AI Archives | Colaberry

Microsoft Fabric: Disrupting the Data Landscape with Unified Analytics

In today’s data-driven world, organizations are constantly seeking ways to harness the power of data and gain a competitive edge. Microsoft has introduced Microsoft Fabric, an end-to-end analytics platform aimed at revolutionizing the data landscape and paving the way for the era of AI. Fabric integrates various data analytics tools and services into a single unified product, offering organizations a streamlined and comprehensive solution to their data analytics needs.

Unified Analytics Platform

Fabric sets itself apart by providing a complete analytics platform that caters to every aspect of an organization’s analytics requirements. Traditionally, organizations have had to rely on specialized and disconnected services from multiple vendors, resulting in complex and costly integration processes. With Fabric, organizations can leverage a unified experience and architecture through a single product, eliminating the need for stitching together disparate services from different vendors.

By offering Fabric as a software-as-a-service (SaaS) solution, Microsoft ensures seamless integration and optimization, enabling users to sign up within seconds and derive real business value within minutes. This approach simplifies the analytics process and reduces the time and effort required for implementation, allowing organizations to focus on extracting insights from their data.

Comprehensive Capabilities

Microsoft Fabric encompasses a wide range of analytics capabilities, including data movement, data lakes, data engineering, data integration, data science, real-time analytics, and business intelligence. By integrating these capabilities into a single solution, Fabric enables organizations to manage and analyze vast amounts of data effectively. Moreover, Fabric ensures robust data security, governance, and compliance, providing organizations with the confidence to leverage their data without compromising privacy or regulatory requirements.

Simplified Operations and Pricing

Fabric offers a streamlined approach to analytics by providing an easy-to-connect, onboard, and operate solution. Organizations no longer need to struggle with piecing together individual analytics services from multiple vendors. Fabric simplifies the process by offering a single, comprehensive solution that can be seamlessly integrated into existing environments, reducing complexity and improving operational efficiency.

In terms of pricing, Microsoft Fabric introduces a transparent and simplified pricing model. Organizations can purchase Fabric Capacity, a billing unit that covers all the data tools within the Fabric ecosystem. This unified pricing model saves time and effort, allowing organizations to allocate resources to other critical business and technological needs. The Fabric Capacity SKU offers pay-as-you-go pricing, ensuring cost optimization and flexibility for organizations.

Synapse Data Warehouse in Microsoft Fabric

As part of the Fabric platform, Microsoft has introduced the Synapse Data Warehouse, a next-generation data warehousing solution. Synapse Data Warehouse natively supports an open data format, providing seamless collaboration between IT teams, data engineers, and business users. It addresses the challenges associated with traditional data warehousings solutions, such as data duplication, vendor lock-ins, and governance issues.

Key features of Synapse Data Warehouse include:

a. Fully Managed Solution: Synapse Data Warehouse is a fully managed SaaS solution that extends modern data architectures to both professional developers and non-technical users. This enables enterprises to accomplish tasks more efficiently, with the provisioning and managing of resources taken care of by the platform.

b. Serverless Compute Infrastructure: Instead of provisioning dedicated clusters, Synapse Data Warehouse utilizes a serverless compute infrastructure. Resources are provisioned as job requests come in, resulting in resource efficiencies and cost savings.

c. Separation of Storage and Compute: Synapse Data Warehouse allows enterprises to scale and pay for storage and compute separately. This provides flexibility in managing resource allocation based on specific requirements.

d. Open Data Standards: The data stored in Synapse Data Warehouse is in the open data standard of Delta-Parquet, enabling interoperability with other workloads in the Fabric ecosystem and the Spark ecosystem. This eliminates the need for data movement and enhances data accessibility [3].

Microsoft Fabric represents a disruptive force in the data landscape by providing organizations with a unified analytics platform that addresses their diverse analytics needs. By integrating various analytics tools and services, Fabric simplifies the analytics process, reduces complexity, and enhances operational efficiency. The introduction of Synapse Data Warehouse within the Fabric ecosystem further strengthens the platform by providing a next-generation data warehousing solution that supports open data standards, collaboration, and scalability. With Fabric, Microsoft aims to empower organizations to unlock the full potential of their data and embrace the era of AI.

