Last Project : AI Social Medya and Marketing Assistant

 Duration: Takes 4 to 8 weeks

 About Pricing and Additional Costs:

During our launch phase, which lasts until the 31st of December 2023, I offer a special hourly rate of €39. Starting from the 1st of January 2024, the standard hourly rate will be €99. Please note that additional costs may arise based on cloud services and AI library selections. The final cost will be contingent upon the specific features and services you opt for. Below is a detailed breakdown of the services provided:

Services Offered by the Virtual Assistant:

1. Communication & Content Creation:

• Speech-to-text and text-to-speech conversion in multiple languages.
• Article generation by analyzing millions of websites promptly.
• Visualization of ideas and rapid presentation creation.
• Efficient email management with optional auto-reply features.
• Creation of visual assistants for video or voice message dissemination.
• Video content creation with custom scenes (English language only).
• Swift content reading and summarization based on provided links.

2. Research & Data Analysis:

• Real-time data extraction from open sources.
• Comprehensive internet search capabilities.
• Video analysis, data extraction, scripting, training, or summarization.
• Instant video data retrieval.
• Shopping & Lifestyle:
• Coupon discovery for optimal deals globally.
• Shopping list creation with price comparisons.
• Personalized shopping recommendations based on user preferences.
• Swift reservation services with optional payment processing.

4. Education & Professional Development:

• Recommendations for top-tier courses by university professors.
• Job search assistance.
• Code writing in multiple programming languages.

5. Travel & Leisure:

• Travel deal sourcing and itinerary planning.
• Rapid global flight price comparisons.
• Movie recommendations based on user data.

6. Health & Nutrition:

• Custom recipe creation with nutritional analysis and diet recommendations.
• Personalized workout routines with video guides.

7. Business & Investments:

• Cryptocurrency investment advice.
• Business diagram creation with optional email delivery.
• A personalized Wikipedia for your needs.

8. Branding & Naming:

• Brand and single-word domain name suggestions.

9. Multimedia & Design:

• Personal DJ services.
• Photo creation, editing, and redesigning.
• Comprehensive PDF management, including summarization.

10. Integration & Automation:

• Mobile app integration and optimization.
•  Video creation in multiple languages with your voice tone (Version 1 with simple text / Version 2 with content creating skills).

 Overview:This initiative delved into the domain of energy optimization by employing cutting-edge reinforcement learning techniques. The central objective was to harness the capabilities of machine learning to optimize energy consumption and efficiency in a specified energy vessel.

Key Features:

1. Dynamic Learning: Using reinforcement learning, our model dynamically adapted to changing conditions, ensuring the energy vessel operates at peak efficiency under varying circumstances.
2. Data-driven Approach: Real-time data was constantly fed into the model, enabling it to make informed decisions that resulted in optimal energy utilization.
3. Environment Simulation: Before actual deployment, simulated environments were created to train the model. This ensured that the AI was well-prepared for diverse real-world scenarios.
4. Feedback Loop: A robust feedback mechanism was integrated, allowing the model to learn from its actions and decisions, thereby continuously refining its performance.

Achievements:

1. Enhanced Efficiency: Post-deployment, the energy vessel showcased a significant improvement in efficiency metrics, resulting in substantial energy savings.
2. Reduction in Wastage: Through smart decision-making, the model minimized energy wastage, contributing to a more sustainable and eco-friendly operation.
3. Operational Excellence: The AI-driven approach not only optimized energy usage but also streamlined various operational aspects of the vessel, leading to increased overall performance.

How It Was Achieved:

1. Data Collection: An extensive dataset, representing different operational conditions and scenarios, was amassed.
2. Model Training: Leveraging this data, the reinforcement learning model underwent rigorous training cycles, continually improving its decision-making capabilities.
3. Simulation Testing: Before deployment, the model was tested in simulated environments to ensure its effectiveness and reliability.
4. Integration and Monitoring: Once integrated into the energy vessel's system, continuous monitoring mechanisms were put in place, allowing for real-time adjustments and improvements.

