generating code, and answering complex questions. Central to this progress is the concept of scaling laws—rules that explain how AI models improve as they grow, are trained on more data, or are powered by greater computational resources. For years, these laws served as a blueprint for developing better AI.

Recently, a new trend has emerged. Researchers are finding ways to achieve groundbreaking results without simply making models bigger. This shift is more than a technical evolution. It’s reshaping how AI is built, making it more efficient, accessible, and sustainable.

The Basics of Scaling Laws

Scaling laws are like a formula for AI improvement. They state that as you increase the size of a model, feed it more data, or give it access to more computational power, its performance improves. For example:

Model size: Larger models with more parameters can learn and represent more complex patterns. Parameters are the adjustable parts of a model that allow it to make predictions.

Data: Training on vast, diverse datasets helps models generalize better, enabling them to handle tasks they weren’t explicitly trained for.

Compute: More computational power allows faster and more efficient training, achieving higher performance.

This recipe has driven AI’s evolution for over a decade. Early neural networks like AlexNet and ResNet demonstrated how increasing model size could improve image recognition. Then came transformers where models like GPT-3 and Google’s BERT have showed that scaling could unlock entirely new capabilities, such as few-shot learning.

The Limits of Scaling

Despite its success, scaling has limits. As models grow, the improvements from adding more parameters diminish. This phenomenon, known as the “law of diminishing returns,” means that doubling a model’s size doesn’t double its performance. Instead, each increment delivers smaller gains. This means that to further push the performance of such models would require even more resources for relatively modest gains. This has real-world consequences. Building massive models comes with significant financial and environmental costs. Training large models is expensive. GPT-3 reportedly cost millions of dollars to train. These costs make cutting-edge AI inaccessible to smaller organizations. Training massive models consumes vast amounts of energy. A study estimated that training a single large model could emit as much carbon as five cars over their lifetimes.

Researchers recognized these challenges and began exploring alternatives. Instead of relying on brute force, they asked: How can we make AI smarter, not just bigger?

Breaking the Scaling Code

Recent breakthroughs show it’s possible to outperform traditional scaling laws. Smarter architectures, refined data strategies, and efficient training techniques are enabling AI to reach new heights without requiring massive resources.

Smarter Model Designs: Rather than making models larger, researchers are focusing on making them more efficient. Examples are:

Sparse models: Instead of activating all parameters at once, sparse models only use the parts needed for a specific task. This approach saves computational power while maintaining performance. A notable example is Mistral 7B, which, despite having only 7 billion parameters, outperforms much larger models by using a sparse architecture.

Transformer improvements: Transformers remain the backbone of modern AI, but their designs are evolving. Innovations like linear attention mechanisms make transformers faster and less resource-intensive.

Better Data Strategies: More data isn’t always better. Curated, high-quality datasets often outperform sheer volume. For example,

Focused datasets: Instead of training on massive, unfiltered data, researchers are using clean and relevant datasets. For instance, OpenAI has shifted toward carefully selected data to improve reliability.

Domain-specific training: In specialized areas like medicine or law, targeted datasets help models perform well with fewer examples.

Efficient Training Methods: New training techniques are reducing resource demands without sacrificing performance. Some examples of these training methods include:

Curriculum learning: By starting with simpler tasks and gradually introducing harder ones, models learn more effectively. This mirrors how humans learn.

Techniques like LoRA (Low-Rank Adaptation): These methods fine-tune models efficiently without retraining them entirely.

Gradient checkpointing: This approach reduces memory use during training, enabling larger models to run on limited hardware.

Emergent Abilities: As models grow, they sometimes display surprising capabilities, like solving problems they weren’t explicitly trained for. These emergent abilities challenge traditional scaling laws, as they often appear in larger models but not in their smaller counterparts. Researchers are now investigating ways to unlock these abilities more efficiently, without relying on brute-force scaling.