Let us know what you think! Will Fabric be a game-changing disruptor or is MS just playing catchup to Snowflake? Which SaaS do you think will hold the most market share by the end of 2023?

Spotlight on School Events: A Look Into The Exciting Activities Happening Every Month!

Step into the world of boundless opportunities at our weekly and monthly Blog events, designed to empower and equip students and professionals with cutting-edge skills in Business Intelligence and Analytics. Brace yourself for an awe-inspiring lineup of events, ranging from Power BI and Data Warehouse events, SQL Wednesday events, Qlik and Tableau events, and IPBC Saturday events, to multiple sessions, focused on helping students ace their coursework and mortgage projects.

Power BI Event (Monday, 7:30 pm CST)

Data Warehouse (ETL) Event (Monday, 7:30 pm CST)

Our Power BI and Data Warehouse event is an excellent opportunity for beginners and professionals to learn and improve their skills in creating effective data visualizations and building data warehouses. Our experienced trainers will provide a comprehensive overview of the latest tools and techniques to help you unlock the full potential of Power BI and Data Warehouse. Join us on Monday at 7:30 pm CST to learn more.

SQL Wednesday Event (2nd and 3rd Wednesday 7:30pm CST)

Our SQL Wednesday event is designed to help participants gain in-depth knowledge and understanding of SQL programming language. The event is divided into two sessions on the 2nd and 3rd Wednesday of every month, where we cover different topics related to SQL programming. Our experts will guide you through the nuances of SQL programming, and teach you how to use the language to extract insights from large datasets.

Tableau Events (Thursday 7:30 pm CST)

Qlik Events (Thursday 7:30 pm CST)

Our Qlik and Tableau events are dedicated to helping participants master the art of data visualization using these powerful tools. Whether you are a beginner or an experienced professional, our trainers will provide you with valuable insights and best practices to create compelling data stories using Qlik and Tableau. Join us on Thursday to learn how to make sense of complex data and present it in an engaging and impactful way.

IPBC Saturday Event (Saturday at 10 am CST)

Our IPBC Saturday event is designed to provide participants with a broad understanding of the fundamentals of business analytics, including predictive analytics, descriptive analytics, and prescriptive analytics. Our trainers will provide hands-on experience with the latest tools and techniques, and demonstrate how to apply analytics to real-world business problems.

Mortgage Project Help (Monday, Wednesday, & Thursday at 7:30 pm CST)

For those students who need help with their mortgage projects, we have dedicated sessions on Monday, Wednesday, and Thursday at 7:30 pm CST. Our experts will guide you through the process of creating a successful mortgage project, and help you understand the key factors that contribute to a successful project.

Homework help (Wednesday, Thursday, Saturday)

We understand that students may face challenges in their coursework, and may need additional help to understand concepts or complete assignments. That’s why we offer dedicated homework help sessions on Wednesday at 8 pm CST, Thursday at 7:30 pm CST, and Saturday at 1:30 pm CST. Our tutors will provide personalized guidance and support to help you overcome any challenges you may face in your coursework.

CAP Competition Event (1st Wednesday of the Month at 7:30 pm CST)

We have our monthly CAP competition where students showcase their communication skills and compete in our Monthly Data Challenge. Open to all students, this event offers a chance to sharpen skills and showcase abilities in front of a live audience. The top three winners move on to the next level. The event is free, so come and support your fellow classmates on the 1st Wednesday of every month at 7:30 pm CST. We look forward to seeing you there!

The Good Life Event (1st Thursday of the Month at 10 am CST)

Good Life event on the 1st Thursday of every month at 10 am CST. Successful alumni come to share their inspiring success stories and offer valuable advice to current students. Don’t miss this opportunity to gain insights and learn from those who have already achieved success. It’s an event not to be missed, so mark your calendar and join us for the next Good Life event.

Data Talent Showcase Event (4th Thursday of Every Month at 4 pm CST)

Our Data Talent Showcase Event is the next level of the CAP Competition where the top three winners compete against each other. It’s an event where judges from the industry come to evaluate and select the winner based on the projects presented. This event is a great opportunity for students to showcase their skills and receive feedback from industry experts. Join us at the event and witness the competition among the best students, and see who comes out on top!

