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Everybody wants a bite of the generative AI cake, from software developers to non-technical business leaders. Having all the information and skills required to take generative AI to the next level in your career or organization is all you need. You can now get this information with Amazon's free courses that dive into generative AI, through different aspects, sectors, and also for different job titles.

Generative AI Foundations

Link: Generative AI Foundations

Tune into Amazon's Generative AI Foundations YouTube playlist that goes into a technical deep dive designed for those already familiar with AI modeling. You will learn about the conceptual foundations of generative AI, and be offered practical advice, as well as hands-on guidance to pre-train, fine-tune, and deploy state-of-the-art Foundation Models on AWS and beyond.

Foundation of Prompt Engineering

Link: Foundation of Prompt Engineering

This a standalone course, where you will learn the principles, techniques and best practices for effective prompt engineering. Start with learning the basics and slowly progress to more advanced techniques. You will also learn how to guard against prompt misuse as well as mitigating bias.

This course is aimed at intermediate-level tech professionals and will take 4 hours to complete. To get the most out of this course, it is recommended that you have a good understanding of generative AI, how to deal with it in projects and also Amazon Bedrock.

Generative AI Learning Plan for Developers

Link: Generative AI Learning Plan for Developers

Start your generative AI learning path for software developers with this free Amazon course which goes into large-language models, planning generative AI projects, learning the foundations of prompt engineering, and also getting started with Amazon Bedrock.

Consisting of 5 courses, which will take 11 hours in total. To get the most out of this course, the prerequisites are having AWS Technical Essentials and intermediate-level proficiency in Python.

Building Language Models on AWS

Link: Building Language Models on AWS

Learn how to build large language models on Amazon SageMarker, a platform known to aid data scientists in building, training, deploying and monitoring machine learning models. Data scientists will be put up with the task of building large language models on the platform, learning about the different storage, ingestion and training options to be able to process the large text data required for the model. You will also learn about the challenges found when deploying large language models for generative AI tasks.

This course is aimed at advanced-level tech professionals and will take 5.5 hours to complete.

Generative AI Learning Plan for Decision Makers

Link: Generative AI Learning Plan for Decision Makers

When it comes to being a tech professional or working with data-driven results, making decisions can be one of the most challenging things to do. In this free course provided by Amazon, you will learn about how generative AI is being used in the business aspect and how it is used to make technical decisions. Become one of these decision-makers as you learn how to approach a generative AI project to make an organization generative AI-ready.

This learning path consists of 3 courses, which will take 1 hour each.

Generative AI for Executives

Link: Generative AI for Executives

Although there is a lot of hype around generative AI at the moment, you may still not understand its true capabilities and how it can be implemented to improve your organization. In this course, you will get a high-level picture of generative AI, how it can address executives’ concerns and challenges, and how it supports business growth. This will be backed by various use cases and also a video on how you can train your workforce to use generative AI.

Wrapping it up

Regardless of where you stand in your organisation, you must understand where the technology landscape currently is and what we should expect in the future. These courses can help you implement new strategies for your organisation or upskill you as a software developer.

Everybody should have free access to learn about today's world.

Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.

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A report commissioned by the Semiconductor Industry Association (SIA) and the India Electronics and Semiconductor Association (IESA) recommends advanced US-India cooperation on semiconductors through a partnership under the CHIPS for America International Technology Security and Innovation Fund (ITSI) to strengthen India’s semiconductor competitiveness.

The report, titled “Assessing India’s Readiness to Assume a Greater Role in Global Semiconductor Value Chains,” evaluates India’s existing semiconductor ecosystem and policy frameworks and offers recommendations to facilitate longer-term strategic development of complementary semiconductor ecosystems in the U.S. and India.

The report, which was authored by the Information Technology and Innovation Foundation (ITIF), also recommends creating a pilot visa programme to facilitate the circulation of skilled workers between the US and India, as a potential deliverable of the initiative on Critical and Emerging Technology (iCET).

It further suggests advanced policy reforms to lower the cost of doing business for semiconductor companies in India, including offering tax breaks to chip companies, reducing customs administration burdens, and expediting clearance times for goods entering the country.

Other suggestions include establishing cross-sector partnerships with higher-education institutions to grow India’s semiconductor-ready workforce and facilitating robust and ongoing consultation with semiconductor industry stakeholders.

