Tag Archives: science

A new partnership to support computer science teachers

Today we are excited to announce a new partnership with the Computer Science Teachers Association (CSTA). Microsoft will provide $2 million, over three years, to help CSTA launch new chapters and strengthen existing ones. It will help them attract new members and partners to build a stronger community to serve computer science teachers.

 We’re thrilled that students of all ages are discovering the exciting – and critical – field of computer science. From the Hour of Code, to Minecraft Education, and even Advanced Placement Computer Sciences courses, participation rates are expanding. This surge of student interest, combined with the premium our economy places on technology skill of all kinds, requires us to do all we can to ensure every student has access to computer science courses. And it all starts with our teachers.  

 Nearly every teacher belongs to a professional membership organization, from social studies, to reading, to math and science. These organizations provide teachers with subject-specific professional development, up-to-date curriculum, and networking opportunities with peers and other professionals. CSTA was started in 2004 to fill this need for computer science teachers. But to meet today’s needs in this quickly changing and growing field of study, CSTA is expanding as well. We are proud to support them!

 Our investment in CSTA continues Microsoft Philanthropies’ long-standing commitment to computer science education through our Technology Education and Literacy in Schools (TEALS) program, which pairs technology industry volunteers with classroom teachers to team-teach computer science in 350 U.S. high schools. It builds on our investments in nonprofits such as Code.org, Girls Who Code, and Boys & Girls Clubs of America, with whom we partnered to create a computer science learning pathway. And it builds on our work advocating at a state and federal level for policy change and investments in computer science education across the United States.  

While technology can be a powerful learning tool, nothing can replace the expertise, guidance, and encouragement that teachers provide to students each day of the school year. I remember my own favorite teachers who helped me see a world beyond the rural town in which I grew up. I would guess that nearly everyone has a similar story. We thank our teachers and we hope that this investment in computer science teachers, through CSTA, empowers more educators to do what they do best: make a positive difference in the lives of students. To learn how you can help CSTA serve teachers, please visit https://www.csteachers.org/page/GetInvolved.

Building a data science pipeline: Benefits, cautions

Enterprises are adopting data science pipelines for artificial intelligence, machine learning and plain old statistics. A data science pipeline — a sequence of actions for processing data — will help companies be more competitive in a digital, fast-moving economy. 

Before CIOs take this approach, however, it’s important to consider some of the key differences between data science development workflows and traditional application development workflows.

Data science development pipelines used for building predictive and data science models are inherently experimental and don’t always pan out in the same way as other software development processes, such as Agile and DevOps. Because data science models break and lose accuracy in different ways than traditional IT apps do, a data science pipeline needs to be scrutinized to assure the model reflects what the business is hoping to achieve.

At the recent Rev Data Science Leaders Summit in San Francisco, leading experts explored some of these important distinctions, and elaborated on ways that IT leaders can responsibly implement a data science pipeline. Most significantly, data science development pipelines need accountability, transparency and auditability. In addition, CIOs need to implement mechanisms for addressing the degradation of a model over time, or “model drift.” Having the right teams in place in the data science pipeline is also critical: Data science generalists work best in the early stages, while specialists add value to more mature data science processes.

Data science at Moody’s

Jacob Grotta, managing director, Moody's AnalyticsJacob Grotta

CIOs might want to take note from Moody’s, the financial analytics giant, which was an early pioneer in using predictive modeling to assess the risks of bonds and investment portfolios. Jacob Grotta, managing director at Moody’s Analytics, said the company has streamlined the data science pipeline it uses to create models in order to be able to quickly adapt to changing business and economic conditions.

“As soon as a new model is built, it is at its peak performance, and over time, they get worse,” Grotta said. Declining model performance can have significant impacts. For example, in the finance industry, a model that doesn’t accurately predict mortgage default rates puts a bank in jeopardy. 

Watch out for assumptions

Grotta said it is important to keep in mind that data science models are created by and represent the assumptions of the data scientists behind them. Before the 2008 financial crisis, a firm approached Grotta with a new model for predicting the value of mortgage-backed derivatives, he said. When he asked what would happen if the prices of houses went down, the firm responded that the model predicted the market would be fine. But it didn’t have any data to support this. Mistakes like these cost the economy almost $14 trillion by some estimates.

