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Data is Precious more than the value of Gold!

Data is everywhere and how do organisations decode and digress it defines how successful they would be a winner the data race. Data exists in the cloud, data lakes and

The five pillars that define Data Integration-

1. Earmarking a Budget for Data Ingestion 2. Making Data Resource Ready 3. Data Sanitation and Quality Checks 4. Data Standardization 5. Harnessing Data Insights

Understanding Data Integration Pillars

• Earmarking a Budget for Data Ingestion There goes the adage “Before enterprises can digest data, they must ingest it!” Well true to many organisations, an enterprise needs to access the data sources before it can lay on analytics and data mining algorithms. These • Making Data Resource Ready Before an enterprise performs any transformation or analysis on its data, they must have the resources available, along with data integration tools. • Data Sanitation and Quality Checks The next step is to ensure that the collated data is accurate, complete, and relevant. Bad data = Bad analysis, An unpleasant situation which an Enterprise would love to avoid! Data cleansing tools are essential to creating data pipelines, or analysis-ready data which makes it easy for an analyst to easily harness the data for model building. • Data Standardization and Quality Checks Every enterprise has a separate • Harnessing Data Insights

Data is everywhere and how do organisations decode and digress it defines how successful they would be a winner the data race. Data exists in the cloud, data lakes and data silos , organizations need to collect, organize, and analyse this data to reap long term gains. The data integration journey that organisations go through varies from an organisation structure to another. The persistent question remains, how do enterprises decode their data integration journey? To instil confidence, enterprises must chalk out their data strategy plans. To start with they must check the availability of data at their disposal and resource constraints who will on convert this data into meaningful information!1. Earmarking a Budget for Data Ingestion 2. Making Data Resource Ready 3. Data Sanitation and Quality Checks 4. Data Standardization 5. Harnessing Data InsightsThere goes the adage “Before enterprises can digest data, they must ingest it!” Well true to many organisations, an enterprise needs to access the data sources before it can lay on analytics and data mining algorithms. These data sources may include data storages as well as relational databases and Hadoop sources, data lakes, data warehouses and data silos.Before an enterprise performs any transformation or analysis on its data, they must have the resources available, along with data integration tools. Data delivery is possible when businesses are resilient and adapt to the best resource practices. These resources or data experts can be in house or external vendors that an enterprise may chúng tôi next step is to ensure that the collated data is accurate, complete, and relevant. Bad data = Bad analysis, An unpleasant situation which an Enterprise would love to avoid! Data cleansing tools are essential to creating data pipelines, or analysis-ready data which makes it easy for an analyst to easily harness the data for model building. Data sanitization and quality checks make data enterprise-ready.Every enterprise has a separate need for data , and to catalyse this need enterprises need to ascertain that the data Standardization checks are met along with the persistent quality control checks. This step makes it possible for Data Standardization and quality checks, for an efficient and effectively harnessing data for intelligent chúng tôi final step is to understand a more complete picture of the data at disposal to extract insights. An analyst may use services like Watson Knowledge Catalog to create a smart data catalogue to help with data governance, or IBM Watson Studio AutoAI to quickly develop machine learning models and automate hyperparameter tuning. This process lets enterprises transform data and operationalize data delivery for analytics and business-ready AI use cases.

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The Three Pillars Of European Approach To Ai Excellence

As the world has embarked on a successful

Being ahead of technological developments and encouraging uptake by the public and private sectors

The Commission is increasing its annual investments in AI by 70% under the research and innovation programme Horizon 2023. It will reach EUR 1.5 billion for the period 2023-2023. It will connect and strengthen AI research centers across Europe; support the development of an “

Prepare for socio-economic changes brought about by AI

To support the efforts of the Member States which are responsible for labor and education policies, the Commission will support business-education partnerships to attract and keep more artificial intelligence talent in Europe; set up dedicated training and retraining schemes for professionals; foresee changes in the labor market and skills mismatch; support digital skills and competences in science, technology, engineering, mathematics (STEM), entrepreneurship and creativity; and encourage the Member States to modernize their education and training systems.