“Given its rapidly expanding domestic market, a well-developed design ecosystem, supportive government policies, and concerted industry collaboration to tap global markets, the present juncture presents a unique opportunity for the establishment of semiconductor manufacturing in India. The report underscores India’s tremendous progress towards becoming a prime destination for electronics and semiconductor manufacturing, capitalising on its robust semiconductor design ecosystem,” said Ashok Chandak, president of IESA.

The post Industry Bodies Recommend Advanced US-India Cooperation To Strengthen Semiconductor Competitiveness appeared first on Analytics India Magazine.

At Nasscom’s technology and leadership summit 2024, BharatGPT (an IIT Bombay initiative) in collaboration with its GPU buddy Vizzhy, introduced a new generative AI called “Hanooman,” driven by the virtue of leveraging technological power for the greater public good rather than personal gain.

Collaborating with Indian IIT Bombay, the Hanooman project is poised to democratise AI for India. One of its primary objectives is to make AI accessible to non-English speakers.

The first four open-source models in the series (1.5 billion, 7 billion, 13 billion, and 40 billion parameters) are set to be released next month. Being multimodal, the models will be able to converse in 11 languages including Hindi, Tamil, Telugu, Malayalam, and Marathi. The initiative aims to extend language support to all 22 official languages of India in future.

Vishnu Vardhan, chief of Vizzhy, threw light on the stark economic contrast between Boston and Mumbai, emphasising the critical role of a science-driven ecosystem in fostering economic prosperity. The initiative has evolved to include collaboration with seven IITs to create a sustainable and innovation-centric AI ecosystem in India.

By expanding the reach of AI in indic languages and fostering inclusivity, the project aims to contribute significantly to the country’s technological advancement and economic development.

“We have to depend on English for a lot of things,” explained Vardhan in an earlier conversation with AIM, that when it comes to science and healthcare, we are bound to look at it from an English lens, which becomes a huge barrier for research in India as 80% of the people are not proficient in the language. “People look at English just as another language, but they think in their native language.”

Echoing similar thoughts, Ganesh Ramakrishnan, an IIT Bombay professor who is leading the BharatGPT initiative said that there is definitely a need for a foundational model for Indic languages. “Mistral got France on the AI map. We want India to get on the AI map with BharatGPT,” he told AIM.

The overarching goal is to empower individuals and communities, transcending linguistic barriers and bridging the gap in AI usage.

The post BharatGPT Unveils Hanooman, a NewSuite of Indic Generative AI Models appeared first on Analytics India Magazine.

While fine-tuning with Indic language tokens on top of existing English models is a viable approach, building foundational models from scratch offers several advantages, and that is what BharatGPT is aiming to do.

“Existing models may not adequately represent the Indian cultural and linguistic diversity, which can lead to biases and limitations in their applicability,” said Professor Maunendra Sankar Desarkar from IIT Hyderabad, who is also a core team member of the BharatGPT initiative.

“Moreover, fine-tuning may not fully address the unique linguistic challenges posed by Indic languages,” he added. He further said that by building foundational models tailored to the Indian context, we can ensure greater inclusivity and effectiveness across diverse linguistic communities, which would deliver AI in the best possible way in India.

“We’re sourcing data from various repositories available on the web, including digitised books and datasets,” Desarkar added. He said that the OCR technology plays a crucial role in digitising textual content, though it’s not always error-free. “We’re exploring methods to detect and correct OCR errors algorithmically,” he added, highlighting the collaboration with organisations like BHASHINI.

Indic data is gold mine for global research

“We observed that many communities, including the Indian diaspora, produce content that differs from the polished English typically encountered,” explained Desarkar. This realisation prompted the researchers to explore how NLP techniques could be tailored to better serve diverse linguistic communities. “Consequently, our focus expanded to include domains such as healthcare management and travel planning, where NLP could offer valuable solutions.”

“Beyond the sheer volume of data required, there is also significant heterogeneity in data distribution, particularly in multilingual settings within India,” Desarkar added. This necessitates different algorithmic treatments and techniques to effectively handle and process diverse datasets, ensuring that models are robust and adaptable across different linguistic contexts, not just one.

Moreover, the identification of language similarities can form the development of intelligent techniques to improve model performance, particularly in languages with limited training data. By leveraging insights from languages like Hindi, which may share similarities with other languages, researchers can develop strategies to enhance performance in related linguistic domains that do not have enough available data, such as Bhojpuri, Desarkar explained.

“One of the things that we are very focused on is developing models that reach a vast audience, which includes a small hospital in a village without the available resources to run OpenAI’s model, for example,” said Desarkar. In this regard, he said that the BharatGPT initiative is also focusing on the development of smaller, more efficient modules that deliver comparable performance without requiring extensive infrastructure.