The expectation among companies often is that someone understands what the model does and its inherent risks. But these unverified assumptions can create blind spots for even the most accurate models. Grotta said it is a good practice to create lines of defense against these sorts of blind spots.

The first line of defense is to encourage the data modelers to be honest about what they do and don’t know and to be clear on the questions they are being asked to solve. “It is not an easy thing for people to do,” Grotta said.

A second line of defense is verification and validation. Model verification involves checking to see that someone implemented the model correctly, and whether mistakes were made while coding it. Model validation, in contrast, is an independent challenge process to help a person developing a model to identify what assumptions went into the data. Ultimately, Grotta said, the only way to know if the modeler’s assumptions are accurate or not is to wait for the future.

A third line of defense is an internal audit or governance process. This involves making the results of these models explainable to front-line business managers. Grotta said he was working with a bank recently that protested its bank managers would not use a model if they didn’t understand what was driving its results. But he said the managers were right to do this. Having a governance process and ensuring information flows up and down the organization is extremely important, Grotta said.

Baking in accountability

Models degrade or “drift” over time, which is part of the reason organizations need to streamline their model development processes. It can take years to craft a new model. “By that time, you might have to go back and rebuild it,” Grotta said. Critical models must be revalidated every year.

To address this challenge, CIOs should think about creating a data science pipeline with an auditable, repeatable and transparent process. This promises to allow organizations to bring the same kind of iterative agility to model development that Agile and DevOps have brought to software development.

Transparent means that upstream and downstream people understand the model drivers. It is repeatable in that someone can repeat the process around creating it. It is auditable in the sense that there is a program in place to think about how to manage the process, take in new information, and get the model through the monitoring process. There are varying levels of this kind of agility today, but Grotta believes it is important for organizations to make it easy to update data science models in order to stay competitive.

How to keep up with model drift

Nick Elprin, CEO and co-founder of Domino Data Lab, a data science platform vendor, agreed that model drift is a problem that must be addressed head on when building a data science development pipeline. In some cases, the drift might be due to changes in the environment, like changing customer preferences or behavior. In other cases, drift could be caused by more adversarial factors. For example, criminals might adopt new strategies for defeating a new fraud detection model.

Nick Elprin, CEO and co-founder, Domino Data LabNick Elprin

In order to keep up with this drift, CIOs need to include a process for monitoring the effectiveness of their data models over time and establishing thresholds for replacing these models when performance degrades.

With traditional software monitoring, the IT service management needs to track metrics related to CPU, network and memory usage. With data science, CIOs need to capture metrics related to accuracy of model results. “Software for [data science] production models needs to look at the output they are getting from those models, and if drift has occurred, that should raise an alarm to retrain it,” Elprin said.

Fashion-forward data science

At Stitch Fix, a personal shopping service, the company’s data science pipeline allows it to sell clothes online at full price. Using data science in various ways allows them to find new ways to add value against deep discount giants like Amazon, said Eric Colson, chief algorithms officer at Stitch Fix.

Eric Colson, chief algorithms officer,  Stitch FixEric Colson

For example, the data science team has used natural language processing to improve its recommendation engines and buy inventory. Stitch Fix also uses genetic algorithms — algorithms that are designed to mimic evolution and iteratively select the best results following a set of randomized changes. These are used to streamline the process for designing clothes, coming up with countless iterations: Fashion designers then vet the designs.

This kind of digital innovation, however, was only possible he said because the company created an efficient data science pipeline. He added that it was also critical that the data science team is considered a top-level department at Stitch Fix and reports directly to the CEO.

Specialists or generalists?

One important consideration for CIOs in constructing the data science development pipeline is whether to recruit data science specialists or generalists. Specialists are good at optimizing one step in a complex data science pipeline. Generalists can execute all the different tasks in a data science pipeline. In the early stages of a data science initiative, generalists can adapt to changes in the workflow more easily, Colson said.

Some of these different tasks include feature engineering, model training, enhance transform and loading (ETL) data, API integration, and application development. It is tempting to staff each of these tasks with specialists to improve individual performance. “This may be true of assembly lines, but with data science, you don’t know what you are building, and you need to iterate,” Colson said. The process of iteration requires fluidity, and if the different roles are staffed with different people, there will be longer wait times when a change is made.

In the beginning at least, companies will benefit more from generalists. But after data science processes are established after a few years, specialists may be more efficient.

