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6 years of experience in building and managing AI Systems.
* I develop end-to-end AI systems from requirements analysis, and data gathering to deployment, implementing new methods/research papers, and turning projects into research outcomes. I have achieved substantial performance in DL/ML/RL models for CV and NLP domain problems.
* Implementing and deploying projects handling and maintaining scalability, research papers as well as POCs.
* Project planning, requirements gathering, and analysing requirements to define the system's architecture, and implementation timeline.
* Provide mentoring to junior developers for ML projects.
Website: [roshantanisha.github.io/?ref=codementor](http://roshantanisha.github.io/?ref=codementor)
Summary:
I like to work on technology that is smart, simple and sophisticated. This sums up the vast knowledge required to work on projects to excel in a working product. I like to train Deep Neural Networks and understand them well.
I have mentored many students for their AI careers, teaching them Machine Learning and Mathematics. I am a mentor for the RFS (Reach for the Stars) Programme by the Aga Khan Education Board for India. I am an alumnus of this program as well.
I have a cumulative experience of 6 years working in the product and service-based industry for creating Machine Learning projects.
I have done some innovative work that I am proud of and am continuing to do so. I try my best to contribute my expertise to the project I am working on.
Highly Experienced in Machine Learning, Deep Learning, Advanced Deep Learning, Artificial Intelligence, and Algorithms, including models in the production environment, and deploying ML models. Working with top Indian colleges like BITS, niche NLP and CV, real-estate startups, MNCs, and Fortune top 20 companies, working with sensitive anonymized datasets, and creating state-of-the-art models are some of my achievements. I have strong and correct knowledge of Deep Learning concepts from the above experiences.
Hello everyone!
I'm Mateus, a full-stack **Data Scientist** from Brazil with a background in **Digital Signal Processing**. I graduated from **Brown University** in 2018 with a Bachelor of Science in **Electrical Engineering** and have been working in the Data domain ever since. I have experience with the full scope of the Data Science process, from building data pipelines and developing models to evaluating A/B tests for Data Products.
I'm open to any mentorship opportunities related to data, especially when it comes to **Machine Learning**, **Natural Language Processing**, **Python**, and **Elasticsearch**.
I am currently a Data Scientist at Microsoft and have previously worked at the largest investment bank in Latin America and at Telefonica.
Throughout my career, I developed skills and projects in various segments, including Customer Segmentation, Next Best Offer models, and detection of rare events.
I have over 4 years of experience in programming with Python, Machine Learning (especially when applied to CRM and Product Analytics), and Natural Language Processing. I also have solid experience implementing data orchestration and Elasticsearch-based analytics.
Finally, I have been a mentor for both for-profit and non-profit organizations since I was 16 years old. I'm extensively trained in mentorship and tutoring and have mentored all kinds of people in many topics, from essay writing to machine learning.
Hello, if you are having trouble figuring out what to do with your new startup in AWS, Im here to help guide you throughout the setup and architectural design.
I have been working for multiple startups and have been using AWS for the past 5 years. I am mainly a backend application developer with experiences mainly in writing microservices with SpringBoot integrated with various data stores, ranging from relational databases (Postgres, MySQL, MSSQL) to NoSQL (Elasticsearch, MongoDB, Redis) as well as third-party REST APIs.
Aside from that, I am continuously expanding my knowledge by doing frontend work on the side (React, iOS, Android) as well as training myself for machine learning projects using Tensorflow on AWS SageMaker.
**ABOUT ME**
As a seasoned AI/ML engineer with over 9 years of dedicated experience, I specialize in the entire spectrum of AI SaaS product development. My recent focus lies in the advancement of Generative AI and Large Language Models, leveraging from vector databases to optimize content accuracy for superior user engagement.
Over the course of my career, I have engaged with a diverse array of companies, navigating complex challenges and delivering tangible results through the completion of 80֡ innovative projects. My portfolio encompasses a breadth of industries including Retail, Media, Sports, Security, and Health Care, showcasing a consistent ability to translate visionary concepts into impactful, market leading solutions.
**ACHIEVEMENTS**
* Ranked in the Top 1% and handpicked to be featured at Upwork's exclusive enterprise event.
* Played a part in the journey to industry acclaim, earning trust from top brands including P&G, Nvidia, and AT&S. Featured at Adobe, HubSpot, and beyond!