Discover the electrifying world of events that Colaberry organizes for students and alumni, aimed at fostering continuous growth and progress in the ever-evolving realm of Business Intelligence and Analytics. With an ever-changing landscape, our dynamic and captivating lineup of events ensures that you stay ahead of the curve and are continuously intrigued. Get ready to be swept off your feet by the exciting opportunities that await you!

To see our upcoming events, <click here>

5 Most Powerful Ways AI is Revolutionizing Business Analytics

AI is revolutionizing business analytics enabling businesses to make intelligent decisions faster and with more accuracy. Is your company keeping up with the competition? Here are the five ways AI is transforming business analytics:

Predicting the future, identifying new opportunities, and helping companies make better decisions.
Analyzing data in real-time and providing valuable insights into business operations.
Personalizing offerings, improving customer experience, and increasing customer loyalty.
Identifying potential risks and fraud in real-time.
Making AI-powered analytics tools affordable for smaller companies to compete with larger ones.

Real-time data analysis and decision-making are essential for businesses to keep pace with the fast-changing market. AI algorithms analyze customer feedback and social media sentiment, providing valuable insights for improving marketing strategies and product offerings. Businesses use AI-powered analytics to personalize marketing campaigns and improve customer experiences, leading to higher conversion and loyalty.

business analytics with AI image — **The tech is available but do you have the talent to use it?**

Predictive analytics and forecasting. AI algorithms can interpret large volumes of data, generating insights that help businesses make informed decisions. With predictive analytics, businesses can understand customer behavior, optimize inventory levels, and identify new market opportunities.

Helping companies identify potential risks and fraud in real-time to detect any unusual activity that may indicate fraud. This is important in today’s highly competitive business environment, where fraudsters are always looking for new ways to exploit vulnerabilities. By detecting and preventing fraudulent activity in real time, businesses can save money and protect their reputation. Thanks to AI, businesses can stay ahead of potential risks and fraud, giving them a competitive advantage.

AI is changing the way businesses handle mundane or repetitive tasks. Chatbots and personalized recommendations are helping businesses interact with customers, providing 24/7 support and tailored products to customer preferences. AI-powered analytics can automate routine tasks and identify inefficiencies, leading to cost savings and revenue growth.

AI is changing business analytics in significant ways, enhancing decision-making processes, improving customer experiences, and increasing profitability.
Where it leads us is anyone’s guess. Businesses that embrace AI-powered analytics can leverage it to drive growth and achieve their goals.
Interested in finding out what a digital transformation would look like for your business? Or not sure where to start? Then reach out to us at Colaberry, our only business is data and we have everything you need to start or finish your digital transformation. Under budget and on time.

Andrew “Sal” Salazar
682.375.0489
[email protected]

The Hidden Cost of Development or Technical Debt – Spotting And Stopping It

The Hidden Cost of Development or Technical Debt – Spotting And Stopping It

Technical debt is an often hidden cost a company incurs when a data department is forced to take shortcuts in a project or software development. It is the result of developers’ decisions to prioritize speed over long-term efficiency and stability and not having adequate resources to ensure overall quality. These decisions lead to the accumulation of errors, making the system harder to maintain and scale over time. Technical debt often accumulates unnoticed, as companies focus on delivering products quickly rather than addressing the underlying issues.

How to know if you are accumulating technical debt. What you should look out for:

Delayed project timelines: Technical debt can cause projects to take longer to complete, as developers have to spend more time fixing issues with patches and one-off solutions as they continue to build on it or use it for a longer period of time.
Decreased quality: Technical debt can lead to low-quality products, making it harder to maintain and scale the system over time.
High maintenance costs: Technical debt can become more expensive to maintain over time, as developers have to spend more time fixing bugs and maintaining the project.

Avoiding it altogether is the smartest solution however, it is often not noticed until it is a huge impediment to continued progress. One way to avoid it from the beginning is to use an outside firm like Colaberry to help with maturity assessments that evaluate the maturity of your data landscape and provide recommendations for improvements and prioritization. Using an outside company helps ensure you receive unbiased feedback and evaluations as they are not invested in any particular product or solution which is a possibility with internal evaluations.