Ensure an appropriate ethical and legal framework

As the world has embarked on a successful artificial intelligence (AI) journey to bring about innovative transformation across various domains, several countries have come forward with comprehensive AI strategies to produce a guideline to regulate investment and innovations brought in by the technology. Today we are going to discuss the European AI policies and developments. The European Commission puts forward a European approach to artificial intelligence and robotics. It deals with technological, ethical, legal and socio-economic aspects to boost EU’s research and industrial capacity and to put artificial intelligence at the service of European citizens and economy. As noted by the European Commission, AI has become an area of strategic importance and a key driver of economic development. It can bring solutions to many societal challenges from treating diseases to minimizing the environmental impact of farming. However, socio-economic, legal and ethical impacts have to be carefully addressed. It is essential to join forces in the European Union to stay at the forefront of this technological revolution, to ensure competitiveness and to shape the conditions for its development and use (ensuring respect of European values). In its Communication , the European Commission puts forward a European approach to artificial intelligence-based on three pillars:The Commission is increasing its annual investments in AI by 70% under the research and innovation programme Horizon 2023. It will reach EUR 1.5 billion for the period 2023-2023. It will connect and strengthen AI research centers across Europe; support the development of an “ AI-on-demand platform ” that will provide access to relevant AI resources in the EU for all users; and support the development of artificial intelligence applications in key sectors. However, this represents only a small part of all the investments from the Member States and the private sector. This is the glue linking the individual efforts, to make together a solid investment, with an expected impact much greater than the sum of its parts. Given the strategic importance of the topic and the support shown by the European countries signing the declaration of cooperation during the Digital Day 2023 , it is expected that the Member States and the private sector will make similar efforts. The High-Level Expert Group on Artificial Intelligence (AI HLEG) presented their Policy and Investment Recommendations for Trustworthy AI during the first European AI Alliance Assembly in June 2023. Joining forces at European level, the goal is to reach altogether, more than EUR 20 billion per year over the next chúng tôi support the efforts of the Member States which are responsible for labor and education policies, the Commission will support business-education partnerships to attract and keep more artificial intelligence talent in Europe; set up dedicated training and retraining schemes for professionals; foresee changes in the labor market and skills mismatch; support digital skills and competences in science, technology, engineering, mathematics (STEM), entrepreneurship and creativity; and encourage the Member States to modernize their education and training chúng tôi 19 February 2023, the European Commission published a White Paper aiming to foster a European ecosystem of excellence and trust in AI and a Report on the safety and liability aspects of AI . The White Paper proposes measures that will streamline research, foster collaboration between the Member States and increase investment into AI development and deployment; and policy options for a future EU regulatory framework that would determine the types of legal requirements that would apply to relevant actors, with a particular focus on high-risk applications.

Journey Of An Ai/Ml Specialist At Google: Innovating And Solving Problems Using Cutting

Introduction

There has been an increase in the availability of data and the need for businesses to make technology related and data-driven decisions. Developing sophisticated machine learning algorithms and artificial intelligence techniques has led to a demand for skilled professionals in companies such as Google and Micorsoft.

“Did you know that there is no study guide in the market of Google”

Did you know that? Well, if you didn’t, let’s learn more unknown facts with Ms. Mona! Get ready to be inspired by a person who, after facing challenges, never gave up but came up with solutions to overcome those challenges.

Ms. Mona is a Machine Learning Specialist currently working at Google while having previously worked at Amazon Web Services (AWS). Machine Learning Specialists are especially in demand as they have expertise in developing and implementing algorithms and models using tools such as Python, R, Tensor Flow, etc. to analyze large amounts of data and extract meaningful insights. Working with data scientists and other professionals helps identify problems and develop solutions to make informed decisions about a business/organization.

In this article, you will get to know the following things:

Gain insights into the career trajectory of a successful AI/ML specialist and the skills and experience required to achieve such a role.

Understand the value of pursuing a master’s degree in computer science or a related field to gain expertise in AI and ML.

Gain an understanding of the challenges faced by professionals in the field of AI/ML and the strategies used to overcome these challenges, such as evangelizing solutions and creating awareness.

Explore the ways in which AI/ML can be used to solve real-world problems and create value for organizations in various industries.