Hybrid Approaches for Smarter AI: Combining neural networks with symbolic reasoning is another promising direction. These hybrid systems combine pattern recognition with logical reasoning, making them more intelligent and adaptable. This approach reduces the need for massive datasets and compute power.

Real-World Examples

Several recent models showcase how these advancements are rewriting the rules:

GPT-4o Mini: The model delivers performance comparable to its much larger version but at a fraction of the cost and resources. It achieves these results with the help of smarter training techniques and focused datasets.

Mistral 7B: With only 7 billion parameters, this model outperforms models with tens of billions. Its sparse architecture proves that smart design can surpass raw size.

Claude 3.5: Prioritizing safety and ethical considerations, this model balances strong performance with thoughtful resource use.

The Impact of Breaking Scaling Laws

These advancements have real-world implications.

Making AI More Accessible: Efficient designs lower the cost of developing and deploying AI. Open-source models like Llama 3.1 are making advanced AI tools available to smaller companies and researchers.

A Greener Future: Optimized models reduce energy consumption, making AI development more sustainable. This shift is critical as concerns about AI’s environmental footprint grow.

Expanding AI’s Reach: Smaller, more efficient models can run on everyday devices, like smartphones and IoT gadgets. This opens new possibilities for applications, from real-time language translation to autonomous systems in cars.

The Bottom Line

Scaling laws have shaped AI’s past, but they no longer define its future. Smarter architectures, better data handling, and efficient training methods are breaking the rules of traditional scaling. These innovations are making AI not just more powerful, but also more practical and sustainable.

The focus has shifted from brute-force growth to intelligent design. This new era promises AI that’s accessible to more people, environmentally friendly, and capable of solving problems in ways we’re just beginning to imagine. The scaling code isn’t just being broken—it’s being rewritten.

[ivyinkblue33]

Image Library

Here's the link of those folders: images

An example folder

[aimarketingexpertemea]

DATASETS in GOOGLE SEARCH

Value of datasets in Google Search:

Imagine you're a detective investigating a complex case. You have witness testimonies (like knowledge graphs), photos from the crime scene (images), and maybe even security camera footage (videos). These give you clues and a general idea of what happened. But what if you could access the raw forensic data – fingerprints, DNA analysis, ballistic reports? That's what datasets offer in the world of search.

Datasets are like the underlying evidence that allows you to go beyond surface-level understanding and conduct your own in-depth analysis. They empower you to connect the dots, uncover hidden patterns, and draw your own conclusions.

Think of a student researching the impact of social media on teenagers. They might find articles discussing the topic (knowledge graphs) and see illustrative images or videos. But with a dataset containing survey results, social media usage statistics, and mental health indicators, they can dive deeper, explore correlations, and potentially uncover new insights about this complex relationship.

Datasets are not just for academics or detectives, though. They can be incredibly useful for everyday life. Imagine a family planning a vacation. They might look at beautiful pictures of destinations (images) and watch travel vlogs (videos). But with access to datasets on weather patterns, flight prices, and local attractions, they can make informed decisions, optimize their itinerary, and ultimately have a more fulfilling experience.

The beauty of datasets lies in their versatility. They can be visualized, analyzed, and combined with other data sources to create new knowledge and solve real-world problems. Google Search, with its vast reach and powerful algorithms, has the potential to make these datasets accessible to everyone, democratizing information and empowering individuals to make data-driven decisions in all aspects of their lives.

By integrating datasets seamlessly into search results, Google can transform the way we interact with information, moving beyond passive consumption to active exploration and discovery. This will not only enhance our understanding of the world around us but also foster a more informed and data-literate society.

[govindhtech]

Use AWS Supply Chain Analytics To Gain Useful Knowledge

Use AWS Supply Chain Analytics to unleash the power of your supply chain data and obtain useful insights.

AWS Supply Chain

Reduce expenses and minimize risks with a supply chain solution driven by machine learning.