These services provide businesses with the necessary expertise, tools, and infrastructure to be able to analyze the data and develop solutions that improve efficiency, stability, and scalability. By using managed data services, your businesses can focus on delivering features quickly while also ensuring that your systems remain efficient and stable over time.

**Having the resources to flex with a project or product needs can be the key to long-term success rather than trying to retain the talent you need on a full-time basis.**

Another solution to avoiding technical debt is to ensure you have an adequate amount of analysts who are skilled in the latest tech stacks to identify areas of technical debt and develop solutions that improve efficiency, stability, and scalability. When you choose Colaberry as a partner you get data talents who are skilled in using the latest technology such as AI & Chat GPT to ensure they can meet your product’s technical demands on time and on budget.

Technical debt can have significant consequences on your overall system’s health and competitiveness. By using managed data services to oversee your data department or hiring additional data analytics talent from Colaberry, you can prevent technical debt from accumulating in the first place.
Colaberry has a team of experienced data analytics professionals who can analyze complex systems and develop solutions that improve efficiency, stability, and scalability. Don’t let technical debt hold your business back; contact Colaberry today to discuss a complimentary maturity assessment or what specific types of talents you need to get the job done. Colaberry is your source for simple data science talent solutions.

Andrew “Sal” Salazar
[email protected]
682.375.0489
LinkedIn Profile

The Power or Pain of Retention: Your Super Power or Achilles Heel?

The Power or Pain of Retention: Your Super Power or Achilles Heel?

In today’s fast-paced business world, retaining talented employees has become one of the biggest challenges for companies. Retention is particularly important when it comes to data analytics departments, where skilled employees can make or break a company’s ability to stay ahead of the competition and the average tenure is 2.43 years. In this article, we’ll explore how employee retention can be your company’s superpower because work life is different than it was just a decade ago.

Employee retention is particularly important in data analytics departments where the field is constantly evolving, and skilled employees who have built up institutional knowledge are essential for staying ahead of the competition. Losing talented employees can set a company back in both the short and long term depending on the role and how in demand the skillset happens to be in the marketplace.

Benefits of Retaining Skilled Employees of retaining long-term employees are easy to list:

institutional knowledge and experience
Cost Savings-Replacing employees can be expensive, especially when it comes to highly skilled data analytics professionals.
Improved Productivity-Skilled employees who are familiar with the company’s data analytics processes and tools are more productive than new hires
Increased Morale-Hard to create a sense of team when you are suffering from churn

Now we get to the good part. How to retain top talent.
Training and opportunities for personal & professional GROWTH. In the last week, I’ve been approached by two Colaberry Alumni who have pretty good jobs with well-known companies to approach me for help finding another role.
My first question was why? More money was actually the second thing listed. They both felt stagnant in their roles as the company was stuck using legacy tech and they had no growth opportunities. Their companies did not offer or encourage any upskilling or continuing education.

**Colaberry can be a great resource for upskilling and retraining employees. Why find new talent when you can upskill your existing team?**

Offering competitive salaries and benefits packages is usually the first thing we think about when talking about retention. I believe there are 5 areas of a comp plan each employee looks at

Financial: Annual salary, bonuses, equity, healthcare, benefits, etc.
Psychological: The internal and external meaning you derive from your work. Your connection to the mission, product, work you produce, and praise you receive.
Social: Prestige, job title, and identity capital you receive.
Education: Skills, relationships, and learnings that contribute to your development as a person and professional.
Freedom: Your ability to work on your own terms. It’s the new normal and especially in the data industry time and location constraints can play a big role in an employee’s decision to stay or go.

A positive work culture; includes things like team-building activities, flexible schedules, and a supportive management style. This includes
Providing regular performance feedback to employees to help them understand their strengths and weaknesses. This helps employees feel valued and supported, which can lead to higher levels of engagement and job satisfaction.