The Journey from a Java Developer to AI/ML Specialist AV: Hello, Welcome to analytics vidhya! How are you? Can you please share your professional journey and how you got to where you are today at Google?

Ms. Mona: Hello! Thank you so much for having me. As for my professional journey, after graduating from Indraprastha University, Delhi, I started working as a Java Developer. Then, I completed my post-graduation in Computer Information Systems from Georgia State University.

During my master’s course, my major was in Big Data Analytics. I was also introduced to Machine Learning courses which inspired me most as I loved Machine Learning. I wanted to become a Data Scientist. However, getting into a Data Science career without a Ph.D. was hard. Before coming to the US, I already had multiple certifications, such as AWS Solutions Architect, Scrum Master, and others. My resume got picked by AWS for the Associate Solution Architect program, which was the first of its kind. I was fortunate to interview and selected to join Amazon in 2023 as a Solution Architect. After joining Amazon as an Associate Solution Architect, I got the opportunity to specialize in Machine Learning and move to the Machine Learning Specialist Solution Architect role.

AV: That’s an inspiring story! So, after joining Amazon as an associate solution architect, You got the opportunity to specialize in machine learning and move to the machine learning specialist solution architect role. Can you share an example of a project or initiative you’ve worked on at Amazon that you’re particularly proud of, And what impact did it have?

Source: ostomylifestyle.org

Ms. Mona: I authored 17 blogs and got the chance to work on a research paper on Neural Search called Cord 19 search. I also spoke at multiple conferences and wrote launch blogs. I also had all AWS cloud certifications and authored a book called Natural Language Processing using AWS AI services.

Ms. Mona: I worked on the RadLab platform for alphaFold protein folding. I presented this at the International Conference for Molecular Biology in 2023. This RadLab AlphaFold provides an automated cloud environment to researchers and solves protein folding problems. Previously it used to take decades to fold a protein. With Deepmind’ making the AlphaFold model available, it is possible to fold protein sequence and visualize it in hours rather than decades.

Source: 9to5Google

You can refer to my blog to learn more

Another initiative is the book I am about to publish called Google Cloud Certified ML Engineer which is currently in preview. This is going to be an official Google Study Guide.

AV: You also spoke for Analytics Vidhya’s data hour in April 2023. How was your experience as a guest speaker?

Ms. Mona: So, talking about the Technical skills, which include, Ability to code and understand overall architecture such as networking, security, storage, computing, and AI/ML.

Ms. Mona: Problem-solving is about understanding the problem first. Some of the ways I do

Listen actively to what the customer wants or what the problem is.

Ask clarifying, meaningful questions to understand the problem.

If I don’t know the solution, search internally or ask for experts in the domain area for help. It’s always helpful to seek expert guidance before killing yourself to find an answer.

Provide a holistic cloud AI solution taking 1,2 and 3 into consideration.

Ms. Mona: The biggest challenge is that space AI is evolving in a space that is difficult to keep up. It’s exciting and overwhelming at the same time. With Google Cloud Generative AI offerings, many new use cases can be solved easily with LLMs and large language models, which was not possible before.

Ms. Mona: Google Cloud has responsible AI practices integrated into all its AI/ML offerings

Ms. Mona: I would recommend that you keep exploring Cloud AI solutions. As we would need more people in this space. My idea for writing the book “Natural language processing using AWS AI Services was to empower students and IT professionals to get started with machine learning with no previous expertise with low/code no code AWS AI services.

AV: Thanks for your time! This will be helpful for all aspirants who want to go into data sciences or data fields. So, Let’s conclude today’s discussion. Conclusion

To conclude this success story, Ms. Mona’s journey is truly inspirational for anyone who wants to pursue a career in the field of AI/ML. From her humble beginnings in India to her current position at Google, Ms. Mona’s dedication and hard work have brought her success and recognition in the industry.

Her work on the RadLab platform for alphaFold protein folding and the upcoming Google Cloud Certified ML Engineer book exemplify her expertise in the field. Through her efforts, Ms. Mona has contributed to the AI/ML community and helped solve real-world problems using cutting-edge technology.