Align data science with business

Today a lot of data science models are built in silos that are disconnected from normal business operations, Domino’s Elprin said. To make data science effective, it must be integrated into existing business processes. This comes from aligning data science projects with business initiatives. This might involve things like reducing the cost of fraudulent claims or improving customer engagement.

In less effective organizations, management tends to start with the data the company has collected and wonder what a data science team can do with it. In more effective organizations, data science is driven by business objectives.

“Getting to digital transformation requires top down buy-in to say this is important,” Elprin said. “The most successful organizations find ways to get quick wins to get political capital. Instead of twelve-month projects, quick wins will demonstrate value, and get more concrete engagement.”

New data science platforms aim to be workflow, collaboration hubs

An emerging class of data science platforms that provide collaboration and workflow management capabilities is gaining more attention from both users and vendors — most recently Oracle, which is buying its way into the market.

Oracle’s acquisition of startup DataScience.com puts more major-vendor muscle behind the workbench-style platforms, which give data science teams a collaborative environment for developing, deploying and documenting analytical models. IBM is already in with its Data Science Experience platform, informally known as DSX. Other vendors include Domino Data Lab and Cloudera, which last week detailed plans for a new release of its Cloudera Data Science Workbench (CDSW) software this summer.

These technologies are a subcategory of data science platforms overall. They aren’t analytics tools; they’re hubs that data scientists can use to build predictive and machine learning models in a shared and managed space — instead of doing so on their own laptops, without a central location to coordinate workflows and maintain models. Typically, they’re aimed at teams with 10 to 20 data scientists and up.

The workbenches began appearing in 2014, but it’s only over the past year or so that they matured into products suitable for mainstream users. Even now, the market is still developing. Domino and Cloudera wouldn’t disclose the number of customers they have for their technologies; in a March interview, DataScience.com CEO Ian Swanson said only that its namesake platform has “dozens” of users.

A new way to work with data science volunteers

Ruben van der Dussen, ThornRuben van der Dussen

Thorn, a nonprofit group that fights child sex trafficking and pornography, deployed Domino’s software in early 2017. The San Francisco-based organization only has one full-time data scientist, but it taps volunteers to do analytics work that helps law enforcement agencies identify and find trafficking victims. About 20 outside data scientists are often involved at a time — a number that swells to 100 or so during hackathons that Thorn holds, said Ruben van der Dussen, director of its Innovation Lab.

That makes this sort of data science platform a good fit for the group, he said. Before, the engineers on his team had to create separate computing instances on the Amazon Elastic Compute Cloud (EC2) for volunteers and set them up to log in from their own systems. With Domino, the engineers put Docker containers on Thorn’s EC2 environment, with embedded Jupyter Notebooks that the data scientists access via the web. That lets them start analyzing data faster and frees up time for the engineers to spend on more productive tasks, van der Dussen said.

He added that data security and access privileges are also easier to manage now — an important consideration, given the sensitive nature of the images, ads and other online data that Thorn analyzes with a variety of machine learning and deep learning models, including ones based on natural language processing and computer vision algorithms.

Thorn develops and trains the analytical models within the Domino platform and uses it to maintain different versions of the Jupyter Notebooks, so the work done by data scientists is documented for other volunteers to pick up on. In addition, multiple people working together on a project can collaborate through the platform. The group uses tools like Slack for direct communication, “but Domino makes it really easy to share a Notebook and for people to comment on it,” van der Dussen said.

Screenshot of Domino Data Lab's data science platform
Domino Data Lab’s data science platform lets users run different analytics tools in separate workspaces.

Oracle puts its money down on data science

Oracle is betting that data science platforms like DataScience.com’s will become a popular technology for organizations that want to manage their advanced analytics processes more effectively. Oracle, which announced the acquisition this month, plans to combine DataScience.com’s platform with its own AI infrastructure and model training tools as part of a data science PaaS offering in the Oracle Cloud.

By buying DataScience.com, Oracle hopes to help users get more out of their analytics efforts — and better position itself as a machine learning vendor against rivals like Amazon Web Services, IBM, Google and Microsoft. Oracle said it will continue to invest in DataScience.com’s technology, with a goal of delivering “more functionality and capabilities at a quicker pace.” It didn’t disclose what it’s paying for the Culver City, Calif., startup.