Past performance, can be a huge indicator to help assess if a candidate will stick around or take off at the first offer of more pay. While the current sentiment is to not label job hoppers, sometimes it is what it appears to be. I believe these should be judged on an individual’s personality and story. The uncertainty of the last 5 years has been an unexpected adventure for many of us.

All of these take both time and effort, and there is no silver bullet. Setting yourself and the employee up for a long-term relationship takes planning and dedication in your HR and Learning departments. Finding the right formula can take some trial and error, but there are companies who have clearly figured out the formula and have implemented all of these strategies.

If your company needs to explore how it can offer upskilling or reskilling to help stay competitive Colaberry can be a great resource. Colabery Alumni boast an 80% interview to offer track record and have been in their role previous to Colaberry for an average of 4 years. Have you explored alternative options to sourcing your data talent? Contact us today to find out if there’s a solution beyond what you have now.

Andrew “Sal” Salazar
[email protected]
682.375.0489
LinkedIn Profile

The One Question to Ask Chat GPT to Excel in Any Job

Open AI chatgpt image with black background

The One Question to Ask Chat GPT to Excel in Any Job

Have you ever found yourself struggling to complete a task at work or unsure of what questions to ask to gain the skills you need? We’ve all been there, trying to know what we don’t know. But what if there was a simple solution that could help you become better at any job, no matter what industry you’re in?

At Colaberry, we’ve discovered the power of asking the right questions at the right time. Our one-year boot camp takes individuals with no experience in the field and transforms them into top-performing data analysts and developers. And one of the keys to our success is teaching our students how to use Chat GPT and how to ask the right questions.

Everyones talking about Chat GPT but the key to mastery with it lies in knowing how to ask the right question to find the answer you need. What if there was one question you could ask Chat GPT to become better at any job? This a question that acts like a magic key and unlocks a world of possibilities and can help you gain the skills you need to excel in your career.

Are you ready? The question is actually asking for more questions.

“What are 10 questions I should ask ChatGPT to help gain the skills needed to complete this requirement?”

By passing in any set of requirements or instructions for any project, Chat GPT can provide you with a list of questions you didn’t know you needed to ask.

In this example, we used “mowing a lawn”, something simple we all think we know how to do right? But, do we know how to do it like an expert?

Looking at the answers Chat GPT gave us helps us see factors we might not ever have thought of. Now instead of doing something “ok” using what we know and asking a pointed or direct question, we can unlock the knowledge of the entire world on the task!

And the best part? You can even ask Chat GPT for the answers.

Now, imagine you had a team of data analysts who were not only trained in how to think like this but how to be able to overcome any technical obstacle they met.

If you’re looking for talent that not only has a solid foundation in data analytics and how to integrate the newest technology but how to maximize both of those tools, then Colaberry is the perfect partner. We specialize in this kind of forward-thinking training. Not just how to do something, but how to use all available tools to do something, to learn how to do it, and more. Real-life application of “smarter, not harder”.

Our approach is built on learning a foundation of data knowledge that is fully integrated with the latest tech available, to speed up the learning process. We use Chat GPT and other AI tools to help our students become self-sufficient and teach them how to apply their skills to newer and more difficult problem sets.

But, they don’t do it alone. Our tightly knit alumni network consists of over 3,000 data professionals throughout the US, and many of Colaberry’s graduates have gone on to become Data leaders in their organization, getting promoted to roles such as Directors, VPs, and Managers. When you hire with Colaberry, you’re not just hiring one person – you’re hiring a network of highly skilled data professionals.

So why not take the first step toward unlocking the full potential of your data? Let Colaberry supply you with the data talent you need to take your company to the next level.

Want more tips like this? Sign up for our weekly newsletter HERE and get our free training guide: 47 Tips to Master Chat GPT.

Serving Jupyter Notebooks to Thousands of Users

Serving Jupyter Notebooks to Thousands of Users

In our organization, Colaberry Inc, we provide professionals from various backgrounds and various levels of experience, with the platform and the opportunity to learn Data Analytics and Data Science. In order to teach Data Science, the Jupyter Notebook platform is one of the most important tools. A Jupyter Notebook is a document within an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.

In this blog, we will learn the basic architecture of JupyterHub, the multi-user jupyter notebook platform, its working mechanism, and finally how to set up jupyter notebooks to serve a large user base.