* Achieved top honors with both "Best Student" and "Best Final Year Project" awards from the University of Bradford, UK, for excelling in my degree and capstone project.
**EXPERTISE:**
* **Scripting & Languages:** Python, C, C++, Matlab, R, Bash
* **Tools:** OpenCV, OpenVino, Numpy, Pandas, Spacy, Gensim, Transformers, NLTK, CoreNLP, Other
* **Deep Learning frameworks:** Torch / PyTorch, Tensorflow, CUDA/CuDNN GPU Accelerations
* **LLMs Platforms & Frameworks:** LangChain, OpenAI, Anthropic, Cohere and more
* **Vector Databases:** Pinecone, Chroma, Milvus, Superbase, PgVector
* **Restful APIs:** with Flask, Fast, Nginx, Gunicorn, Locust
* **Cloud Platforms:** Google Cloud (Compute, GCS), Paperspace, AWS (EC2, S3), Azure
* **Deploy models at scale:** optimize your cloud cost with Replicate, Salad, Modal.com
* **Hardware:** CPU, GPU/Multi-GPU, TPU, Raspberry PI 4, Coral Dev Board, Nvidia Jetsen TX2 & others
* **Edge Accelerators:** Coral Accelerator, Intel Movidius Neural Stick
* **OS:** Linux, Windows, MacOS; Containers: Kubernetes, Docker
**CERTIFICATIONS**
* Generative AI with Large Language Models - DeepLearning.AI & AWS
* Machine Learning – Stanford University
* Data Science – R Programming
I'm a Passionate Machine Learning engineer where I'm always eager to pick challenges and sort out an issue within a confined time. Would love to lend a hand and collaborate with people in solving problems. I have working experience in MNCs for around 5 years. My expertise lies in Python, Machine Learning, Data science,C#,Angular,Devops ,AWS and AWS Sagemaker
See the power of our AWS SageMaker tutors through glowing user reviews that showcase their successful AWS SageMaker learning journeys. Don't miss out on top-notch AWS SageMaker training.
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Olajide Olanrewaju
AWS SageMaker tutor
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Olamide Soyoye
AWS SageMaker tutor
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Opeoluwa Lanre
AWS SageMaker tutor
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Frequently asked questions
How to learn AWS SageMaker?
Learning AWS SageMaker effectively takes a structured approach, whether you're starting as a beginner or aiming to improve your existing skills. Here are key steps to guide you through the learning process:
Understand the basics: Start with the fundamentals of AWS SageMaker. You can find free courses and tutorials online that cater specifically to beginners. These resources make it easy for you to grasp the core concepts and basic syntax of AWS SageMaker, laying a solid foundation for further growth.
Practice regularly: Hands-on practice is crucial. Work on small projects or coding exercises that challenge you to apply what you've learned. This practical experience strengthens your knowledge and builds your coding skills.
Seek expert guidance: Connect with experienced AWS SageMaker tutors on Codementor for one-on-one mentorship. Our mentors offer personalized support, helping you troubleshoot problems, review your code, and navigate more complex topics as your skills develop.
Join online communities: Engage with other learners and professionals in AWS SageMaker through forums and online communities. This engagement offers support, new learning resources, and insights into industry practices.
Build real-world projects: Apply your AWS SageMaker skills to real-world projects. This could be anything from developing a simple app to contributing to open source projects. Using AWS SageMaker in practical applications not only boosts your learning but also builds your portfolio, which is crucial for career advancement.
Stay updated: Since AWS SageMaker is continually evolving, staying informed about the latest developments and advanced features is essential. Follow relevant blogs, subscribe to newsletters, and participate in workshops to keep your skills up-to-date and relevant.
How long does it take to learn AWS SageMaker?
The time it takes to learn AWS SageMaker depends greatly on several factors, including your prior experience, the complexity of the language or tech stack, and how much time you dedicate to learning. Here’s a general framework to help you set realistic expectations:
Beginner level: If you are starting from scratch, getting comfortable with the basics of AWS SageMaker typically takes about 3 to 6 months. During this period, you'll learn the fundamental concepts and begin applying them in simple projects.
Intermediate level: Advancing to an intermediate level can take an additional 6 to 12 months. At this stage, you should be working on more complex projects and deepening your understanding of AWS SageMaker’s more advanced features and best practices.