Depot - Scandinavia Project

Role: Board Advisor & AI Strategy Leader
Duration: 2021 – 2022

Overview:
At Depot - Scandinavia, a significant transition was underway to incorporate advanced AI technology into its operational framework and migrate to the Azure cloud environment. As the Board Advisor & AI Strategy Leader, the task was to helm this transition, ensuring smooth integration while adhering to set timelines and budgetary constraints.

Key Responsibilities and Achievements:

1. Agile Leadership: Successfully directed an agile project team, ensuring fluid communication, timely decision-making, and efficient problem-solving. This leadership was instrumental in navigating the challenges of the transition seamlessly.
2. Adoption of Agile Frameworks: Implemented proven agile methodologies, including Scrum and Kanban, to oversee the project from inception to completion. This structured approach ensured all phases, from goal-setting to final implementation, were executed effectively.
3. Machine Learning Integration: Spearheaded the process of integrating machine learning models into a production environment. This involved rigorous testing, refining, and optimization to ensure the models delivered the desired output.
4. Data Exploration & Manipulation: Leveraged sophisticated techniques for exploratory data analysis and data manipulation. These crucial steps ensured the quality and relevance of the data, setting the foundation for accurate machine learning outcomes.
5. Model Selection: Adopted a strategic approach to candidate model selection, ensuring the chosen models were best suited to meet the project's objectives and delivered optimal performance.
6. Project Delivery: Testament to the meticulous planning and robust execution, the project was successfully delivered on time and within the stipulated budget.

Journey to Success:

1. Goal Definition: Began with a thorough understanding of Depot - Scandinavia's objectives and aligning them with the AI and Azure cloud migration strategy.
2. Agile Implementation: With a clear vision, agile methodologies were incorporated, ensuring every phase of the project was executed systematically.
3. Continuous Feedback: Adopted a feedback-driven approach, where iterative feedback was gathered and implemented, ensuring the project remained on the right track.
4. Final Deployment: After rigorous testing and refinements, the AI tech and Azure cloud environment were fully integrated, marking the project's successful completion.

Outcome:
The project marked a transformative phase for Depot - Scandinavia, empowering it with the prowess of AI and the efficiency of the Azure cloud environment. It stands as a testament to the power of structured project management, clear vision, and robust execution.

Outcomes achieved include text transformations, personalization, natural language generation (NLG), seamless integration with PIM and CMS platforms, scalability, efficiency, consistency, and compliance. With these advancements, the deep learning solution empowers retailers to:

• Obtain marketing texts from manufacturing brands.
• Adapt these texts to adhere to their distinct tone of voice guidelines.
• Generate retailer-specific marketing content through AI.

This approach ensures that retailers can deliver consistent, personalized, and compelling marketing messages. By doing so, they can craft content that not only aligns with their brand identity but also deeply resonates with their target audience.

Seeing AI 2016 Prototype - A Microsoft Research Project

Duration: Jan 2015 - Nov 2016
Associated with: Dr.Keskin 

Overview:
"Seeing AI" stands as a testament to Microsoft's commitment to harnessing technology for creating an inclusive world. It's an application engineered primarily to aid those with visual impairments or blindness, allowing them to "see" and interact with the world in ways previously deemed unattainable.

Key Features:

1. Object Recognition: The app is endowed with the capability to identify everyday objects, assisting users in comprehending their environment.
2. Text Reading: Whether it's a book, a signboard, or a menu, Seeing AI can audibly read text, bridging the gap between written content and the visually impaired.
3. Currency Recognition: To ensure transactions are transparent and accurate, the app recognizes various currencies, thereby aiding in financial exchanges.
4. Image Description: Beyond mere objects, the application can describe entire images, painting a vivid picture for its users.
5. Audio Cues: The app provides auditory feedback for a myriad of scenarios, such as detecting facial expressions, gauging the distance to objects, or discerning colors.

Achievements:

1. Empowerment: By offering essential information about surroundings, the application has gifted its users a newfound sense of independence and confidence in their daily lives.
2. Recognition: The app has been lauded for its precision and user-centric design, establishing itself as a trusted tool in the visually impaired community.
3. Evolution: Beginning as a research project, its overwhelming success catalyzed its evolution into a freely available smartphone app in 2017.