Desarkar said due to all these reasons, Indic data trained models would benefit the whole world due to the hidden knowledge and the depth that models would achieve with their algorithms.

Tailored solutions for India

A month ago, Desarkar’s paper titled ‘CharSpan: Utilising Lexical Similarity to Enable Zero-Shot Machine Translation for Extremely Low-resource Languages‘ was accepted in the EACL 2024 main conference, which dealt with the low resource models. Another paper titled ‘Unsupervised Noise Injection to Enable Zero-Shot Machine Translation for Extremely Low-resource Languages‘ was also selected for EMNLP 2023.

“While our long-term plans involve diverse sources, our immediate focus is on healthcare data obtained through partnerships with relevant organisations,” added Desarkar. “This data isn’t randomly sourced from the web but is generated through specific channels, ensuring its relevance and reliability,” he added, saying that it helps in giving unique and essential solutions for specific needs.

Desarkar said that while Indic models are on the rise, there is also a need for building a good metric and benchmark for these models. “We’re actively working on developing metrics tailored to Indic languages, which will enable more accurate evaluation of model performance,” he added, saying that fostering dialogue among researchers to establish a consensus on evaluation metrics is crucial for advancing the field.

Working in this field for more than a decade, and completing his PhD from IIT Kharagpur, Desarkar’s focus was on problems such as ranking, search, recommendation systems, and so forth. Over time, with the exponential increase in data available online, particularly NLP, became more apparent.

Even in areas like e-commerce and social media, where communication was initially concise, “we began to see longer-form content”, he said about the reason for interest in the field.

Talking about BharatGPT, Desarkar said that the computational demands are substantial. “While we’ve secured commitments from certain quarters, we may need to leverage cloud services to address this challenge,” he added. However, beyond hardware infrastructure, data availability is also critical.

“While we may have sufficient data for these languages, expanding beyond that presents challenges,” he added. Consequently, the BharatGPT team is exploring algorithmic solutions to make the most of limited data resources. “While progress is being made in setting up the necessary infrastructure, we’re also focusing on addressing algorithmic and data-related challenges, and this would be beneficial for the whole world,” concluded Desarkar.

The post IIT Hyderabad Professor Believes Indic Data is All You Need appeared first on Analytics India Magazine.

HuggingFace has Cosmopedia v0.1, the largest open synthetic dataset consisting of over 30 million samples, generated by Mixtral 7b. It consists of various types of content such as textbooks, blog posts, stories, and WikiHow articles, contributing to a total of 25 billion tokens.

The dataset aims to compile global knowledge by mapping information from web datasets like RefinedWeb and RedPajama. It features essential information, including prompts, synthetic content, seed data sources, token lengths, text formats (e.g., textbook, blog post), and target audiences. The comprehensive breakdown of splits, distributions, and creation methodology is presented, offering researchers insights into the dataset’s structure and potential applications.

Inspired by Phi1.5’s work, this initial version of Cosmopedia provides a foundation for research in the synthetic data domain. It serves as a comprehensive resource for diverse topics, emphasizing its potential for further enhancement in subsequent iterations.

The dataset is structured into eight splits, each derived from distinct seed samples. These splits include web_samples_v1 and web_samples_v2, constituting approximately 75% of the dataset, sourced from an internal web dataset akin to RefinedWeb.

The Stanford split utilizes scraped course outlines from stanford.edu, while the stories split incorporates generated narratives from UltraChat and OpenHermes2.5. Additionally, WikiHow, OpenStax, KhanAcademy, and automathtext splits involve prompts related to their respective sources.

To facilitate dataset access, users can employ the provided code snippet to load specific splits. A smaller subset, Cosmopedia-100k, is also available for those seeking a reduced dataset. Furthermore, a larger model, Cosmo-1B, has been trained on Cosmopedia, demonstrating scalability and versatility.

The dataset creation process involves a topic clustering method for web samples, refining prompts iteratively, and addressing contamination issues. The objective is to maximize diversity by tailoring prompt styles and audiences, significantly reducing duplicate content.

The post HuggingFace Introduces Cosmopedia, the Largest Open Synthetic Dataset appeared first on Analytics India Magazine.

Kochi-based Nimo Planet created quite a buzz at CES 2024 with its spatial computer which features Nimo 1 OS, Nimo 1 Glasses, and Nimo 1 Core. The company aspires to create a pocket-sized portable computer dedicated to boosting employee productivity.