Demand forecasting and inventory visibility, actionable insights, integrated contextual collaboration, demand and supply planning, n-tier supplier visibility, and sustainability information management are all enhanced by AWS Supply Chain, a cloud-based supply chain management application that aggregates data and offers ML-powered forecasting techniques. In addition to utilizing ML and generative AI to transform and combine fragmented data into the supply chain data lake (SCDL), AWS Supply Chain can interact with your current solutions for enterprise resource planning (ERP) and supply chain management. Without requiring replatforming, upfront license costs, or long-term commitments, AWS Supply Chain may enhance supply chain risk management.

Advantages

Reduce the risk of overstock and stock-outs

Reduce extra inventory expenditures and enhance consumer experiences by reducing the risk of overstock and stock-outs.

Increase visibility quickly

Obtain supply chain visibility quickly without having to make long-term commitments, pay upfront license fees, or replatform.

Actionable insights driven by ML

Use actionable insights driven by machine learning (ML) to make better supply chain decisions.

Simplify the process of gathering sustainability data and collaborating on supply plans

Work with partners on order commitments and supply plans more safely and conveniently. Determine and address shortages of materials or components and gather sustainability data effectively.

AWS is announcing that AWS Supply Chain Analytics, which is powered by Amazon QuickSight, is now generally available. Using your data in AWS Supply Chain, this new functionality enables you to create personalized report dashboards. Your supply chain managers or business analysts can use this functionality to visualize data, conduct bespoke analysis, and obtain useful insights for your supply chain management operations.

Amazon QuickSight embedded authoring tools are integrated into the AWS Supply Chain user interface, and AWS Supply Chain Analytics makes use of the AWS Supply Chain data lake. You may create unique insights, measurements, and key performance indicators (KPIs) for your operational analytics using this integration’s unified and customizable interface.

Furthermore, AWS Supply Chain Analytics offers pre-made dashboards that you may use exactly as is or alter to suit your requirements. The following prebuilt dashboards will be available to you at launch:

Plan-Over-Plan Variance: Shows differences in units and values across important dimensions including product, site, and time periods by comparing two demand plans. Seasonality Analytics: Provides a view of demand from year to year, showing trends in average demand quantities and emphasizing seasonality patterns with monthly and weekly heatmaps.

Let’s begin

Allow me to explain you about AWS Supply Chain Analytics’ features.

Turning on AWS Supply Chain Analytics is the first step. Go to Settings, pick Organizations, and then pick Analytics to accomplish this. You can enable analytics data access here.

Now you can add new roles with analytics access or edit roles that already exist.

After this feature is activated, you may choose the Connecting to Analytics card or Analytics from the left navigation menu to access the AWS Supply Chain Analytics feature when you log in to AWS Supply Chain.

The Supply Chain Function dropdown list then allows you to choose the prebuilt dashboards you require:

The best thing about these prebuilt dashboards is how simple it is to get started. All of the data, analysis, and even a dashboard will be prepared for me by AWS Supply Chain Analytics. You click Add to get started.

Then view the results when navigating to the dashboard page. Additionally, you can share this dashboard with your colleagues, which enhances teamwork.

You can go to Datasets and choose New Datasets if you need to add more datasets in order to create a custom dashboard.

You can leverage an existing dataset in this case, which is the AWS Supply Chain data lake.

You can leverage an existing dataset in this case, which is the AWS Supply Chain data lake.

After that, you may decide which table to use in your analysis. You can view every field that is provided in the Data section. AWS Supply Chain creates all data sets that begin with asc_, including supply planning, demand planning, insights, and other data sets.

Additionally, you can locate every dataset you have added to the AWS Supply Chain. One thing to keep in mind is that before using AWS Supply Chain Analytics, you must ingest data if you haven’t already done so in AWS Supply Chain Data Lake.

You can begin your analysis at this point.

Currently accessible

In every country where AWS Supply Chain is available, AWS Supply Chain Analytics is now widely accessible. Try using AWS Supply Chain Analytics to see how it can change your operations.

Read more on Govindhtech.com