Why Jupyter Notebooks?

In our platform, refactored.ai we provide users an opportunity to learn Data Science and AI by providing courses and lessons on Data Science and machine learning algorithms, the basics of the python programming language, and topics such as data handling and data manipulation.

Our approach to teaching these topics is to provide an option to “Learn by doing”. In order to provide practical hands-on learning, the content is delivered using the Jupyter Notebooks technology.

Jupyter notebooks allow users to combine code, text, images, and videos in a single document. This also makes it easy for students to share their work with peers and instructors. Jupyter notebook also gives users access to computational environments and resources without burdening the users with installation and maintenance tasks.

Limitations

One of the limitations of the Jupyter Notebook server is that it is a single-user environment. When you are teaching a group of students learning data science, the basic Jupyter Notebook server falls short of serving all the users.

JupyterHub comes to our rescue when it comes to serving multiple users, with their own separate Jupyter Notebook servers seamlessly. This makes JupyterHub equivalent to a web application that could be integrated into any web-based platform, unlike the regular jupyter notebooks.

JupyterHub Architecture

The below diagram is a visual explanation of the various components of the JupyterHub platform. In the subsequent sections, we shall see what each component is and how the various components work together to serve multiple users with jupyter notebooks.

Components of JupyterHub

Notebooks

At the core of this platform are the Jupyter Notebooks. These are live documents that contain user code, write-up or documentation, and results of code execution in a single document. The contents of the notebook are rendered in the browser directly. They come with a file extension .ipynb. The figure below depicts how a jupyter notebook looks:

Notebook Server

As mentioned above, the notebook servers serve jupyter notebooks as .ipynb files. The browser loads the notebooks and then interacts with the notebook server via sockets. The code in the notebook is executed in the notebook server. These are single-user servers by design.

Hub

Hub is the architecture that supports serving jupyter notebooks to multiple users. In order to support multiple users, the Hub uses several components such as Authenticator, User Database, and Spawner.

Authenticator

This component is responsible for authenticating the user via one of the several authentication mechanisms. It supports OAuth, GitHub, and Google to name a few of the several available options. This component is responsible for providing an Auth Token after the user is successfully authenticated. This token is used to provide access for the corresponding user.

Refer to JupyterHub documentation for an exhaustive list of options. One of the notable options is using an identity aggregator platform such as Auth0 that supports several other options.

User Database

Internally, Jupyter Hub uses a user database to store the user information to spawn separate user pods for the logged-in user and then serve notebooks contained within the user pods for individual users.

Spawner

A spawner is a worker component that creates individual servers or user pods for each user allowed to access JupyterHub. This mechanism ensures multiple users are served simultaneously. It is to be noted that there is a predefined limitation on the number of the simultaneous first-time spawn of user pods, which is roughly about 80 simultaneous users. However, this does not impact the regular usage of the individual servers after initial user pod creation.

How It All Works Together

The mechanism used by JupyterHub to authenticate multiple users and provide them with their own Jupyter Notebook servers is described below.

The user requests access to the Jupyter notebook via the JupyterHub (JH) server.
The JupyterHub then authenticates the user using one of the configured authentication mechanisms such as OAuth. This returns an auth token to the user to access the user pod.
A separate Jupyter Notebook server is created and the user is provided access to it.
The requested notebook in that server is returned to the user in the browser.
The user then writes code (or documentation text) in the notebook.
The code is then executed in the notebook server and the response is returned to the user’s browser.

Deployment and Scalability

The JupyterHub servers could be deployed in two different approaches:
Deployed on the cloud platforms such as AWS or Google Cloud platform. This uses Docker and Kubernetes clusters in order to scale the servers to support thousands of users.
A lightweight deployment on a single virtual instance to support a small set of users.

Scalability

In order to support a few thousand users and more, we use the Kubernetes cluster deployment on the Google Cloud platform. Alternatively, this could also have been done on the Amazon AWS platform to support a similar number of users.

This uses a Hub instance and multiple user instances each of which is known as a pod. (Refer to the architecture diagram above). This deployment architecture scales well to support a few thousand users seamlessly.