Journey to Success:

1. Research & Development: The project's foundation was laid with meticulous research into the needs of visually impaired individuals, combined with advancements in AI and computer vision.
2. Prototyping: The initial prototype created in 2016 underwent rigorous testing and refinements to enhance its efficacy.
3. Feedback Integration: User feedback played a pivotal role in shaping the app, ensuring that the tool was both robust and aligned with user needs.
4. Release & Expansion: Post its success as a prototype, the application was made accessible to a broader audience in 2017 as a free smartphone app. Its journey didn't stop there; with continuous updates, it remains a living project that adapts and grows.

Comcast, a global technology and media behemoth, aims to provide millions of its customers with tailor-made experiences. However, the journey to offer these personalized interactions was riddled with challenges. They had to grapple with massive data, brittle data pipelines, and a lack of collaboration tools. Their goal was to leverage the latest in technology, specifically Data Lake and MLflow, to handle vast data quantities and streamline the life cycle of numerous models. This was to culminate in an unmatched viewer experience enriched by voice recognition and machine learning capabilities.

In the fast-paced world of entertainment, there's no room for stagnation. Comcast recognized that to stand out, they needed a complete overhaul of their analytics approach. From ingesting data to deploying intricate machine learning models, they sought to introduce features that would leave their customers in awe.

• Unified Analytics Approach: Comcast shifted gears by implementing a unified analytics strategy. This transition allowed them to peer into the future, delivering AI-infused entertainment experiences that not only engaged viewers but also gave them an edge over competitors.
• Award-winning Viewer Interaction: The results of their endeavors were evident. They rolled out an innovative viewer experience that scooped up awards, a testament to its uniqueness. A key highlight was the integration of intelligent voice commands, further enhancing user engagement.
• Optimized Infrastructure: Efficiency was another victory. Comcast revamped data ingestion, enabling a tenfold reduction in compute costs. By downsizing from 640 machines to just 64, they bolstered performance while ensuring their teams could focus more on analytics and less on infrastructure management.
• Enhanced Collaboration & Productivity: By embracing Data Lake, Comcast fostered a collaborative environment for its data scientists, irrespective of geographical barriers. The tool provided a unified workspace that catered to various programming languages. It also ensured data was readily available at every stage of the pipeline, leading to accelerated model development.
• Swift Model Deployment: Time is of the essence, and Comcast proved they could keep up. The modernization efforts slashed model deployment durations from weeks to mere minutes. This agility was evident as operations teams rolled out models across diverse platforms seamlessly.

Through strategic technological adoption and a clear vision, Comcast transformed its viewer experience. By leveraging advanced analytics, optimizing infrastructure, and promoting collaboration, they set a new benchmark in the entertainment industry. The results were not only seen in enhanced viewer engagement but also in significant cost savings and productivity gains. Their journey underscores the transformative power of data analytics in shaping the future of entertainment.

 Regeneron : Harnessing Genomic Data for Groundbreaking Medical Innovations

Regeneron stands at the forefront of the medical industry with a core mission: to unlock the potential of genomic data, transforming it into innovative treatments for patients worldwide. The endeavor, while noble, presented numerous hurdles. With subpar data processing capabilities and scalability constraints, the data teams at Regeneron grappled with the mammoth task of analyzing petabytes of intricate genomic and clinical data.

The pharmaceutical world is fraught with uncertainties, with over 95% of experimental medicines in the developmental phase anticipated to hit roadblocks. In its quest for accuracy and efficiency, the Regeneron Genetics Center orchestrated one of the most expansive genetic databases ever seen, merging sequenced exomes with the electronic health records of over 400,000 individuals. But this came with its own set of challenges:

• The data, being spread out and decentralized, proved a tough nut to crack for analysis and model training on a colossal 10TB dataset.
• Scaling their existing architecture to handle the staggering 80 billion data points was not only challenging but also came with significant cost implications.
• The data teams found themselves mired in days-long processes, mainly focused on Extract, Transform, and Load (ETL) operations, hampering analytics.

• Unified Data Analytics Platform: Regeneron's game-changing move was to incorporate a Unified Data Analytics Platform powered by Amazon Web Services (AWS). This platform wasn't just a technical upgrade; it revolutionized operations, amplifying drug discovery via elevated data science efficiency.
• Rapid Drug Target Detection: This new infrastructure ushered in unprecedented efficiency. Tasks that previously took data scientists and biologists 30 minutes now wrapped up in 3 seconds, marking a phenomenal 600-fold enhancement.
• Augmented Productivity: Collaboration saw a new dawn with integrated DevOps, and data pipelines became more swift and agile. ETL processes that would have otherwise taken 3 weeks were now condensed into just 2 days, empowering the teams to embark on a wider spectrum of studies.

Embracing the power of AWS and state-of-the-art data analytics, Regeneron has transcended its previous limitations. Not only have they dramatically accelerated drug target identification processes, but they've also augmented team productivity, enabling more extensive research scopes. In harnessing the power of genomic data with such efficiency, Regeneron is paving the way for a new era of medical discoveries, bringing hope and advanced treatments to patients globally.

The insurance sector is in a state of flux, with vast data availability coupled with stiffening market competition, compelling providers to recalibrate their strategies. Nationwide, a major player in the industry, recognized an imperative need to optimize their pricing strategies. With a vast database encompassing hundreds of millions of insurance records at their disposal, the challenge lay in harnessing this data efficiently. Their existing batch analysis methods were sluggish and lacked precision, impeding their ability to anticipate claim frequency and severity accurately.

The focal point of Nationwide's undertaking was the accurate determination of insurance pricing. This required comprehensive analysis of insurance claims data. However, they grappled with unpredictable and sporadic claim records, leading to potential inaccuracies in pricing determinations.

• AI-Powered Data Analytics Platform: Nationwide stepped up its game by implementing its very own artificial intelligence data analytics platform. This holistic solution streamlined everything from initial data ingestion to the final deployment of sophisticated deep learning models.
• Efficient Data Processing: Nationwide's new infrastructure brought about a monumental shift in data processing efficiency. Tasks that previously consumed a staggering 34 hours were now executed in under 4 hours — a ninefold enhancement in performance.
• Rapid Feature Identification: One of the crucial steps in machine learning is feature engineering. With their revamped setup, Nationwide's data engineering team could pinpoint crucial features at a pace 15 times swifter than before, reducing the time from 5 hours to a mere 20 minutes.
• Swift Model Training: Time is of the essence when rolling out new models to adapt to market shifts. The new infrastructure slashed model training durations by half, empowering Nationwide to launch new models at a significantly faster rate.
• Enhanced Model Scoring: Accurate model scoring is vital to ensure the models' reliability. Nationwide's enhanced setup trimmed down model scoring times drastically from 3 hours to just about 5 minutes, marking a 60-fold improvement.

Through the strategic integration of AI and data analytics, Nationwide has effectively redefined its operational landscape. Not only have they attained greater precision in pricing determinations, but they've also unlocked fresh avenues for data-driven innovations. With a rapid data processing framework, accelerated feature engineering, and swift model deployments, Nationwide stands poised to lead the insurance domain, delivering optimized pricing and value to its customer base.

Condé Nast stands as a titan in the media realm, boasting a range of iconic magazine titles such as The New Yorker, Wired, and Vogue. With a massive reach, the company taps into the potential of data, impacting over a billion people across print, online platforms, video, and social media channels.

Given its expansive brand portfolio, which includes more than 20 distinct brands, Condé Nast grapples with an overwhelming influx of data every month. This data translates into 100 million-plus visits and over 800 million page views. The primary goal? Harness this data deluge to refine user engagement using machine learning, offering personalized content recommendations and laser-targeted ads to readers.

• Transition to the Cloud: Condé Nast streamlined its operations by embracing a fully managed cloud platform. This move not only simplified operations but also turbocharged the performance, laying a robust foundation for data science innovations.
• Redefining Customer Engagement: By refining the data pipeline, Condé Nast can now deliver enhanced content recommendations at a rapid pace. This means readers receive content tailored to their preferences, resulting in enriched user experiences and bolstered engagement levels.
• Scalability at its Best: In the world of data, growth is inevitable. Recognizing this, Condé Nast ensured that their infrastructure was built for scale. No matter how vast their datasets grow, their infrastructure remains adept at processing and extracting valuable insights without missing a beat.
• Accelerating Model Deployment: Leveraging MLflow, a platform designed to manage the machine learning lifecycle, Condé Nast's data science teams can now bring innovations to the table faster. This agility has led to the deployment of over 1,200 machine learning models in production, each contributing to refining the user experience further.