Her experience as a guest speaker for Analytics Vidhya’s Data Hour also highlights her willingness to share her knowledge with others and help them succeed in their careers. Ms. Mona’s story is a testament to the fact that anyone can succeed in their chosen field with passion and hard work.

Power Bi Copilot: Enhancing Data Analysis With Ai Integration

Are you ready to elevate your data analysis capabilities? Then let’s delve into the realm of Power BI Copilot and its AI integration. This tool isn’t just another addition to your data analysis toolkit; it’s akin to having an intelligent assistant, always ready to help you navigate through your data.

In this article, we’ll explain how Power BI Copilot works and how you can leverage it to empower you and your organization.

Let’s get started!

Copilot is an AI tool that provides suggestions for code completion. The tool is powered by Codex, an AI system developed by OpenAI that can generate code from a user’s natural language prompts.

Copilot already has Git integration in GitHub Codespaces, where it can be used as a tool for writing tests, fixing bugs, and autocompleting snippets from plain English prompts like “Create a function that checks the time” or “Sort the following information into an alphabetical list.”

The addition of Copilot in Power BI has infused the power of large language models into Power BI. Generative AI can help users get more out of their data. All you have to do is describe the visuals or the insights you want, and Copilot will do the rest.

With Copilot, you can:

Create and tailor Power BI reports and gain insights in minutes

Generate and refine DAX calculations

Ask questions about your data

Create narrative summaries

All of the above can be done using conversational language. Power BI already had some AI features, such as the quick measure suggestions that help you come up with DAX measures using natural language, but Copilot takes it to the next level.

With Copilot, you can say goodbye to the tedious and time-consuming task of sifting through data and hello to instant, actionable insights. It’s the ultimate assistant for uncovering and sharing insights faster than ever before.

Some of its key features include:

Automated report generation: Copilot can automatically generate well-designed dashboards, data narratives, and interactive elements, reducing manual report creation time and effort.

Conversational language interface: You can describe your data requests and queries using simple, conversational language, making it easier to interact with your data and obtain insights.

Real-time analytics: Power BI users can harness Copilot’s real-time analytics capabilities to visualize data and respond quickly to changes and trends.

Alright, now that we’ve gone over some of the key features of Power BI Copilot, let’s go over how it can benefit your workflow in the next section.

Looking at Power BI Copilot’s key features, it’s easy to see how the tool has the potential to enhance your data analysis experience and business decision-making process.

Some benefits include:

Faster insights: With the help of generative AI, Copilot allows you to quickly uncover valuable insights from your data, saving time and resources.

Ease of use: The conversational language interface makes it easy for business users with varying levels of technical expertise to interact effectively with the data.

Reduced time to market: Using Copilot in Power Automate can reduce the time to develop workflows and increase your organization’s efficiency.

Using Power BI Copilot’s features in your production environments will enable you to uncover meaningful insights from your data more efficiently and make well-informed decisions for your organization. However, the product is not without its limitations, as you’ll see in the next section.

Copilot for Microsoft Power BI is a new product that was announced together with Microsoft Fabric in May 2023. However, it’s still in private preview mode and hasn’t yet been released to the public. There is no official public release date, but it’ll likely be launched before 2024.

Some other limitations of Copilot include:

Quality of suggestions: Copilot is trained in all programming languages available on public repositories. However, the quality of the suggestions may depend on the volume of the available training dataset for that language. Suggestions for niche programming languages (APL, Erlang, Haskell, etc.) won’t be as good as those of popular languages like Python, Java, C++, etc.

Doesn’t understand context like a human: While the AI has been trained to understand context, it is still not as capable as a human developer in fully understanding the high-level objectives of a complex project. It may fail to provide appropriate suggestions in some complicated scenarios.

Lack of creative problem solving: Unlike a human developer, the tool cannot come up with innovative solutions or creatively solve problems. It can only suggest code based on what it has been trained on.

Possible legal and licensing issues: As Copilot uses code snippets from open-source projects, there are questions about the legal implications of using these snippets in commercial projects, especially if the original code was under a license that required derivative works to be open source as well.

Inefficient for large codebases: The tool is not optimized for navigating and understanding large codebases. It’s most effective at suggesting code for small tasks.