The workbench platforms centralize work on analytics projects and management of the data science workflow. Data scientists can team up on projects and run various commercial and open source analytics tools to which the platforms connect, then deploy finished models for production applications. The platforms also support data security and governance, plus version control on analytical models.

Cloudera said its upcoming CDSW 1.4 release adds features for tracking and comparing different versions of models during the development and training process, as well as the ability to deploy models as REST APIs embedded in containers for easier integration into dashboards and other applications. DataScience.com, Domino and IBM provide similar functionality in their data science platforms.

Screenshot of Cloudera Data Science Workbench
Cloudera Data Science Workbench uses a sessions concept for running analytics applications.

Choices on data science tools and platforms

Deutsche Telekom AG is offering both CDSW and IBM’s DSX to users of Telekom Data Intelligence Hub, a cloud-based big data analytics service that the telecommunications company is testing with a small number of customers in Europe ahead of a planned rollout during the second half of the year.

Users can also access Jupyter, RStudio and three other open source analytics tools, said Sven Löffler, a business development executive at the Bonn, Germany, company who’s leading the implementation of the analytics service. The project team sees benefits in enabling organizations to connect to those tools through the two data science platforms and get “all this sharing and capabilities to work collaboratively with others,” he said.

However, Löffler has heard from some customers that the cost of the platforms could be a barrier compared to working directly with the open source tools as part of the service, which runs in the Microsoft Azure cloud. It’s fed by data pipelines that Deutsche Telekom is building with a new Azure version of Cloudera’s Altus Data Engineering service.

Virtual tools help explore computer science and robotics in the classroom |

I am sure everyone enjoyed Computer Science Education Week and its amazing focus on enabling the students of today to create the world of tomorrow. We live in an amazing time of technological progress. Every aspect of our lives is being shaped by digital transformation. However, with transformation comes disruption. There’s growing concern over job growth, economic opportunity, and the world we are building for the next generation. So, the real question is: How can technology create more opportunity not for a few, but for all?

This week we would love to focus on how to bring applied computer science through robotics into the classroom. The skill of programming is fundamental for structured, logical thinking and enables students to bring technology to life and make it their own. Oftentimes this can be a lofty goal when resources are limited, but there is room for a grounded, everyday approach.

Code Builder for Minecraft: Education Edition is an extension that allows educators and students to explore, create, and play in an immersive Minecraft world – all by writing code. Since they can connect their work to learn-to-code packages like ScratchX, Tynker, and Microsoft MakeCode, players start with familiar tools, templates and tutorials. Minecraft: Education Edition is available free to qualified education institutions with any new Windows 10 device. You can check out our Minecraft: Education Edition sign-up page to learn how you can receive a one-year, single-user subscription for Minecraft: Education Edition for each new Windows 10 device purchased for your K-12 school.

OhBot is an educational robotics system that has been designed to stretch pupils’ computational thinking and understanding of computer science, and explore human/robot interaction through a creative robotic head that students program to speak and interact with their environment.

Another key area that we are supporting is in simulation solutions for robotics, to enable lower-cost access and better design practices in the classroom. With these programs, educators can teach robotic coding without a physical robot.

Daniel Rosenstein, a volunteer Robotics coach at the Elementary, Middle school and High school levels, firmly believes that simulation illustrates the connection between computer science and best practices in engineering design. Simulation makes the design process uniquely personal, because students are encouraged to build digital versions of their physical robot, and to try their programs in the simulator before investing in physical tools. The simulation environment, similar to a video game, creates a digital representation of the robot and its tasks, and allows for very quick learning cycles through design, programming, trial and error.

The Virtual Robotics Toolkit (VRT) is a good example. It’s an advanced simulator designed to enhance the LEGO MINDSTORMS experience. An excellent learning tool for classroom and competitive robotics, the VRT is easy to use and is approved by teachers and students.

Looks set to be another year of great new apps in the Microsoft Store and we are excited to shortly be welcoming Synthesis: An Autodesk Technology to the Store.  This app is built for design simulation and will enable students to work together to design, test and experiment with robotics, without having to touch a piece of physical hardware.

We look forward to connecting with you on this and more soon!

CIOs should lean on AI ‘giants’ for machine learning strategy

NEW YORK — Machine learning and deep learning will be part of every data science organization, according to Edd Wilder-James, former vice president of technology strategy at Silicon Valley Data Science and now an open source strategist at Google’s TensorFlow.

Wilder-James, who spoke at the Strata Data Conference, pointed to recent advancements in image and speech recognition algorithms as examples of why machine learning and deep learning are going mainstream. He believes image and speech recognition software has evolved to the point where it can see and understand some things as well as — and in some use cases better than — humans. That makes it ripe to become part of the internal workings of applications and the driver of new and better services to internal and external customers, he said.

But what investments in AI should CIOs make to provide these capabilities to their companies? When building a machine learning strategy, choice abounds, Wilder-James said.

Machine learning vs. deep learning

Deep learning is a subset of machine learning, but it’s different enough to be discussed separately, according to Wilder-James. Examples of machine learning models include optimization, fraud detection and preventive maintenance. “We use machine learning to identify patterns,” Wilder-James said. “Here’s a pattern. Now, what do we know? What can we do as a result of identifying this pattern? Can we take action?”

Deep learning models perform tasks that more closely resemble human intelligence such as image processing and recognition. “With a massive amount of compute power, we’re able to look at a massively large number of input signals,” Wilder-James said. “And, so what a computer is able to do starts to look like human cognitive abilities.”

Some of the terrain for machine learning will look familiar to CIOs. Statistical programming languages such as SAS, SPSS and Matlab are known territory for IT departments. Open source counterparts such as R, Python and Spark are also machine-learning ready. “Open source is probably a better guarantee of stability and a good choice to make in terms of avoiding lock-in and ensuring you have support,” Wilder-James said.

Unlike other tech rollouts

The rollout of machine learning and deep learning models, however, is a different process than most technology rollouts. After getting a handle on the problem, CIOs will need to investigate if machine learning is even an appropriate solution.

“It may not be true that you can solve it with machine learning,” Wilder-James said. “This is one important difference from other technical rollouts. You don’t know if you’ll be successful or not. You have to enter into this on the pilot, proof-of-concept ladder.”

The most time-consuming step in deploying a machine learning model is feature engineering, or finding features in the data that will help the algorithms self-tune. Deep learning models skip the tedious feature engineering step and go right to the training step. To tune a deep learning model correctly requires immense data sets, graphic processing units or tensor processing units, and time. Wilder-James said it could take weeks and even months to train a deep learning model.

One more thing to note: Building deep learning models is hard and won’t be a part of most companies’ machine learning strategy.

“You have to be aware that a lot of what’s coming out is the closest to research IT has ever been,” he said. “These things are being published in papers and deployed in production in very short cycles.”

CIOs whose companies are not inclined to invest heavily in AI research and development should instead rely on prebuilt, reusable machine and deep learning models rather than reinvent the wheel. Image recognition models, such as Inception, and natural language models, such as SyntaxNet and Parsey McParseface, are examples of models that are ready and available for use.

“You can stand on the shoulders of giants, I guess that’s what I’m trying to say,” Wilder-James said. “It doesn’t have to be from scratch.”

Machine learning tech

The good news for CIOs is that vendors have set the stage to start building a machine learning strategy now. TensorFlow, a machine learning software library, is one of the best known toolkits out there. “It’s got the buzz because it’s an open source project out of Google,” Wilder-James said. “It runs fast and is ubiquitous.”

While not terribly developer-friendly, a simplified interface called Keras eases the burden and can handle the majority of use cases. And TensorFlow isn’t the only deep learning library or framework option, either. Others include MXNet, PyTorch, CNTK, and Deeplearning4j.

For CIOs who want AI to live on premises, technologies such as Nvidia’s DGX-1 box, which retails for $129,000, are available.

But CIOs can also utilize cloud as a computing resource, which would cost anywhere between $5 and $15 an hour, according to Wilder-James. “I worked it out, and the cloud cost is roughly the same as running the physical machine continuously for about a year,” he said.

Or they can choose to go the hosted platform route, where a service provider will run trained models for a company. And other tools, such as domain-specific proprietary tools like the personalization platform from Nara Logics, can fill out the AI infrastructure.

“It’s the same kind of range we have with plenty of other services out there,” he said. “Do you rent an EC2 instance to run a database or do you subscribe to Amazon Redshift? You can pick the level of abstraction that you want for these services.”