“Apple’s Vision Pro can provide an immersive experience to users for one or two hours inside the home, but it is not designed for the workspace where you can carry it in your pocket for multiple hours at work. That’s where Nimo Planet steps in,” said Rohildev Nattukallingal, Nimo Planet’s chief in an exclusive interaction with AIM.

Lately, there have been reports about customers returning their Apple Vision Pros under the 14-day return policy, citing discomfort and the poor price-to-features ratio.

Nimo Work

“Some customers are adopting a hybrid workspace, working a few days from home and a few days from the office. Such people prefer to use Nimo,” said Nattukallingal, adding that the initial category of customers comprised traveling professionals.

The company has shipped its products to companies like KPMG Consulting and a few other consulting firms. Additionally, it has some enterprise use cases in companies from the Middle East, US, and Europe, who want to use it to better their employee experience. As of today, the company has delivered Nimo OS to 15 enterprises.

Comparing Nimo Planet with Quest 3, Nattukallingal said, “With their VR headsets, Meta is focusing more on the metaverse and games. The way we see it, our focus is on increasing work productivity for enterprises and we don’t concentrate on general use cases.” He added that even with AI integration, the company aims to find ways to build operating systems and applications specifically for employees.

In the near future, the company aims to replace traditional input devices like keyboard and mouse. However, Nattukallingal believes that hand gestures are not a suitable alternative for keyboards, as typing in the air proves to be challenging.

“Right now, the keyboard is the best option. But in the future, I see voice plus AI that does some automation as a potent option. However, voice does not work in some scenarios, such as when I’m sitting in a flight,” said Dev, adding that he expects advancements like Neuralink, allowing mouse control with thoughts, to emerge.

Nattukallingal anticipates a future without laptops. “I would say, in the next five to eight years, let’s imagine people start forgetting to take laptops because if they have something like this, they can just carry it in their hands or pockets.”

What Nimo OS Offers

Nimo OS allows users to run multiple applications simultaneously, each displayed on a separate virtual screen in the 3D space. This makes it easy to switch between tasks and compare information side-by-side. It is built on the foundation of Android Open Source Project (AOSP) and Linux .

“We built a custom rendering system and have not used any wrapper products in the market. This means I can run stereo 3D videos, create apps, and convert any existing app into stereoscopic,” he said.

“One of the biggest advantages of this architecture is that I can run multiple Android, Unity, and web apps simultaneously,” he added, saying that the only other company to have adopted this approach is Apple.

However, Nimo Glasses currently lack a camera for video-calling. Nattukallingal said that they are planning to introduce a new case equipped with a battery and a camera that can be connected for video calling. Nimo OS also supports Mac and Windows applications through USB-C or remote desktop. It has been granted a US utility patent for Nimo OS Spatial Workspace and Multi-Window Architecture.

Nimo’s Roadmap

“The immediate goal is to ship 3,000 to 5,000 units of Nimo OS to early customers in the next six to eight months. We are fundraising to help us with mass production,” said Nattukallingal, saying that it plans to partner with enterprises and companies manufacturing AR glasses.

“Plus, we are starting a developer programme in which people can start developing custom applications,” he added. The company recently partnered with Rokid Global, a US-based AR glass manufacturer. Nattukallingal said that there are plans currently in the pipeline to partner with three more companies, and further announcements will be made soon.

The post Nimo Planet’s Spatial Computer Brings in a Future without Laptops appeared first on Analytics India Magazine.

Illinois-based 273 Ventures-owned Kelvin Legal Data OS has released KL3M, a family of LLMs designed for legal purposes. It is trained from scratch on legally permissible data for enterprise applications. KL3M1 is the largest legal model trained on over two trillion tokens of proprietary clean data, focusing on legal, financial, and general domains.

This dataset, the Kelvin Legal DataPack, is commercially available and scored and filtered for continuous improvement using a custom pipeline.

KL3M targets enterprise use in legal, regulatory, and financial workflows. Based on the performance metrics, including perplexity, toxicity, and human preference, KL3M demonstrates superiority over peer models. The initial models, kl3m-170m and kl3m-1.7b, outperform peers in perplexity and toxicity, with applications for tasks such as answering regulatory questions, drafting contracts, and extracting structured information, surpassing other models in detail and stylistic realism. Larger Mixture-of-Experts (MoE) models are also in development for release in the late first quarter.

The models’ training data is ethically collected, avoiding fair use interpretations or contract breaches. The release aligns with the success of small language models (SLMs), prompting an accelerated roadmap. The models are designed to run efficiently on consumer-grade hardware like a MacBook Air or a $300 NVIDIA GPU.

KL3M’s availability is tied to the Kelvin Legal Data OS, and interested parties can sign up for notifications. The creators seek collaboration for fine-tuning KL3M for other domains and testing it against existing SLM workflows. KL3M’s training involves a high-quality, curated English subset of the Kelvin Legal DataPack, and the models are based on the GPT-3 architecture with specific modifications.

The post 273 Ventures Introduces KL3M, a Family of Legal LLMs for Enterprises appeared first on Analytics India Magazine.

On the same day when NVIDIA surpassed Elon Musk’s AI company Tesla as the most traded on the market, its stocks faced the worst day since October 2023, that too, a day before the highly anticipated fourth-quarter results.

The American chip maker started this year with a 47% year-to-date gain, and the stock momentum came with gains in 2023 as well. But the tides have shifted, and the company lost $78 billion in market value.

On Tuesday morning, its stock price was $741 per share, with a market cap of $1.83 trillion (ahead of Amazon’s $1.75 trillion and Alphabet’s $1.78 trillion). Then Nvidia shares dipped as much as 8.6%, sending the market cap as low as $1.67 trillion before the company’s stock began to recover. Now, Alphabet and Amazon are once again ahead of of the chip maker.

CNBC’s Cramer reported, “Open your eyes, people,” Cramer said. “If you think that Nvidia’s quarter is one and done, you’re also thinking that AI is one and done.” The long-term supporter of the company is not wrong since Wall Street continues to bet big on AI, from which NVIDIA emerged as a winner.

All eyes are on the AI company with the rising demand for chips in the market and the upcoming GTC conference in San Jose, CA. The upcoming event in March is expected to bring updates on Blackwell, NVIDIA’s next-gen architecture. The company has already confirmed that their roadmap for 2024-25 features Blackwell, namely the B100 and GB200, which have been listed.

The teaser video gave a glimpse of generative AI’s features like the WPP/NVIDIA engine for digital advertising, the newly introduced Chat with RTX, an industrial metaverse powered by SyncTwin, AI art by Refik Anadol Studio, and OpenAI creating code for Blender animations.

Wall Street analysts are currently focused on the company’s demand outlook for its AI-enabled H100 GPU chips, which can sell for upwards of $40,000.

The post NVIDIA’s Stock Momentum Swings, Google & Amazon Take Lead Again appeared first on Analytics India Magazine.

Fujitsu, founded in 1935 and shaped by the aftermath of the 1923 Great Kanto Earthquake, has emerged as a global technology leader catering to services encompassing IT consulting, system integration, cloud computing, cybersecurity, and digital transformation solutions. The company also provides hardware products, including servers, storage, and client computing devices.

Over the years, Fujitsu has played a pivotal role in the evolution of computing. From pioneering successive FACOM computers in the 1950s to embracing international standardisation in the 1970s, the company has consistently expanded its global influence.

“Our current focus lies in key areas of computing, network, AI, data & security, and convergent technologies, with significant developments like Computing as a Service (CaaS), the Kozuchi AI Platform, and the 1FINITY Ultra Optical System,” Amit Kumar Shrivastava, Head of AI, Fujitsu Fellow & Distinguished Engineer, told AIM.

Last April, the company launched Fujitsu Kozuchi – Fujitsu’s AI platform to deliver access to a range of powerful AI and ML technologies. The platform includes tools like Fujitsu AutoML for automated machine learning model generation, Fujitsu AI Ethics for Fairness to test AI model fairness, and Fujitsu Wide Learning for simulating scientific discovery processes.

The aim is to accelerate the testing and deployment of AI solutions for specific business challenges, offering best-of-breed tools, open-source software access, and collaboration with partner companies.

In generative AI, the company has developed solutions spanning various sectors, from predicting protein structural changes in drug discovery to streamlining operations in finance and enhancing customer engagement in retail.

A collaboration with Tokyo Tech, Tohoku University, and RIKEN focuses on developing distributed training for LLMs to bolster Japan’s AI research capabilities. Additionally, Fujitsu has made efforts to tackle the issue of hallucination in conversational AI by launching technologies that protect systems from adversarial attacks. This ensures ongoing advancements in AI technology while prioritising safety and reliability.

Headquartered in Tokyo, the company’s tech strength is exemplified by the development of groundbreaking supercomputers, including K in 2011 and the remarkable Fugaku in 2020. Beyond technology, Fujitsu places a strong emphasis on sustainability and social responsibility, deeply rooted in our enduring values and Japanese heritage principles of Kaizen and Ikigai.

Inside Fujitsu’s Data Science Lab

The company is dedicated to leveraging AI technology for the creation of secure, diverse, and universally accessible solutions. With a global deployment exceeding 6,000 AI solutions, Fujitsu aims to integrate AI into daily life, with a team comprising experts from diverse backgrounds.

“We work closely with clients to turn AI proofs of concept into practical business applications, as demonstrated by our Kozuchi AI platform,” said Shrivastava, stating that the platform incorporates AI innovation Components and Core Engines, facilitating swift validation and implementation across diverse business scenarios.

Fujitsu’s contributions extend to cancer research acceleration, particularly in combating drug resistance, resulting in a significant reduction in timeframes. “Our Fujitsu AutoML and fairness technology exemplify our dedication to democratising AI. This technology is open-source and hosted on the Linux Foundation,” he added.

Emphasising transparency in AI, Fujitsu provides solutions like WideLearning and Deep Tensor, supporting explainable AI. As a founding member of AI4People, dedicated to AI ethics, the company aims to develop ethical AI, eliminating biases. To ensure safe and secure AI deployment, Fujitsu has established an AI Ethics and Governance Office.

In the space of diversity, the company developed tools such as LiveTalk for multilingual speech recognition and AI-equipped translation engines.

“As generative AI is a hot topic, let me tell you, we have multiple solutions addressing various sectors in the field. We developed technology to predict protein structural changes in drug discovery to reduce development time and costs. In finance, our AI streamlines development and system maintenance operations. Our AI modules generate avatars and customised content based on consumer behaviour data in retail, enhancing customer and business engagement,” said Shrivastava.

Interview Process

At Fujitsu India, the interview process for AI roles is methodically structured to identify and bring on board the most qualified candidates.

The process kicks off with their specialised recruitment team, well-versed in sourcing talent for niche areas like AI. They meticulously comprehend the project teams’ requirements, reaching out to candidates whose profiles align precisely with the projects’ specific scope and needs.

This initial screening is pivotal in considering only the most suitable candidates. Once shortlisted, candidates undergo a further evaluation by the project team, constituting a two-tier filtering process to ensure quality and relevance. Successful candidates at this stage become potential fits for their AI team.

Those who navigate the initial stages are invited for an interview, commencing with a challenging yet accessible 15-minute live programming assignment to assess practical problem-solving skills. Subsequently, candidates face a 45-minute to an hour technical interview featuring scenario-based questions probing their practical experience and understanding of AI.

The complexity of this round varies based on the role, incorporating diverse skills and multiple panellists for comprehensive evaluation.

“In the next round, a project manager, or sometimes I, take the interview for senior data science roles that we are hiring for, then assess the candidates who stand out in the technical interview. This stage of the interview process expands to include a broader range of skills, assessing the candidate’s technical knowledge in AI, business understanding, and interpersonal abilities,” said Shrivastava.

A holistic approach is taken to select technically proficient candidates who align with our organisational culture. Proficiency in an additional language, like Japanese, is considered an advantage.

Successful candidates progress to the final phase of HR discussions tailored to align the candidate’s career aspirations with Fujitsu India’s goals and values. This stage facilitates mutual understanding, ensuring a decision that benefits both parties.

Work Culture

In fostering a workplace that champions sustainability, innovation, and core values, Fujitsu’s work culture revolves around three pillars: aspiration, trust, and empathy.

The focus is on purpose-driven management, encouraging employees to align their personal goals with the broader mission of the organisation. Continuous learning is pivotal, transforming the team into dynamic digital transformation (DX) experts adept in digital technology, adaptive mindsets, and cultural agility.

“Our work-life shift initiative, consisting of smart working, borderless office, and culture change, is designed to boost autonomy, well-being, and work-life balance,” said Shrivastava. The company prioritises diversity, equity, and inclusion to create an environment where uniqueness is celebrated, and everyone is empowered to contribute fully for a truly inclusive culture.

Central to the company’s principles is environmental responsibility, guided by the Global Responsible Business framework. Addressing human rights and well-being issues, the commitment extends beyond the organisation, aiming for a significant social impact, long-term contributions to society, and ample opportunities for employee growth and development.

If you think you are fit for Fujitsu, check out its career page for open positions.

The post Data Science Hiring Process at Fujitsu appeared first on Analytics India Magazine.