While Power BI Copilot offers a compelling platform for data analytics and visualization, its limitations shouldn’t be overlooked. You have to balance the undeniable benefits of Copilot with its constraints and align the tool with your unique operational needs.

As we mentioned in the previous section, Copilot for Power BI was announced at the same time as Microsoft Fabric, so naturally, there’s a lot of confusion about whether Fabric is replacing Power BI or whether Power BI is now a Microsoft Fabric product.

Microsoft Fabric is a unified data foundation that’s bringing together several data analysis tools under one umbrella. It’s not replacing Power BI; instead, it’s meant to enhance your Power BI experience.

Power BI is now one of the main products available under the Microsoft Fabric tenant setting. Some other components that fall under the Fabric umbrella include:

Data Factory: This component brings together the best of Power Query and Azure Data Factory. With Data Factory, you can integrate your data pipelines right inside Fabric and access a variety of data estates.

Synapse Data Engineering: Synapse-powered data engineering gives data professionals an easy way to collaborate on projects that involve data science, business intelligence, data integration, and data warehousing.

Synapse Data Science: Synapse Data Science is designed for data scientists and other data professionals who work with large data models and want industry-leading SQL performance. It brings machine-learning tools, collaborative code authoring, and low-code tools to Fabric.

Synapse Data Warehousing: For data warehousing professionals, Synapse Data Warehouse brings the next-gen of data warehousing capabilities to Fabric with open data formats, cross-querying, and automatic scaling.

Synapse Real-Time Analytics: This component simplifies data integration for large organizations and enables business users to gain quick access to data insights through auto-generated visualizations and automatic data streaming, partitioning, and indexing.

OneLake: The “OneDrive for data,” OneLake is a multi-cloud data lake where you can store all an organization’s data. It’s a lake-centric SaaS solution with universal compute capacities to enable multiple developer collaboration.

Through Fabric, Microsoft is bringing the capabilities of machine learning models to its most popular data science tools. There are other components, like Data Activator, which are still in private preview and are not yet available in Fabric.

Microsoft Fabric is available to all Power BI Premium users with a free 60-day trial. To get started, go to the Power BI admin portal and opt-in to start the free trial.

In a world brimming with data, Copilot might just be the ‘wingman’ you need to make your data speak volumes. It’s turning Power BI into a human-centered analytics product that enables both data engineers and non-technical users to explore data using AI models.

Whether you’re a small business trying to make sense of customer data or a multinational figuring out global trends, give Copilot a whirl and let it take your data analysis to the next level. Happy analyzing!

To learn more about how to use Power BI with ChatGPT to supercharge your organization’s reports, check out the playlist below:

Copilot in Power BI is still in private preview, but it will become available to Power BI customers soon. With this tool, users can use natural language queries to write DAX formulas, auto-generate complete reports using Power BI data, and add visualizations to existing reports.

To use Copilot in Power BI, all you have to do is write a question or request describing what you want, such as “Help me build a report summarizing the profile of customers who have visited our homepage.” If you want Copilot to give you suggestions, type “/” in the query box.

Once Copilot for Power BI comes out of private preview, it’ll be available at no extra cost to all Power BI license holders (pro or premium).

The Importance Of First Party Data Activation

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Cookies going away in Chrome?

They already have been eliminated from the most popular browser on the mobile market – Safari.

How does this affect marketing & sales? What about Shopify merchants?

Brent Ramos, Product Director at Adswerve, joined me to discuss incremental measurement in ecommerce and beyond.

We talked about the importance of first-party data and the possibility of losing a lot of the third-party data that we’re getting through cookies on the Chrome browser.

Third-party data will probably always exist in some format, to some kind of degree, and not all third-party data is bad. First-party data is certainly not bad. It’s required for many daily things that we as consumers experience that we enjoy. So those first-party cookies will persist and will persist more than the third card, third-party cookies. –Brent Ramos, 05:58

Those touchpoints make up a full, wholesome persona of what a real human being could look like. And so it’s not a matter of how you collect it, but it’s a matter of having you started? And what are you doing? See it with an eye to activation. –Brent Ramos, 07:20

From the consumers’ point of view, they will be getting a better experience. They should be able to have better conversations with their brands across all of the different touchpoints and channels in a way that’s responsible and appropriate. And it’s useful all at the same time. –Brent Ramos, 11:10

[22:01] – Samples of first-party campaigns.

Resources mentioned:

So the faster you can get first-party data and lifetime value modeling embedded into your bids, the better you will be. And you won’t have to worry about competition nearly as much when you know you can do that. –Brent Ramos, 26:52

Once you add lifetime value into the equation, no matter what the attribution channel, you’re just talking in a different language. Which is more so marketing than direct response that we’re used to talking with an SEO. –Loren Baker, 24:07

It’s only going to force firms and agencies to become better storytellers. That’s really what the core component is. –Brent Ramos, 11:10

Connect with Brent Ramos:

Brent is a seasoned digital entrepreneur and ad tech expert with over 15 years of experience. He combines his expertise in front-line tactics and high-level strategy to help clients use the Google Marketing Platforms to achieve their goals.

He has been focused on delivering the highest level of predictable success possible based on new ideas that lead to high-level strategic marketing success as Product Director at Adswerve.

Connect with Loren Baker, Founder of Search Engine Journal:

Why Synthetic Data And Deepfakes Are The Future Of Data Analytics?

Synthetic data can help test exceptions in software design or software response when scaling.

It’s impossible to understand what’s going on in the enterprise technology space without first understanding data and how data is driving innovation.

What is synthetic data?

Synthetic data is data that you can create at any scale, whenever and wherever you need it. Crucially, synthetic data mirrors the balance and composition of real data, making it ideal for fueling machine learning models. What makes synthetic data special is that data scientists, developers, and engineers are in complete control. There’s no need to put your faith in unreliable, incomplete data, or struggle to find enough data for machine learning at the scale you need. Just create it for yourself.

What is Deepfake?

Deepfake technology is used in synthetic media to create falsified content, replace or synthesizing faces, and speech, and manipulate emotions. It is used to digitally imitate an action by a person that he or she did not commit.

Advantages of deepfakes:

Bringing Back the Loved Ones! Deepfakes have a lot of potential users in the movie industry. You can bring back a decedent actor or actress. It can be debated from an ethical perspective, but it is possible and super easy if we do not think about ethics! And also, probably way cheaper than other options.

Chance of Getting Education from its Masters

Just imagine a world where you can get physics classes from Albert Einstein anytime, anywhere! Deepfake makes impossible things possible. Learning topics from its masters is a way motivational tool. You can increase the efficiency, but it still has a very long way to go.

Can Synthetic Data bring the best in Artificial Intelligence (AI) and Data Analytics?

In this technology-driven world, the need for training data is constantly increasing. Synthetic data can help meet these demands. For an AI and data analytics system, there is no ‘real’ or ‘synthetic’; there’s only data that we feed it to understand. Synthetic data creation platforms for AI training can generate the thousands of high-quality images needed in a couple of days instead of months. And because the data is computer-generated through this method, there are no privacy concerns. At the same time, biases that exist in real-world visual data can be easily tackled and eliminated. Furthermore, these computer-generated datasets come automatically labeled and can deliberately include rare but crucial corner cases, even better than real-world data. According to Gartner, 60 percent of the data used for AI and data analytics projects will be synthetic by 2024. By 2030, synthetic data and deepfakes will have completely overtaken real data in AI models.

Use Cases for Synthetic Data

There are a number of business use cases where one or more of these techniques apply, including:

Software testing: Synthetic data can help test exceptions in software design or software response when scaling.

User-behavior: Private, non-shareable user data can be simulated and used to create vector-based recommendation systems and see how they respond to scaling.

Marketing: By using multi-agent systems, it is possible to simulate individual user behavior and have a better estimate of how marketing campaigns will perform in their customer reach.

Art: By using GAN neural networks, AI is capable of generating art that is highly appreciated by the collector community.

Simulate production data: Synthetic data can be used in a production environment for testing purposes, from the resilience of data pipelines to strict policy compliance. The data can be modeled depending on the needs of each individual.