Condé Nast's visionary approach to integrating data science into its core operations has yielded remarkable results. Through tailored content recommendations and targeted ads, the company has managed to resonate even more with its vast reader base. The robust cloud infrastructure ensures they remain future-ready, no matter the data scale. And with a rapid model deployment mechanism in place, they are poised to continuously innovate, shaping the future of media consumption.

SHOWTIME, a leading premium television network and streaming service, boasts an impressive portfolio of award-winning series, including "Shameless," "Homeland," "Billions," and many more. In an era dominated by digital streaming and intense competition, staying ahead necessitates harnessing data effectively.

The core objective for SHOWTIME's Data Strategy team was to democratize data and analytics throughout the organization. By collating vast subscriber data – encompassing shows watched, time of day, devices used, subscription history, and more – the aim was to leverage machine learning to predict viewer behavior, thereby refining both programming schedules and content offerings.

• Fostering a Data-Driven Culture: The key to SHOWTIME's success was embracing a holistic data-centric approach. By ensuring all departments had access to actionable insights, the organization transitioned into a more informed decision-making model.
• Acceleration of Data Pipelines: One of the standout achievements was the transformation of data processing speeds. Pipelines, which previously took upwards of 24 hours, were streamlined to conclude within just 4 hours. This 6x enhancement in data processing enabled teams to make faster, more informed decisions, staying responsive in a dynamic industry.
• Simplifying Infrastructure: The transition to a cloud-based, fully managed platform drastically reduced the complexity associated with infrastructure management. With automated cluster management, the data science team could pivot their focus from hardware intricacies to advancing their machine learning pursuits.
• Revolutionizing the Subscriber Experience: By fostering an environment of collaboration among data scientists, SHOWTIME ensured that new models and features were brought to the market swiftly. This reduced lead time, coupled with the power of machine learning, enabled the creation of highly personalized viewer experiences. The results? Enhanced viewer engagement and satisfaction.

SHOWTIME's commitment to harnessing data has clearly paid dividends. By prioritizing a data-driven culture, simplifying infrastructure, and constantly innovating the subscriber experience, they've managed to stay ahead in a fiercely competitive landscape. This case study epitomizes the transformative power of data and how, when leveraged effectively, it can redefine industry standards and viewer expectations.

Shell, a global titan in the oil and gas sector, is renowned for its trailblazing advancements in exploration and production technologies. With its distinction as one of the world's premier oil and natural gas producers, marketers, and petrochemical manufacturers, Shell operates at a colossal scale, necessitating optimal supply chain and inventory management.

Shell's global operations involve stocking more than 3,000 different spare parts across various facilities. The challenge is twofold: ensuring timely availability of parts to mitigate potential outages and optimizing stock levels to avoid costly overstocking.

• Cloud-native Unified Analytics Platform: Shell transitioned to a cloud-based analytics platform. This centralized system augments inventory and supply chain management, streamlining processes and enabling real-time insights.
• Predictive Modeling at Scale: A salient feature of this transformation was the scalable predictive modeling. This model was developed and deployed to cater to over 3,000 types of materials across 50+ locations. The result? Enhanced accuracy in predicting inventory needs and reducing wastages.
• Historical Analysis via Markov Chain Monte Carlo: A methodical approach was adopted where each material model underwent 10,000 Markov Chain Monte Carlo simulations. This exhaustive approach ensures that historical data and previous challenges are factored into the predictions, refining the model's accuracy.
• Massive Performance Uplift: The analytical prowess of the new system became evident when the data science team, by leveraging a 50 node Apache Spark™ cluster on Databricks, slashed the inventory analysis and prediction time from a staggering 48 hours to a mere 45 minutes. This 32x performance enhancement signifies quicker decision-making and improved responsiveness.
• Significant Cost Reduction: The new system's effectiveness isn't just in its predictive accuracy but also in its fiscal impact. Shell realized cost savings that equate to millions of dollars annually. This is a testament to how optimized inventory management can lead to substantial financial benefits.

Shell's venture into advanced analytics reaped impressive dividends. Not only did it enhance inventory precision, but it also facilitated massive cost savings. This case underscores the potential of data-driven decision-making in large-scale operations, demonstrating that with the right analytical tools and approaches, enterprises can achieve remarkable operational and fiscal efficiencies.

Riot Games, established in 2006 in Los Angeles, has established itself as the epitome of a player-focused gaming company. It is most recognized for its globally acclaimed game, "League of Legends," attracting over 100 million enthusiastic gamers on a monthly basis.

Riot Games aimed to intensify the gaming experience, focusing on refining network performance monitoring and addressing in-game abusive language.

• Scalable and Swift Analytics: Riot Games transitioned to an analytical approach that is both scalable and quick, facilitating real-time and robust gaming experience improvements.
• Enhancement of In-Game Purchase Experience: Riot Games successfully integrated a recommendation engine built to operate on a colossal 500B data points. The result? Gamers are presented with individualized offers, making content discovery and purchase more streamlined and intuitive.
• Machine Learning for Reduced Game Lag: Delays or 'lags' during gameplay can significantly mar the user experience. By implementing a machine learning model, Riot Games can now detect potential network glitches in real-time. By proactively identifying and rectifying these issues, the company ensures that players enjoy an uninterrupted gaming session.
• Accelerated Analytics: Data processing, a pivotal aspect of game enhancement, witnessed a dramatic 50% performance boost when compared to the EMR (Elastic MapReduce) approach. This augmentation in data preparation and exploration has empowered Riot Games to derive insights faster, translating to quicker gameplay optimizations.

Riot Games' investment in cutting-edge analytical tools and techniques has proven fruitful. Gamers are now treated to a more immersive experience, characterized by personalized content recommendations, reduced in-game disruptions, and an overall enriched gaming environment. This case exemplifies how advanced analytics, when applied meticulously, can elevate user engagement and satisfaction in the gaming industry.

Quby, a forefront technology enterprise, is the driving force behind the innovative Toon energy management device. Designed to give users optimal control over their energy usage, Toon not only enhances energy management but also boosts the security and comfort of homes. With their smart devices installed in numerous households across Europe, Quby boasts Europe's most expansive dataset, drawn from a myriad of IoT sensors deployed in homes. Harnessing this invaluable data, Quby's primary mission revolves around promoting energy efficiency and comfort by delivering tailored energy consumption advice.

The principal objective was to design and provide individualized energy use recommendations. This was to be achieved by optimally utilizing machine learning algorithms and the vast swathes of IoT data, predominantly through the Waste Checker application. The app was designed to offer bespoke suggestions that would ultimately curb energy consumption within homes.

• Unified Data Analytics Platform: Quby integrated a Unified Data Analytics Platform, drastically transforming their data analytics approach. This platform fostered an environment that promotes collaboration between data science teams and engineers. As a result, the pace of data analysis accelerated, allowing more rapid innovation and swift deployment of machine learning-enhanced services tailored to customer needs.
• Optimized Infrastructure with Databricks: The introduction of cost-effective solutions like auto-scaling clusters and Spot instances by Databricks has enabled Quby to efficiently manage their extensive infrastructure. Not only did this lead to a significant reduction in operational expenses, but it also ensured the seamless processing of vast amounts of data.
• Streamlined Development Process: Previously, the transition from a prototype to a fully-fledged product consumed more than a year. With the revamped architecture and strategies, the entire process has been condensed to fewer than eight weeks. This means new features powered by machine learning can be rolled out at an accelerated pace, enhancing user experience.
• Tangible Results with the Waste Checker App: By leveraging the Waste Checker app, Quby has identified potential savings of over 67 million kilowatt hours of energy. This was achieved by giving users precise recommendations on energy consumption based on their unique usage patterns.

Through a combination of advanced data analytics, machine learning, and innovative solutions, Quby has not only achieved its goal of delivering personalized energy recommendations but also contributed significantly to energy conservation. The project serves as a benchmark in the energy management sector, showcasing how technology can be harnessed for sustainable and efficient living.