To learn more about how to set up your own JupyterHub instance, refer to the Zero to JupyterHub documentation.

Conclusion

JupyterHub is a scalable architecture of Jupyter Notebook servers that supports thousands of users in a maintainable cluster environment on popular cloud platforms.

This architecture suits several use cases with thousands of users and a large number of simultaneous users, for example, an online Data Science learning platform such as refactored.ai

AWS DeepRacer Workshop With Colaberry

Image of Colaberry Data Scientist at Amazon Deepracer Workshop

Ready, Set, AWS DeepRacer

It was a bright Friday afternoon in July in the lovely city of Boston, Massachusetts. We had close to 30 participants ready to race their cars. Sounds straight out of a Fast and Furious movie, right? What if I told you that these participants were actually racing autonomous vehicles on a virtual race track created on Amazon Sagemaker?

This project was the brainchild of Kristina, Pawan, and Sathwik. Earlier this year, they attended a workshop on reinforcement learning for autonomous vehicle racing conducted by Amazon. When they took the idea of conducting workshops in collaboration with Amazon Web Services (AWS) to Ram, the CEO of Colaberry, he was very enthusiastic. And that was what led me to create Colaberry – AWS Deepracer course on Deepracer for quite a few days along with a fellow intern, Manaswi.

We found that Deepracer can be an important tool to introduce people to the world of Machine Learning: they can see its real-world applications and use it in a fun way. The virtual vehicle applies the ML reward function by rewarding an autonomous vehicle for following the track properly and penalizing it for “bad” behaviors like going off track. This reward function utilizes Python programming, and advanced participants in the AWS Deepracer league even prepared their own codes using an exhaustive list of input parameters. For those unfamiliar with coding, the platform also had pre-created reward functions.

As an introductory workshop, we refrained from using complex code, instead explaining the pre-created codes. Advanced users or those seeking to participate in the community race the next day could use the advanced notebooks available. These notebooks provided a deeper understanding of hyperparameters, reward graphs, and other tips that can help a person succeed in the race.

Are you ready to race?

This post was written by Badri Yashwant Kuram, a Colaberry Intern.

Our Program

Colaberry has been providing one-of-a-kind, career-oriented training in data analytics and data science since 2012. We offer instructor-led onsite and online classes. Learn with us in person on our campus in Plano, Texas, or remotely from the comfort of your home. We have helped over 5,000 people to transform their lives with our immersive boot camp-style programs.

In-Demand Skills

Colaberry training programs equip you with in-demand tech and human skills. Our up-to-date lessons and carefully crafted curriculum set you up for success from day one. Throughout the training and the job search, our mentors will support and guide you as you transition into a fast-paced and exciting field of data analytics and data science.

Project-Based Learning

Our programs integrate projects that are based on real-world scenarios to help you master a new concept, tool, or skill. We work with you to build your portfolio to showcase your skills and achievements.

Award Winning Learning Platform

You will be learning using our homegrown technology platform Refactored AI which is recognized as the “Most Promising Work of the Future Solution” in global competition by MIT SOLVE. Our platform also received General Motors’ “Advanced Technology” prize and McGovern Foundation’s “Artificial Intelligence for Betterment of Humanity” prize.

Placement Assistance

Colaberry’s program, platform, and ecosystem empower you with skills that you need to succeed in your job interviews and transition into high-paying careers. Over 1/3rd of Colaberry graduates receive job offers after their first in-person interview. We provide you with continuous mentoring and guidance until you land your job, as well as post-placement support for twelve months so that you not only survive but thrive in your career.

Financial Aid

At Colaberry, we strive to create opportunities for all. We work with each individual to ensure the cost of the training does not hold them back from becoming future-ready. We offer various payment plans, payment options, and scholarships to work with the financial circumstances of our learners.

Military Scholarship

Colaberry is committed to supporting men and women who have served our country in uniform. As part of this commitment, we offer Military Scholarships to enable active-duty and retired military members to transition into civilian life. We have already helped numerous veterans by creating a pathway to rewarding and exciting careers in data science and data analytics. We hold alumni events and provide an extensive support system and a strong community of veterans to help our students succeed. Contact our enrollment team to find out more about how we can help you.