Advanced level: Achieving proficiency or an advanced level of skill in AWS SageMaker generally requires at least 2 years of consistent practice and learning. This includes mastering sophisticated aspects of AWS SageMaker, contributing to major projects, and possibly specializing in specific areas within AWS SageMaker.
Continuous learning: Technology evolves rapidly, and ongoing learning is essential to maintain and improve your skills in AWS SageMaker. Engaging with new developments, tools, and methodologies in AWS SageMaker is a continuous process throughout your career.
Setting personal learning goals and maintaining a regular learning schedule are crucial. Consider leveraging resources like Codementor to access personalized mentorship and expert guidance, which can accelerate your learning process and help you tackle specific challenges more efficiently.
How much does it cost to find a AWS SageMaker tutor on Codementor?
The cost of finding a AWS SageMaker tutor on Codementor depends on several factors, including the tutor's experience level, the complexity of the topic, and the length of the mentoring session. Here is a breakdown to help you understand the pricing structure:
Tutor experience: Tutors with extensive experience or high demand skills in AWS SageMaker typically charge higher rates. Conversely, emerging professionals might offer more affordable pricing.
Pro plans: Codementor also offers subscription plans that provide full access to all mentors and include features like automated mentor matching, which can be a cost-effective option for regular, ongoing support.
Project-based pricing: If you have a specific project, mentors may offer a flat rate for the complete task instead of an hourly charge. This range can vary widely depending on the project's scope and complexity.
To find the best rate, browse through our AWS SageMaker tutors’ profiles on Codementor, where you can view their rates and read reviews from other learners. This will help you choose a tutor who fits your budget and learning needs.
What are the benefits of learning AWS SageMaker with a dedicated tutor?
Learning AWS SageMaker with a dedicated tutor from Codementor offers several significant benefits that can accelerate your understanding and proficiency:
Personalized learning: A dedicated tutor adapts the learning experience to your specific needs, skills, and goals. This personalization ensures that you are not just learning AWS SageMaker, but exceling in a way that directly aligns with your objectives.
Immediate feedback and assistance: Unlike self-paced online courses, a dedicated tutor provides instant feedback on your code, concepts, and practices. This immediate response helps eliminate misunderstandings and sharpens your skills in real-time, making the learning process more efficient.
Motivation and accountability: Regular sessions with a tutor keep you motivated and accountable. Learning AWS SageMaker can be challenging, and having a dedicated mentor ensures you stay on track and continue making progress towards your learning goals.
Access to expert insights: Dedicated tutors often bring years of experience and industry knowledge. They can provide insights into best practices, current trends, and professional advice that are invaluable for both learning and career development.
Career guidance: Tutors can also offer guidance on how to apply AWS SageMaker in professional settings, assist in building a relevant portfolio, and advise on career opportunities, which is particularly beneficial if you plan to transition into a new role or industry.
By leveraging these benefits, you can significantly improve your competency in AWS SageMaker in a structured, supportive, and effective environment.
How does personalized AWS SageMaker mentoring differ from traditional classroom learning?
Personalized AWS SageMaker mentoring through Codementor offers a unique and effective learning approach compared to traditional classroom learning, particularly in these key aspects:
Customized content: Personalized mentoring adapts the learning material and pace specifically to your needs and skill level. This means the sessions can focus on areas where you need the most help or interest, unlike classroom settings which follow a fixed curriculum for all students.
One-on-one attention: With personalized mentoring, you receive the undivided attention of the tutor. This allows for immediate feedback and detailed explanations, ensuring that no questions are left unanswered, and concepts are fully understood.
Flexible scheduling: Personalized mentoring is arranged around your schedule, providing the flexibility to learn at times that are most convenient for you. This is often not possible in traditional classroom settings, which operate on a fixed schedule.
Pace of learning: In personalized mentoring, the pace can be adjusted according to how quickly or slowly you grasp new concepts. This custom pacing can significantly enhance the learning experience, as opposed to a classroom environment where the pace is set and may not align with every student’s learning speed.
Practical, hands-on learning: Mentors can provide more practical, hands-on learning experiences tailored to real-world applications. This direct application of skills is often more limited in classroom settings due to the general nature of the curriculum and the number of students involved.
Personalized mentoring thus provides a more tailored, flexible, and intensive learning experience, making it ideal for those who seek a focused and practical approach to mastering AWS SageMaker.