Still, before investments in technology and talent are made, a machine learning strategy should start with the basics: “The single best thing you can do to prepare with AI in the future is to develop a competency with your own data, whether it’s getting access to data, integrating data out of silos, providing data results readily to employees,” Wilder-James said. “Understanding how to get at your data is going to be the thing to prepare you best.”

Resuscitating a dream to save lives with data science – Asia News Center

By Sarmila Basu

Back in high school in India, I had no idea that data science could save lives.

I wanted to be a doctor because I wanted to make a difference. And, what better way to make a difference than by saving lives?

That was my thinking until they told me I would have to cut up cadavers to become a doctor. That ended that. I would become a statistician and other people would be the ones who would make the ultimate difference by keeping us all alive.

Or so I thought.

Now I know better. I shouldn’t have overlooked the power of data to solve problems and find solutions.

Thousands of people die every year from a common, but devastating, bacterial infection they get when hospitalized. It’s called Clostridium Difficile 101 (or CDIFF for short). In the United States alone, around 500,000 people are infected every year and, of those, 29,000 die from it. Many more succumb to the disease around the world.

It’s an ugly infection, putting the most vulnerable at risk of dying from dehydration brought on by seemingly endless bouts of diarrhea. People get infected when antibiotics wipe out their good, infection-fighting bacteria. The elderly, young, and those with compromised immune systems are most at risk. But anyone can get it.

So how are data scientists like those on my Data and Decision Sciences team in Microsoft IT able to help?

We’re working with hospitals to predict when a patient is at risk of being infected by CDIFF.  That might sound simple, but knowing the answer to this question can help hospitals take life-saving precautions to help at-risk patients.

When a patient is brought into an Emergency Room, we use artificial intelligence-driven modeling to assess their risk. This assessment scores them on how likely they are to get infected by CDIFF and, if they are likely to get it, how likely it is to lead to death.

It scores them for age, medical history, antibiotics usage, and a long list of other factors. We’re working with two hospitals in the Midwest region of the United States to refine our model. And, using old data, we’re right 85 percent of the time. That number is gradually climbing as we pour more data and insights into the model, allowing it to learn and become more accurate. We’re hoping to get it into the mid- to upper-90s  percent range and then make it broadly available for all hospitals to use.

Assessing an individual person’s risk enables hospitals to adopt procedures for taking care of these high-risk patients, costly precautions that can be reserved for those at the most risk.

But there is a human side too.

CDIFF is spread easily. Say, for instance, when patients are casual with their hygiene, or when hospital personnel do not remember to wear fresh gloves or wash their hands as thoroughly or as regularly as they should. The story changes, however, when you can look at a patient’s chart and see that she or he is at “high risk” of dying if you aren’t as vigilant as possible. It’s human nature for people to do everything they can to protect that person.

And by now you are probably wondering: if data science can help hospitals fend off CDIFF, what else can it do? 

Lots.  

We’re working with other hospitals to find other ways to help them save lives. Some of the things we’re working on are not as exciting. For instance, forecasting when a hospital will run short on beds is dry stuff, but it can be crucially important when it comes to making sure there are enough of them available during a crisis. Also not flashy is predicting which patients are going be re-admitted for the same problem in less than 30 days. Nonetheless, getting ahead of these re-admissions can save hospitals millions of dollars and keep patient costs down.

We’re working hard to find new ways that data can do what I once thought was impossible, to save lives, and as you can imagine, lots of other cool stuff.  

Learn more about the power of data analytics and machine learning by reading about how we’re using data to help save kids who are in danger of dropping out of school and how we are using data to help manage our buildings at Microsoft. If you want to know how you can do these kinds of things at your company, read about how I started my role at Microsoft and how I built my analytics team.

Sarmila Basu is Senior Director, Data and Decision Sciences at Microsoft and is based at the company’s headquarters in Redmond in the U.S. state of Washington. She was born and raised in India’s third largest city, Kolkata (formerly known as Calcutta). She came to the United States 30 years ago as a postgraduate student and completed a Ph.D in Economics. Her parents still live in Kolkata and she visits there once a year. Sarmila is also president of the Seattle-based not-for-profit group, People for Progress in India. It helps grassroots organizations there in areas such as sustainable agriculture and the use of bio-sand filters to make drinking water available in remote villages, particularly in the south and west of the country. It also supports education and training programs for marginalized segments of society. You can read more about its work here. And, you can read more of  Sarmila’s blogs here.