8 Powerful Examples of AI For Good

Explore the positive impact of AI for good in healthcare, education, agriculture, and more. Discover 8 examples of AI being used for good.

Artificial Intelligence (AI) is no longer just a concept we see in movies or books. Today, AI is being used in many ways to make our lives easier, more efficient, and safer. AI is changing the way we do business, the way we interact with technology, and the way we solve problems. In this blog post, we’ll explore eight examples of how AI is being used for good.

Healthcare AI is being used in healthcare in many ways. One example is the use of AI to analyze medical images to diagnose diseases. AI can quickly identify patterns and anomalies that might be missed by human doctors. AI is also being used to develop personalized treatments for patients, based on their genetic makeup and medical history.

Agriculture

AI is being used in agriculture to help farmers increase crop yields and reduce waste. AI-powered systems can analyze soil and weather data to determine the best times to plant, water, and harvest crops. This helps farmers maximize their yields and reduce the amount of water and fertilizer they use.

Transportation

AI is being used in transportation to improve safety and efficiency. Self-driving cars are a prime example of this. By using AI to analyze traffic patterns and road conditions, self-driving cars can navigate roads more safely and efficiently than human drivers.

Education

AI is being used in education to personalize learning and help students achieve their full potential. AI-powered systems can analyze student data to determine the best learning strategies for each student. This helps teachers tailor their lessons to meet the needs of each student, which can lead to better academic outcomes.

Environmental Monitoring

AI is being used to monitor the environment and identify potential hazards. For example, AI-powered systems can analyze satellite images to identify areas that are at risk of wildfires. This can help emergency responders respond more quickly to fires and help prevent them from spreading.

Finance

AI is being used in finance to improve fraud detection and risk management. AI-powered systems can analyze financial transactions to identify patterns that might indicate fraudulent activity. This can help banks and other financial institutions prevent fraud and protect their customers.

Customer Service

AI is being used in customer service to improve response times and customer satisfaction. Chatbots and virtual assistants can answer customer inquiries and resolve issues quickly and efficiently. This helps companies provide better customer service and can lead to increased customer loyalty.

Emergency Response

AI is being used in emergency response to help first responders quickly assess situations and make better decisions. For example, AI-powered systems can analyze video feeds from drones to help emergency responders assess the extent of damage from natural disasters. This can help responders allocate resources more effectively and respond more quickly to emergencies.

In conclusion, AI is being used in many ways to make our lives better. From healthcare and agriculture to transportation and emergency response, AI is helping us solve problems and improve our quality of life. As we continue to develop new AI technologies, we can expect to see even more ways that AI is used for good in the future.

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5 Ways To Ace Your Business Intelligence Interview – Part 5

5 Ways To Ace Your Business Intelligence Interview – Part 5

Acing your next Business Intelligence (BI) interview isn’t easy but with the tips we’ve shared in the previous four mini-blogs in this series, you should be well on your way. Our last mini-blog covers the most important preparation you can make: completing Colaberry’s online data analytics courses.

1) The First 5 Minutes Matter Most!

2) Come Prepared – Know Exactly What the Role Demands

3) Dress for Success

4) Master Core Human Skills

5) Complete Colaberry’s Online Data Analytics Course

Complete Colaberry’s Online Data Analytics Course

Colaberry’s online data analytics and data science courses allow you to focus on your future career while still being able to spend time with your family and loved ones. Expert instructors help you to get through real-world situations and make sure you are ready for your business intelligence career.

4 Benefits of Completing Colaberry’s Online Data Analytics Program

1) You can spend time with your family while pursuing your dreams.

2) You’ll receive cutting-edge interview preparation with our Refactored.ai mock interview system.

3) You’ll gain access to a supportive student and alumni network that will always be there to assist you in preparing for your next Business Intelligence interview.

4) You can transition to a new career fast – and protect yourself from future obstacles like AI and automation.

Colaberry is completely focused on helping you achieve your dreams. Listen to Adie Mack, one of our successful graduates, who transitioned from teaching to an amazing career as a Business Intelligence Analyst: